A Common Mechanism Underlying Food Choice and Social Decisions

PubMed Central

Krajbich, Ian; Hare, Todd; Bartling, Björn; Morishima, Yosuke; Fehr, Ernst

2015-01-01

People make numerous decisions every day including perceptual decisions such as walking through a crowd, decisions over primary rewards such as what to eat, and social decisions that require balancing own and others’ benefits. The unifying principles behind choices in various domains are, however, still not well understood. Mathematical models that describe choice behavior in specific contexts have provided important insights into the computations that may underlie decision making in the brain. However, a critical and largely unanswered question is whether these models generalize from one choice context to another. Here we show that a model adapted from the perceptual decision-making domain and estimated on choices over food rewards accurately predicts choices and reaction times in four independent sets of subjects making social decisions. The robustness of the model across domains provides behavioral evidence for a common decision-making process in perceptual, primary reward, and social decision making. PMID:26460812

How to use multi-criteria decision analysis methods for reimbursement decision-making in healthcare: a step-by-step guide.

PubMed

Diaby, Vakaramoko; Goeree, Ron

2014-02-01

In recent years, the quest for more comprehensiveness, structure and transparency in reimbursement decision-making in healthcare has prompted the research into alternative decision-making frameworks. In this environment, multi-criteria decision analysis (MCDA) is arising as a valuable tool to support healthcare decision-making. In this paper, we present the main MCDA decision support methods (elementary methods, value-based measurement models, goal programming models and outranking models) using a case study approach. For each family of methods, an example of how an MCDA model would operate in a real decision-making context is presented from a critical perspective, highlighting the parameters setting, the selection of the appropriate evaluation model as well as the role of sensitivity and robustness analyses. This study aims to provide a step-by-step guide on how to use MCDA methods for reimbursement decision-making in healthcare.

Beyond evidence-based nursing: tools for practice.

PubMed

Jutel, Annemarie

2008-05-01

This commentary shares my views of evidence-based nursing as a framework for practice, pointing out its limitations and identifying a wider base of appraisal tools required for making good clinical decisions. As the principles of evidence-based nursing take an increasingly greater hold on nursing education, policy and management, it is important to consider the range of other decision-making tools which are subordinated by this approach. This article summarizes nursing's simultaneous reliance on and critique of evidence-based practice (EBP) in a context of inadequate critical reasoning. It then provides an exemplar of the limitations of evidence-based practice and offers an alternative view of important precepts of decision-making. I identify means by which nurses can develop skills to engage in informed and robust critique of practices and their underpinning rationale. Nurses need to be able to locate and assess useful and reliable information for decision-making. This skill is based on a range of tools which include, but also go beyond EBP including: information literacy, humanities, social sciences, public health, statistics, marketing, ethics and much more. This essay prompts nursing managers to reflect upon whether a flurried enthusiasm to adopt EBP neglects other important decision-making skills which provide an even stronger foundation for robust nursing decisions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ernst, Kathleen M; Van Riemsdijk, Dr. Micheline

This article studies the participation of stakeholders in climate change decision-making in Alaska s National Parks. We place stakeholder participation within literatures on environmental and climate change decision-making. We conducted participant observation and interviews in two planning workshops to investigate the decision-making process, and our findings are three-fold. First, the inclusion of diverse stakeholders expanded climate change decision-making beyond National Park Service (NPS) institutional constraints. Second, workshops of the Climate Change Scenario Planning Project (CCSPP) enhanced institutional understandings of participants attitudes towards climate change and climate change decision-making. Third, the geographical context of climate change influences the decisionmaking process. Asmore » the first regional approach to climate change decision-making within the NPS, the CCSPP serves as a model for future climate change planning in public land agencies. This study shows how the participation of stakeholders can contribute to robust decisions, may move climate change decision-making beyond institutional barriers, and can provide information about attitudes towards climate change decision-making.« less

Putting cognitive psychology to work: Improving decision-making in the medical encounter.

PubMed

Schwab, Abraham P

2008-12-01

Empirical research in social psychology has provided robust support for the accuracy of the heuristics and biases approach to human judgment. This research, however, has not been systematically investigated regarding its potential applications for specific health care decision-makers. This paper makes the case for investigating the heuristics and biases approach in the patient-physician relationship and recommends strategic empirical research. It is argued that research will be valuable for particular decisions in the clinic and for examining and altering the background conditions of patient and physician decision-making.

Many-objective robust decision making for water allocation under climate change.

PubMed

Yan, Dan; Ludwig, Fulco; Huang, He Qing; Werners, Saskia E

2017-12-31

Water allocation is facing profound challenges due to climate change uncertainties. To identify adaptive water allocation strategies that are robust to climate change uncertainties, a model framework combining many-objective robust decision making and biophysical modeling is developed for large rivers. The framework was applied to the Pearl River basin (PRB), China where sufficient flow to the delta is required to reduce saltwater intrusion in the dry season. Before identifying and assessing robust water allocation plans for the future, the performance of ten state-of-the-art MOEAs (multi-objective evolutionary algorithms) is evaluated for the water allocation problem in the PRB. The Borg multi-objective evolutionary algorithm (Borg MOEA), which is a self-adaptive optimization algorithm, has the best performance during the historical periods. Therefore it is selected to generate new water allocation plans for the future (2079-2099). This study shows that robust decision making using carefully selected MOEAs can help limit saltwater intrusion in the Pearl River Delta. However, the framework could perform poorly due to larger than expected climate change impacts on water availability. Results also show that subjective design choices from the researchers and/or water managers could potentially affect the ability of the model framework, and cause the most robust water allocation plans to fail under future climate change. Developing robust allocation plans in a river basin suffering from increasing water shortage requires the researchers and water managers to well characterize future climate change of the study regions and vulnerabilities of their tools. Copyright © 2017 Elsevier B.V. All rights reserved.

A Robust Decision-Making Technique for Water Management under Decadal Scale Climate Variability

NASA Astrophysics Data System (ADS)

Callihan, L.; Zagona, E. A.; Rajagopalan, B.

2013-12-01

Robust decision making, a flexible and dynamic approach to managing water resources in light of deep uncertainties associated with climate variability at inter-annual to decadal time scales, is an analytical framework that detects when a system is in or approaching a vulnerable state. It provides decision makers the opportunity to implement strategies that both address the vulnerabilities and perform well over a wide range of plausible future scenarios. A strategy that performs acceptably over a wide range of possible future states is not likely to be optimal with respect to the actual future state. The degree of success--the ability to avoid vulnerable states and operate efficiently--thus depends on the skill in projecting future states and the ability to select the most efficient strategies to address vulnerabilities. This research develops a robust decision making framework that incorporates new methods of decadal scale projections with selection of efficient strategies. Previous approaches to water resources planning under inter-annual climate variability combining skillful seasonal flow forecasts with climatology for subsequent years are not skillful for medium term (i.e. decadal scale) projections as decision makers are not able to plan adequately to avoid vulnerabilities. We address this need by integrating skillful decadal scale streamflow projections into the robust decision making framework and making the probability distribution of this projection available to the decision making logic. The range of possible future hydrologic scenarios can be defined using a variety of nonparametric methods. Once defined, an ensemble projection of decadal flow scenarios are generated from a wavelet-based spectral K-nearest-neighbor resampling approach using historical and paleo-reconstructed data. This method has been shown to generate skillful medium term projections with a rich variety of natural variability. The current state of the system in combination with the probability distribution of the projected flow ensembles enables the selection of appropriate decision options. This process is repeated for each year of the planning horizon--resulting in system outcomes that can be evaluated on their performance and resiliency. The research utilizes the RiverSMART suite of software modeling and analysis tools developed under the Bureau of Reclamation's WaterSMART initiative and built around the RiverWare modeling environment. A case study is developed for the Gunnison and Upper Colorado River Basins. The ability to mitigate vulnerability using the framework is gauged by system performance indicators that measure the ability of the system to meet various water demands (i.e. agriculture, environmental flows, hydropower etc.). Options and strategies for addressing vulnerabilities include measures such as conservation, reallocation and adjustments to operational policy. In addition to being able to mitigate vulnerabilities, options and strategies are evaluated based on benefits, costs and reliability. Flow ensembles are also simulated to incorporate mean and variance from climate change projections for the planning horizon and the above robust decision-making framework is applied to evaluate its performance under changing climate.

Decision on risk-averse dual-channel supply chain under demand disruption

NASA Astrophysics Data System (ADS)

Yan, Bo; Jin, Zijie; Liu, Yanping; Yang, Jianbo

2018-02-01

We studied dual-channel supply chains using centralized and decentralized decision-making models. We also conducted a comparative analysis of the decisions before and after demand disruption. The study shows that the amount of change in decision-making is a linear function of the amount of demand disruption, and it is independent of the risk-averse coefficient. The optimal sales volume decision of the disturbing supply chain is related to market share and demand disruption in the decentralized decision-making model. The optimal decision is only influenced by demand disruption in the centralized decision-making model. The stability of the sales volume of the two models is related to market share and demand disruption. The optimal system production of the two models shows robustness, but their stable internals are different.

Achieving Robustness to Uncertainty for Financial Decision-making

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barnum, George M.; Van Buren, Kendra L.; Hemez, Francois M.

2014-01-10

This report investigates the concept of robustness analysis to support financial decision-making. Financial models, that forecast future stock returns or market conditions, depend on assumptions that might be unwarranted and variables that might exhibit large fluctuations from their last-known values. The analysis of robustness explores these sources of uncertainty, and recommends model settings such that the forecasts used for decision-making are as insensitive as possible to the uncertainty. A proof-of-concept is presented with the Capital Asset Pricing Model. The robustness of model predictions is assessed using info-gap decision theory. Info-gaps are models of uncertainty that express the “distance,” or gapmore » of information, between what is known and what needs to be known in order to support the decision. The analysis yields a description of worst-case stock returns as a function of increasing gaps in our knowledge. The analyst can then decide on the best course of action by trading-off worst-case performance with “risk”, which is how much uncertainty they think needs to be accommodated in the future. The report also discusses the Graphical User Interface, developed using the MATLAB® programming environment, such that the user can control the analysis through an easy-to-navigate interface. Three directions of future work are identified to enhance the present software. First, the code should be re-written using the Python scientific programming software. This change will achieve greater cross-platform compatibility, better portability, allow for a more professional appearance, and render it independent from a commercial license, which MATLAB® requires. Second, a capability should be developed to allow users to quickly implement and analyze their own models. This will facilitate application of the software to the evaluation of proprietary financial models. The third enhancement proposed is to add the ability to evaluate multiple models simultaneously. When two models reflect past data with similar accuracy, the more robust of the two is preferable for decision-making because its predictions are, by definition, less sensitive to the uncertainty.« less

Decision-Making Under Risk: Integrating Perspectives From Biology, Economics, and Psychology.

PubMed

Mishra, Sandeep

2014-08-01

Decision-making under risk has been variably characterized and examined in many different disciplines. However, interdisciplinary integration has not been forthcoming. Classic theories of decision-making have not been amply revised in light of greater empirical data on actual patterns of decision-making behavior. Furthermore, the meta-theoretical framework of evolution by natural selection has been largely ignored in theories of decision-making under risk in the human behavioral sciences. In this review, I critically examine four of the most influential theories of decision-making from economics, psychology, and biology: expected utility theory, prospect theory, risk-sensitivity theory, and heuristic approaches. I focus especially on risk-sensitivity theory, which offers a framework for understanding decision-making under risk that explicitly involves evolutionary considerations. I also review robust empirical evidence for individual differences and environmental/situational factors that predict actual risky decision-making that any general theory must account for. Finally, I offer steps toward integrating various theoretical perspectives and empirical findings on risky decision-making. © 2014 by the Society for Personality and Social Psychology, Inc.

Transcranial Alternating Current Stimulation Modulates Risky Decision Making in a Frequency-Controlled Experiment

PubMed Central

Awasthi, Bhuvanesh

2017-01-01

Abstract In this study, we investigated the effect of transcranial alternating current stimulation (tACS) on voluntary risky decision making and executive control in humans. Stimulation was delivered online at 5 Hz (θ), 10 Hz (α), 20 Hz (β), and 40 Hz (γ) on the left and right frontal area while participants performed a modified risky decision-making task. This task allowed participants to voluntarily select between risky and certain decisions associated with potential gains or losses, while simultaneously measuring the cognitive control component (voluntary switching) of decision making. The purpose of this experimental design was to test whether voluntary risky decision making and executive control can be modulated with tACS in a frequency-specific manner. Our results revealed a robust effect of a 20-Hz stimulation over the left prefrontal area that significantly increased voluntary risky decision making, which may suggest a possible link between risky decision making and reward processing, underlined by β-oscillatory activity. PMID:29379865

Transcranial Alternating Current Stimulation Modulates Risky Decision Making in a Frequency-Controlled Experiment.

PubMed

Yaple, Zachary; Martinez-Saito, Mario; Feurra, Matteo; Shestakova, Anna; Klucharev, Vasily

2017-01-01

In this study, we investigated the effect of transcranial alternating current stimulation (tACS) on voluntary risky decision making and executive control in humans. Stimulation was delivered online at 5 Hz (θ), 10 Hz (α), 20 Hz (β), and 40 Hz (γ) on the left and right frontal area while participants performed a modified risky decision-making task. This task allowed participants to voluntarily select between risky and certain decisions associated with potential gains or losses, while simultaneously measuring the cognitive control component (voluntary switching) of decision making. The purpose of this experimental design was to test whether voluntary risky decision making and executive control can be modulated with tACS in a frequency-specific manner. Our results revealed a robust effect of a 20-Hz stimulation over the left prefrontal area that significantly increased voluntary risky decision making, which may suggest a possible link between risky decision making and reward processing, underlined by β-oscillatory activity.

Adding flexibility to the search for robust portfolios in non-linear water resource planning

NASA Astrophysics Data System (ADS)

Tomlinson, James; Harou, Julien

2017-04-01

To date robust optimisation of water supply systems has sought to find portfolios or strategies that are robust to a range of uncertainties or scenarios. The search for a single portfolio that is robust in all scenarios is necessarily suboptimal compared to portfolios optimised for a single scenario deterministic future. By contrast establishing a separate portfolio for each future scenario is unhelpful to the planner who must make a single decision today under deep uncertainty. In this work we show that a middle ground is possible by allowing a small number of different portfolios to be found that are each robust to a different subset of the global scenarios. We use evolutionary algorithms and a simple water resource system model to demonstrate this approach. The primary contribution is to demonstrate that flexibility can be added to the search for portfolios, in complex non-linear systems, at the expense of complete robustness across all future scenarios. In this context we define flexibility as the ability to design a portfolio in which some decisions are delayed, but those decisions that are not delayed are themselves shown to be robust to the future. We recognise that some decisions in our portfolio are more important than others. An adaptive portfolio is found by allowing no flexibility for these near-term "important" decisions, but maintaining flexibility in the remaining longer term decisions. In this sense we create an effective 2-stage decision process for a non-linear water resource supply system. We show how this reduces a measure of regret versus the inflexible robust solution for the same system.

Measuring and Modeling Behavioral Decision Dynamics in Collective Evacuation

PubMed Central

Carlson, Jean M.; Alderson, David L.; Stromberg, Sean P.; Bassett, Danielle S.; Craparo, Emily M.; Guiterrez-Villarreal, Francisco; Otani, Thomas

2014-01-01

Identifying and quantifying factors influencing human decision making remains an outstanding challenge, impacting the performance and predictability of social and technological systems. In many cases, system failures are traced to human factors including congestion, overload, miscommunication, and delays. Here we report results of a behavioral network science experiment, targeting decision making in a natural disaster. In a controlled laboratory setting, our results quantify several key factors influencing individual evacuation decision making in a controlled laboratory setting. The experiment includes tensions between broadcast and peer-to-peer information, and contrasts the effects of temporal urgency associated with the imminence of the disaster and the effects of limited shelter capacity for evacuees. Based on empirical measurements of the cumulative rate of evacuations as a function of the instantaneous disaster likelihood, we develop a quantitative model for decision making that captures remarkably well the main features of observed collective behavior across many different scenarios. Moreover, this model captures the sensitivity of individual- and population-level decision behaviors to external pressures, and systematic deviations from the model provide meaningful estimates of variability in the collective response. Identification of robust methods for quantifying human decisions in the face of risk has implications for policy in disasters and other threat scenarios, specifically the development and testing of robust strategies for training and control of evacuations that account for human behavior and network topologies. PMID:24520331

Neural Correlates of Decision Making on a Gambling Task

ERIC Educational Resources Information Center

Carlson, Stephanie M.; Zayas, Vivian; Guthormsen, Amy

2009-01-01

Individual differences in affective decision making were examined by recording event-related potentials (ERPs) while 74 typically developing 8-year-olds (38 boys, 36 girls) completed a 4-choice gambling task (Hungry Donkey Task; E. A. Crone & M. W. van der Molen, 2004). ERP results indicated: (a) a robust P300 component in response to feedback…

Integrative evaluation for sustainable decisions of urban wastewater system management under uncertainty

NASA Astrophysics Data System (ADS)

Hadjimichael, A.; Corominas, L.; Comas, J.

2017-12-01

With sustainable development as their overarching goal, urban wastewater system (UWS) managers need to take into account multiple social, economic, technical and environmental facets related to their decisions. In this complex decision-making environment, uncertainty can be formidable. It is present both in the ways the system is interpreted stochastically, but also in its natural ever-shifting behavior. This inherent uncertainty suggests that wiser decisions would be made under an adaptive and iterative decision-making regime. No decision-support framework has been presented in the literature to effectively addresses all these needs. The objective of this work is to describe such a conceptual framework to evaluate and compare alternative solutions for various UWS challenges within an adaptive management structure. Socio-economic aspects such as externalities are taken into account, along with other traditional criteria as necessary. Robustness, reliability and resilience analyses test the performance of the system against present and future variability. A valuation uncertainty analysis incorporates uncertain valuation assumptions in the decision-making process. The framework is demonstrated with an application to a case study presenting a typical problem often faced by managers: poor river water quality, increasing population, and more stringent water quality legislation. The application of the framework made use of: i) a cost-benefit analysis including monetized environmental benefits and damages; ii) a robustness analysis of system performance against future conditions; iii) reliability and resilience analyses of the system given contextual variability; and iv) a valuation uncertainty analysis of model parameters. The results suggest that the installation of bigger volumes would give rise to increased benefits despite larger capital costs, as well as increased robustness and resilience. Population numbers appear to affect the estimated benefits most, followed by electricity prices and climate change projections. The presented framework is expected to be a valuable tool for the next generation of UWS decision-making and the application demonstrates a novel and valuable integration of metrics and methods for UWS analysis.

An index-based robust decision making framework for watershed management in a changing climate.

PubMed

Kim, Yeonjoo; Chung, Eun-Sung

2014-03-01

This study developed an index-based robust decision making framework for watershed management dealing with water quantity and quality issues in a changing climate. It consists of two parts of management alternative development and analysis. The first part for alternative development consists of six steps: 1) to understand the watershed components and process using HSPF model, 2) to identify the spatial vulnerability ranking using two indices: potential streamflow depletion (PSD) and potential water quality deterioration (PWQD), 3) to quantify the residents' preferences on water management demands and calculate the watershed evaluation index which is the weighted combinations of PSD and PWQD, 4) to set the quantitative targets for water quantity and quality, 5) to develop a list of feasible alternatives and 6) to eliminate the unacceptable alternatives. The second part for alternative analysis has three steps: 7) to analyze all selected alternatives with a hydrologic simulation model considering various climate change scenarios, 8) to quantify the alternative evaluation index including social and hydrologic criteria with utilizing multi-criteria decision analysis methods and 9) to prioritize all options based on a minimax regret strategy for robust decision. This framework considers the uncertainty inherent in climate models and climate change scenarios with utilizing the minimax regret strategy, a decision making strategy under deep uncertainty and thus this procedure derives the robust prioritization based on the multiple utilities of alternatives from various scenarios. In this study, the proposed procedure was applied to the Korean urban watershed, which has suffered from streamflow depletion and water quality deterioration. Our application shows that the framework provides a useful watershed management tool for incorporating quantitative and qualitative information into the evaluation of various policies with regard to water resource planning and management. Copyright © 2013 Elsevier B.V. All rights reserved.

Failure detection system design methodology. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Chow, E. Y.

1980-01-01

The design of a failure detection and identification system consists of designing a robust residual generation process and a high performance decision making process. The design of these two processes are examined separately. Residual generation is based on analytical redundancy. Redundancy relations that are insensitive to modelling errors and noise effects are important for designing robust residual generation processes. The characterization of the concept of analytical redundancy in terms of a generalized parity space provides a framework in which a systematic approach to the determination of robust redundancy relations are developed. The Bayesian approach is adopted for the design of high performance decision processes. The FDI decision problem is formulated as a Bayes sequential decision problem. Since the optimal decision rule is incomputable, a methodology for designing suboptimal rules is proposed. A numerical algorithm is developed to facilitate the design and performance evaluation of suboptimal rules.

Multi-criteria multi-stakeholder decision analysis using a fuzzy-stochastic approach for hydrosystem management

NASA Astrophysics Data System (ADS)

Subagadis, Y. H.; Schütze, N.; Grundmann, J.

2014-09-01

The conventional methods used to solve multi-criteria multi-stakeholder problems are less strongly formulated, as they normally incorporate only homogeneous information at a time and suggest aggregating objectives of different decision-makers avoiding water-society interactions. In this contribution, Multi-Criteria Group Decision Analysis (MCGDA) using a fuzzy-stochastic approach has been proposed to rank a set of alternatives in water management decisions incorporating heterogeneous information under uncertainty. The decision making framework takes hydrologically, environmentally, and socio-economically motivated conflicting objectives into consideration. The criteria related to the performance of the physical system are optimized using multi-criteria simulation-based optimization, and fuzzy linguistic quantifiers have been used to evaluate subjective criteria and to assess stakeholders' degree of optimism. The proposed methodology is applied to find effective and robust intervention strategies for the management of a coastal hydrosystem affected by saltwater intrusion due to excessive groundwater extraction for irrigated agriculture and municipal use. Preliminary results show that the MCGDA based on a fuzzy-stochastic approach gives useful support for robust decision-making and is sensitive to the decision makers' degree of optimism.

Polya's bees: A model of decentralized decision-making.

PubMed

Golman, Russell; Hagmann, David; Miller, John H

2015-09-01

How do social systems make decisions with no single individual in control? We observe that a variety of natural systems, including colonies of ants and bees and perhaps even neurons in the human brain, make decentralized decisions using common processes involving information search with positive feedback and consensus choice through quorum sensing. We model this process with an urn scheme that runs until hitting a threshold, and we characterize an inherent tradeoff between the speed and the accuracy of a decision. The proposed common mechanism provides a robust and effective means by which a decentralized system can navigate the speed-accuracy tradeoff and make reasonably good, quick decisions in a variety of environments. Additionally, consensus choice exhibits systemic risk aversion even while individuals are idiosyncratically risk-neutral. This too is adaptive. The model illustrates how natural systems make decentralized decisions, illuminating a mechanism that engineers of social and artificial systems could imitate.

Polya’s bees: A model of decentralized decision-making

PubMed Central

Golman, Russell; Hagmann, David; Miller, John H.

2015-01-01

How do social systems make decisions with no single individual in control? We observe that a variety of natural systems, including colonies of ants and bees and perhaps even neurons in the human brain, make decentralized decisions using common processes involving information search with positive feedback and consensus choice through quorum sensing. We model this process with an urn scheme that runs until hitting a threshold, and we characterize an inherent tradeoff between the speed and the accuracy of a decision. The proposed common mechanism provides a robust and effective means by which a decentralized system can navigate the speed-accuracy tradeoff and make reasonably good, quick decisions in a variety of environments. Additionally, consensus choice exhibits systemic risk aversion even while individuals are idiosyncratically risk-neutral. This too is adaptive. The model illustrates how natural systems make decentralized decisions, illuminating a mechanism that engineers of social and artificial systems could imitate. PMID:26601255

Healthcare decisions: a review of children's involvement.

PubMed

Baston, Jenny

2008-04-01

Children's rights, their ability to consent to treatment and their involvement in healthcare decisions have received considerable attention in recent years. There is some evidence to suggest that when children are involved in the decision-making process, they retain a sense of control over their situation. However there are still unresolved issues related to a child's right to decide and nurses may be confused about the extent to which children can and should be involved in decision-making. A code of practice for involving children in decisions was first suggested in 2001 and there is still a need for a consistent, structured and robust method of ensuring that children are included in the decision-making process at all stages of their health care.

A framework for sensitivity analysis of decision trees.

PubMed

Kamiński, Bogumił; Jakubczyk, Michał; Szufel, Przemysław

2018-01-01

In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.

Robust Decision Making to Support Water Quality Climate Adaptation: a Case Study in the Chesapeake Bay Watershed

NASA Astrophysics Data System (ADS)

Fischbach, J. R.; Lempert, R. J.; Molina-Perez, E.

2017-12-01

The U.S. Environmental Protection Agency (USEPA), together with state and local partners, develops watershed implementation plans designed to meet water quality standards. Climate uncertainty, along with uncertainty about future land use changes or the performance of water quality best management practices (BMPs), may make it difficult for these implementation plans to meet water quality goals. In this effort, we explored how decision making under deep uncertainty (DMDU) methods such as Robust Decision Making (RDM) could help USEPA and its partners develop implementation plans that are more robust to future uncertainty. The study focuses on one part of the Chesapeake Bay watershed, the Patuxent River, which is 2,479 sq km in area, highly urbanized, and has a rapidly growing population. We simulated the contribution of stormwater contaminants from the Patuxent to the overall Total Maximum Daily Load (TMDL) for the Chesapeake Bay under multiple scenarios reflecting climate and other uncertainties. Contaminants considered included nitrogen, phosphorus, and sediment loads. The assessment included a large set of scenario simulations using the USEPA Chesapeake Bay Program's Phase V watershed model. Uncertainties represented in the analysis included 18 downscaled climate projections (based on 6 general circulation models and 3 emissions pathways), 12 land use scenarios with different population projections and development patterns, and alternative assumptions about BMP performance standards and efficiencies associated with different suites of stormwater BMPs. Finally, we developed cost estimates for each of the performance standards and compared cost to TMDL performance as a key tradeoff for future water quality management decisions. In this talk, we describe how this research can help inform climate-related decision support at USEPA's Chesapeake Bay Program, and more generally how RDM and other DMDU methods can support improved water quality management under climate uncertainty.

Integrated Cognitive Architectures For Robust Decision Making

DTIC Science & Technology

2010-09-20

groups differed significantly from the other three [W(5) > 5, p > 0.13, uncorrected]. Performance by Condition It is useful to look at the average...the research that pursues integrated theories of human cognition, two approaches have become particularly influencial : ACT-R and Leabra. ACT-R...a wide range of tasks involving attention, learning, memory, problem solving, decision making, and language processing. Under the pressure of

Reasoned Decision Making Without Math? Adaptability and Robustness in Response to Surprise.

PubMed

Smithson, Michael; Ben-Haim, Yakov

2015-10-01

Many real-world planning and decision problems are far too uncertain, too variable, and too complicated to support realistic mathematical models. Nonetheless, we explain the usefulness, in these situations, of qualitative insights from mathematical decision theory. We demonstrate the integration of info-gap robustness in decision problems in which surprise and ignorance are predominant and where personal and collective psychological factors are critical. We present practical guidelines for employing adaptable-choice strategies as a proxy for robustness against uncertainty. These guidelines include being prepared for more surprises than we intuitively expect, retaining sufficiently many options to avoid premature closure and conflicts among preferences, and prioritizing outcomes that are steerable, whose consequences are observable, and that do not entail sunk costs, resource depletion, or high transition costs. We illustrate these concepts and guidelines with the example of the medical management of the 2003 SARS outbreak in Vietnam. © 2015 Society for Risk Analysis.

Decision making in a human population living sustainably.

PubMed

Hicks, John S; Burgman, Mark A; Marewski, Julian N; Fidler, Fiona; Gigerenzer, Gerd

2012-10-01

The Tiwi people of northern Australia have managed natural resources continuously for 6000-8000 years. Tiwi management objectives and outcomes may reflect how they gather information about the environment. We qualitatively analyzed Tiwi documents and management techniques to examine the relation between the social and physical environment of decision makers and their decision-making strategies. We hypothesized that principles of bounded rationality, namely, the use of efficient rules to navigate complex decision problems, explain how Tiwi managers use simple decision strategies (i.e., heuristics) to make robust decisions. Tiwi natural resource managers reduced complexity in decision making through a process that gathers incomplete and uncertain information to quickly guide decisions toward effective outcomes. They used management feedback to validate decisions through an information loop that resulted in long-term sustainability of environmental use. We examined the Tiwi decision-making processes relative to management of barramundi (Lates calcarifer) fisheries and contrasted their management with the state government's management of barramundi. Decisions that enhanced the status of individual people and their attainment of aspiration levels resulted in reliable resource availability for Tiwi consumers. Different decision processes adopted by the state for management of barramundi may not secure similarly sustainable outcomes. ©2012 Society for Conservation Biology.

Intelligent Model Management in a Forest Ecosystem Management Decision Support System

Treesearch

Donald Nute; Walter D. Potter; Frederick Maier; Jin Wang; Mark Twery; H. Michael Rauscher; Peter Knopp; Scott Thomasma; Mayukh Dass; Hajime Uchiyama

2002-01-01

Decision making for forest ecosystem management can include the use of a wide variety of modeling tools. These tools include vegetation growth models, wildlife models, silvicultural models, GIS, and visualization tools. NED-2 is a robust, intelligent, goal-driven decision support system that integrates tools in each of these categories. NED-2 uses a blackboard...

Altered Risk-Based Decision Making following Adolescent Alcohol Use Results from an Imbalance in Reinforcement Learning in Rats

PubMed Central

Hart, Andrew S.; Collins, Anne L.; Bernstein, Ilene L.; Phillips, Paul E. M.

2012-01-01

Alcohol use during adolescence has profound and enduring consequences on decision-making under risk. However, the fundamental psychological processes underlying these changes are unknown. Here, we show that alcohol use produces over-fast learning for better-than-expected, but not worse-than-expected, outcomes without altering subjective reward valuation. We constructed a simple reinforcement learning model to simulate altered decision making using behavioral parameters extracted from rats with a history of adolescent alcohol use. Remarkably, the learning imbalance alone was sufficient to simulate the divergence in choice behavior observed between these groups of animals. These findings identify a selective alteration in reinforcement learning following adolescent alcohol use that can account for a robust change in risk-based decision making persisting into later life. PMID:22615989

Correlates of healthcare and financial decision making among older adults without dementia.

PubMed

Stewart, Christopher C; Yu, Lei; Wilson, Robert S; Bennett, David A; Boyle, Patricia A

2018-03-22

Healthcare and financial decision making among older persons has been previously associated with cognition, health and financial literacy, and risk aversion; however, the manner by which these resources support decision making remains unclear, as past studies have not systematically investigated the pathways linking these resources with decision making. In the current study, we use path analysis to examine the direct and indirect pathways linking age, education, cognition, literacy, and risk aversion with decision making. We also decomposed literacy into its subcomponents, conceptual knowledge and numeracy, in order to examine their associations with decision making. Participants were 937 community-based older adults without dementia from the Rush Memory and Aging Project who completed a battery of cognitive tests and assessments of healthcare and financial decision making, health and financial literacy, and risk aversion. Age and education exerted effects on decision making, but nearly two thirds of their effects were indirect, working mostly through cognition and literacy. Cognition exerted a strong direct effect on decision making and a robust indirect effect working primarily through literacy. Literacy also exerted a powerful direct effect on decision making, as did its subcomponents, conceptual knowledge and numeracy. The direct effect of risk aversion was comparatively weak. In addition to cognition, health and financial literacy emerged as independent and primary correlates of healthcare and financial decision making. These findings suggest specific actions that might be taken to optimize healthcare and financial decision making and, by extension, improve health and well-being in advanced age. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Determining the psychometric properties of the Enhancing Decision-making Assessment in Midwifery (EDAM) measure in a cross cultural context.

PubMed

Jefford, Elaine; Jomeen, Julie; Martin, Colin R

2016-04-28

The ability to act on and justify clinical decisions as autonomous accountable midwifery practitioners, is encompassed within many international regulatory frameworks, yet decision-making within midwifery is poorly defined. Decision-making theories from medicine and nursing may have something to offer, but fail to take into consideration midwifery context and philosophy and the decisional autonomy of women. Using an underpinning qualitative methodology, a decision-making framework was developed, which identified Good Clinical Reasoning and Good Midwifery Practice as two conditions necessary to facilitate optimal midwifery decision-making during 2nd stage labour. This study aims to confirm the robustness of the framework and describe the development of Enhancing Decision-making Assessment in Midwifery (EDAM) as a measurement tool through testing of its factor structure, validity and reliability. A cross-sectional design for instrument development and a 2 (country; Australia/UK) x 2 (Decision-making; optimal/sub-optimal) between-subjects design for instrument evaluation using exploratory and confirmatory factor analysis, internal consistency and known-groups validity. Two 'expert' maternity panels, based in Australia and the UK, comprising of 42 participants assessed 16 midwifery real care episode vignettes using the empirically derived 26 item framework. Each item was answered on a 5 point likert scale based on the level of agreement to which the participant felt each item was present in each of the vignettes. Participants were then asked to rate the overall decision-making (optimal/sub-optimal). Post factor analysis the framework was reduced to a 19 item EDAM measure, and confirmed as two distinct scales of 'Clinical Reasoning' (CR) and 'Midwifery Practice' (MP). The CR scale comprised of two subscales; 'the clinical reasoning process' and 'integration and intervention'. The MP scale also comprised two subscales; women's relationship with the midwife' and 'general midwifery practice'. EDAM would generally appear to be a robust, valid and reliable psychometric instrument for measuring midwifery decision-making, which performs consistently across differing international contexts. The 'women's relationship with midwife' subscale marginally failed to meet the threshold for determining good instrument reliability, which may be due to its brevity. Further research using larger samples and in a wider international context to confirm the veracity of the instrument's measurement properties and its wider global utility, would be advantageous.

Changes in Cognition and Decision Making Capacity Following Brain Tumour Resection: Illustrated with Two Cases.

PubMed

Veretennikoff, Katie; Walker, David; Biggs, Vivien; Robinson, Gail

2017-09-24

Changes in cognition, behaviour and emotion frequently occur in patients with primary and secondary brain tumours. This impacts the ability to make considered decisions, especially following surgical resection, which is often overlooked in the management of patients. Moreover, the impact of cognitive deficits on decision making ability affects activities of daily living and functional independence. The assessment process to ascertain decision making capacity remains a matter of debate. One avenue for evaluating a patient's ability to make informed decisions in the context of brain tumour resection is neuropsychological assessment. This involves the assessment of a wide range of cognitive abilities on standard measurement tools, providing a robust approach to ascertaining capacity. Evidence has shown that a comprehensive and tailored neuropsychological assessment has greater sensitivity than brief cognitive screening tools to detect subtle and/or specific cognitive deficits in brain tumours. It is the precise nature and severity of any cognitive deficits that determines any implications for decision making capacity. This paper focuses on cognitive deficits and decision making capacity following surgical resection of both benign and malignant, and primary and secondary brain tumours in adult patients, and the implications for patients' ability to consent to future medical treatment and make decisions related to everyday activities.

Changes in Cognition and Decision Making Capacity Following Brain Tumour Resection: Illustrated with Two Cases

PubMed Central

Veretennikoff, Katie; Walker, David; Biggs, Vivien; Robinson, Gail

2017-01-01

Changes in cognition, behaviour and emotion frequently occur in patients with primary and secondary brain tumours. This impacts the ability to make considered decisions, especially following surgical resection, which is often overlooked in the management of patients. Moreover, the impact of cognitive deficits on decision making ability affects activities of daily living and functional independence. The assessment process to ascertain decision making capacity remains a matter of debate. One avenue for evaluating a patient’s ability to make informed decisions in the context of brain tumour resection is neuropsychological assessment. This involves the assessment of a wide range of cognitive abilities on standard measurement tools, providing a robust approach to ascertaining capacity. Evidence has shown that a comprehensive and tailored neuropsychological assessment has greater sensitivity than brief cognitive screening tools to detect subtle and/or specific cognitive deficits in brain tumours. It is the precise nature and severity of any cognitive deficits that determines any implications for decision making capacity. This paper focuses on cognitive deficits and decision making capacity following surgical resection of both benign and malignant, and primary and secondary brain tumours in adult patients, and the implications for patients’ ability to consent to future medical treatment and make decisions related to everyday activities. PMID:28946652

How Reasoning, Judgment, and Decision Making are Colored by Gist-based Intuition: A Fuzzy-Trace Theory Approach

PubMed Central

Corbin, Jonathan C.; Reyna, Valerie F.; Weldon, Rebecca B.; Brainerd, Charles J.

2015-01-01

Fuzzy-trace theory distinguishes verbatim (literal, exact) from gist (meaningful) representations, predicting that reliance on gist increases with experience and expertise. Thus, many judgment-and-decision-making biases increase with development, such that cognition is colored by context in ways that violate logical coherence and probability theories. Nevertheless, this increase in gist-based intuition is adaptive: Gist is stable, less sensitive to interference, and easier to manipulate. Moreover, gist captures the functionally significant essence of information, supporting healthier and more robust decision processes. We describe how fuzzy-trace theory accounts for judgment-and-decision making phenomena, predicting the paradoxical arc of these processes with the development of experience and expertise. We present data linking gist memory processes to gist processing in decision making and provide illustrations of gist reliance in medicine, public health, and intelligence analysis. PMID:26664820

How Reasoning, Judgment, and Decision Making are Colored by Gist-based Intuition: A Fuzzy-Trace Theory Approach.

PubMed

Corbin, Jonathan C; Reyna, Valerie F; Weldon, Rebecca B; Brainerd, Charles J

2015-12-01

Fuzzy-trace theory distinguishes verbatim (literal, exact) from gist (meaningful) representations, predicting that reliance on gist increases with experience and expertise. Thus, many judgment-and-decision-making biases increase with development, such that cognition is colored by context in ways that violate logical coherence and probability theories. Nevertheless, this increase in gist-based intuition is adaptive: Gist is stable, less sensitive to interference, and easier to manipulate. Moreover, gist captures the functionally significant essence of information, supporting healthier and more robust decision processes. We describe how fuzzy-trace theory accounts for judgment-and-decision making phenomena, predicting the paradoxical arc of these processes with the development of experience and expertise. We present data linking gist memory processes to gist processing in decision making and provide illustrations of gist reliance in medicine, public health, and intelligence analysis.

Fuzzy decision-making framework for treatment selection based on the combined QUALIFLEX-TODIM method

NASA Astrophysics Data System (ADS)

Ji, Pu; Zhang, Hong-yu; Wang, Jian-qiang

2017-10-01

Treatment selection is a multi-criteria decision-making problem of significant concern in the medical field. In this study, a fuzzy decision-making framework is established for treatment selection. The framework mitigates information loss by introducing single-valued trapezoidal neutrosophic numbers to denote evaluation information. Treatment selection has multiple criteria that remarkably exceed the alternatives. In consideration of this characteristic, the framework utilises the idea of the qualitative flexible multiple criteria method. Furthermore, it considers the risk-averse behaviour of a decision maker by employing a concordance index based on TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method. A sensitivity analysis is performed to illustrate the robustness of the framework. Finally, a comparative analysis is conducted to compare the framework with several extant methods. Results indicate the advantages of the framework and its better performance compared with the extant methods.

Building a maintenance policy through a multi-criterion decision-making model

NASA Astrophysics Data System (ADS)

Faghihinia, Elahe; Mollaverdi, Naser

2012-08-01

A major competitive advantage of production and service systems is establishing a proper maintenance policy. Therefore, maintenance managers should make maintenance decisions that best fit their systems. Multi-criterion decision-making methods can take into account a number of aspects associated with the competitiveness factors of a system. This paper presents a multi-criterion decision-aided maintenance model with three criteria that have more influence on decision making: reliability, maintenance cost, and maintenance downtime. The Bayesian approach has been applied to confront maintenance failure data shortage. Therefore, the model seeks to make the best compromise between these three criteria and establish replacement intervals using Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II), integrating the Bayesian approach with regard to the preference of the decision maker to the problem. Finally, using a numerical application, the model has been illustrated, and for a visual realization and an illustrative sensitivity analysis, PROMETHEE GAIA (the visual interactive module) has been used. Use of PROMETHEE II and PROMETHEE GAIA has been made with Decision Lab software. A sensitivity analysis has been made to verify the robustness of certain parameters of the model.

A new decision sciences for complex systems.

PubMed

Lempert, Robert J

2002-05-14

Models of complex systems can capture much useful information but can be difficult to apply to real-world decision-making because the type of information they contain is often inconsistent with that required for traditional decision analysis. New approaches, which use inductive reasoning over large ensembles of computational experiments, now make possible systematic comparison of alternative policy options using models of complex systems. This article describes Computer-Assisted Reasoning, an approach to decision-making under conditions of deep uncertainty that is ideally suited to applying complex systems to policy analysis. The article demonstrates the approach on the policy problem of global climate change, with a particular focus on the role of technology policies in a robust, adaptive strategy for greenhouse gas abatement.

Making the Most of Ourselves

ERIC Educational Resources Information Center

Groombridge, Brian

2008-01-01

Foresight reports are meant to help "decision-makers" understand the possible future effects of their decisions. "Visions of the future", based on "robust science", should be used by policymakers "to inform government policy and strategy, and to improve how science and technology are used within government and by society". They are also intended…

Optimal decision making modeling for copper-matte Peirce-Smith converting process by means of data mining

NASA Astrophysics Data System (ADS)

Song, Yanpo; Peng, Xiaoqi; Tang, Ying; Hu, Zhikun

2013-07-01

To improve the operation level of copper converter, the approach to optimal decision making modeling for coppermatte converting process based on data mining is studied: in view of the characteristics of the process data, such as containing noise, small sample size and so on, a new robust improved ANN (artificial neural network) modeling method is proposed; taking into account the application purpose of decision making model, three new evaluation indexes named support, confidence and relative confidence are proposed; using real production data and the methods mentioned above, optimal decision making model for blowing time of S1 period (the 1st slag producing period) are developed. Simulation results show that this model can significantly improve the converting quality of S1 period, increase the optimal probability from about 70% to about 85%.

A robust optimisation approach to the problem of supplier selection and allocation in outsourcing

NASA Astrophysics Data System (ADS)

Fu, Yelin; Keung Lai, Kin; Liang, Liang

2016-03-01

We formulate the supplier selection and allocation problem in outsourcing under an uncertain environment as a stochastic programming problem. Both the decision-maker's attitude towards risk and the penalty parameters for demand deviation are considered in the objective function. A service level agreement, upper bound for each selected supplier's allocation and the number of selected suppliers are considered as constraints. A novel robust optimisation approach is employed to solve this problem under different economic situations. Illustrative examples are presented with managerial implications highlighted to support decision-making.

Separate neural mechanisms underlie choices and strategic preferences in risky decision making.

PubMed

Venkatraman, Vinod; Payne, John W; Bettman, James R; Luce, Mary Frances; Huettel, Scott A

2009-05-28

Adaptive decision making in real-world contexts often relies on strategic simplifications of decision problems. Yet, the neural mechanisms that shape these strategies and their implementation remain largely unknown. Using an economic decision-making task, we dissociate brain regions that predict specific choices from those predicting an individual's preferred strategy. Choices that maximized gains or minimized losses were predicted by functional magnetic resonance imaging activation in ventromedial prefrontal cortex or anterior insula, respectively. However, choices that followed a simplifying strategy (i.e., attending to overall probability of winning) were associated with activation in parietal and lateral prefrontal cortices. Dorsomedial prefrontal cortex, through differential functional connectivity with parietal and insular cortex, predicted individual variability in strategic preferences. Finally, we demonstrate that robust decision strategies follow from neural sensitivity to rewards. We conclude that decision making reflects more than compensatory interaction of choice-related regions; in addition, specific brain systems potentiate choices depending on strategies, traits, and context.

Separate neural mechanisms underlie choices and strategic preferences in risky decision making

PubMed Central

Venkatraman, Vinod; Payne, John W.; Bettman, James R.; Luce, Mary Frances; Huettel, Scott A.

2011-01-01

Adaptive decision making in real-world contexts often relies on strategic simplifications of decision problems. Yet, the neural mechanisms that shape these strategies and their implementation remain largely unknown. Using a novel economic decision-making task, we dissociate brain regions that predict specific choices from those predicting an individual’s preferred strategy. Choices that maximized gains or minimized losses were predicted by fMRI activation in ventromedial prefrontal cortex or anterior insula, respectively. However, choices that followed a simplifying strategy (i.e., attending to overall probability of winning) were associated with activation in parietal and lateral prefrontal cortices. Dorsomedial prefrontal cortex, through differential functional connectivity with parietal and insular cortex, predicted individual variability in strategic preferences. Finally, we demonstrate that robust decision strategies follow from neural sensitivity to rewards. We conclude that decision making reflects more than compensatory interaction of choice-related regions; in addition, specific brain systems potentiate choices depending upon strategies, traits, and context. PMID:19477159

Ensemble modelling and structured decision-making to support Emergency Disease Management.

PubMed

Webb, Colleen T; Ferrari, Matthew; Lindström, Tom; Carpenter, Tim; Dürr, Salome; Garner, Graeme; Jewell, Chris; Stevenson, Mark; Ward, Michael P; Werkman, Marleen; Backer, Jantien; Tildesley, Michael

2017-03-01

Epidemiological models in animal health are commonly used as decision-support tools to understand the impact of various control actions on infection spread in susceptible populations. Different models contain different assumptions and parameterizations, and policy decisions might be improved by considering outputs from multiple models. However, a transparent decision-support framework to integrate outputs from multiple models is nascent in epidemiology. Ensemble modelling and structured decision-making integrate the outputs of multiple models, compare policy actions and support policy decision-making. We briefly review the epidemiological application of ensemble modelling and structured decision-making and illustrate the potential of these methods using foot and mouth disease (FMD) models. In case study one, we apply structured decision-making to compare five possible control actions across three FMD models and show which control actions and outbreak costs are robustly supported and which are impacted by model uncertainty. In case study two, we develop a methodology for weighting the outputs of different models and show how different weighting schemes may impact the choice of control action. Using these case studies, we broadly illustrate the potential of ensemble modelling and structured decision-making in epidemiology to provide better information for decision-making and outline necessary development of these methods for their further application. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Decision making in urological surgery.

PubMed

Abboudi, Hamid; Ahmed, Kamran; Normahani, Pasha; Abboudi, May; Kirby, Roger; Challacombe, Ben; Khan, Mohammed Shamim; Dasgupta, Prokar

2012-06-01

Non-technical skills are important behavioural aspects that a urologist must be fully competent at to minimise harm to patients. The majority of surgical errors are now known to be due to errors in judgment and decision making as opposed to the technical aspects of the craft. The authors reviewed the published literature regarding decision-making theory and in practice related to urology as well as the current tools available to assess decision-making skills. Limitations include limited number of studies, and the available studies are of low quality. Decision making is the psychological process of choosing between alternative courses of action. In the surgical environment, this can often be a complex balance of benefit and risk within a variable time frame and dynamic setting. In recent years, the emphasis of new surgical curriculums has shifted towards non-technical surgical skills; however, the assessment tools in place are far from objective, reliable and valid. Surgical simulators and video-assisted questionnaires are useful methods for appraisal of trainees. Well-designed, robust and validated tools need to be implemented in training and assessment of decision-making skills in urology. Patient safety can only be ensured when safe and effective decisions are made.

Using multi-objective robust decision making to support seasonal water management in the Chao Phraya River basin, Thailand

NASA Astrophysics Data System (ADS)

Riegels, Niels; Jessen, Oluf; Madsen, Henrik

2016-04-01

A multi-objective robust decision making approach is demonstrated that supports seasonal water management in the Chao Phraya River basin in Thailand. The approach uses multi-objective optimization to identify a Pareto-optimal set of management alternatives. Ensemble simulation is used to evaluate how each member of the Pareto set performs under a range of uncertain future conditions, and a robustness criterion is used to select a preferred alternative. Data mining tools are then used to identify ranges of uncertain factor values that lead to unacceptable performance for the preferred alternative. The approach is compared to a multi-criteria scenario analysis approach to estimate whether the introduction of additional complexity has the potential to improve decision making. Dry season irrigation in Thailand is managed through non-binding recommendations about the maximum extent of rice cultivation along with incentives for less water-intensive crops. Management authorities lack authority to prevent river withdrawals for irrigation when rice cultivation exceeds recommendations. In practice, this means that water must be provided to irrigate the actual planted area because of downstream municipal water supply requirements and water quality constraints. This results in dry season reservoir withdrawals that exceed planned withdrawals, reducing carryover storage to hedge against insufficient wet season runoff. The dry season planning problem in Thailand can therefore be framed in terms of decisions, objectives, constraints, and uncertainties. Decisions include recommendations about the maximum extent of rice cultivation and incentives for growing less water-intensive crops. Objectives are to maximize benefits to farmers, minimize the risk of inadequate carryover storage, and minimize incentives. Constraints include downstream municipal demands and water quality requirements. Uncertainties include the actual extent of rice cultivation, dry season precipitation, and precipitation in the following wet season. The multi-objective robust decision making approach is implemented as follows. First, three baseline simulation models are developed, including a crop water demand model, a river basin simulation model, and model of the impact of incentives on cropping patterns. The crop water demand model estimates irrigation water demands; the river basin simulation model estimates reservoir drawdown required to meet demands given forecasts of precipitation, evaporation, and runoff; the model of incentive impacts estimates the cost of incentives as function of marginal changes in rice yields. Optimization is used to find a set of non-dominated alternatives as a function of rice area and incentive decisions. An ensemble of uncertain model inputs is generated to represent uncertain hydrological and crop area forecasts. An ensemble of indicator values is then generated for each of the decision objectives: farmer benefits, end-of-wet-season reservoir storage, and the cost of incentives. A single alternative is selected from the Pareto set using a robustness criterion. Threshold values are defined for each of the objectives to identify ensemble members for which objective values are unacceptable, and the PRIM data mining algorithm is then used to identify input values associated with unacceptable model outcomes.

Less can be more: How to make operations more flexible and robust with fewer resources

NASA Astrophysics Data System (ADS)

Haksöz, ćaǧrı; Katsikopoulos, Konstantinos; Gigerenzer, Gerd

2018-06-01

We review empirical evidence from practice and general theoretical conditions, under which simple rules of thumb can help to make operations flexible and robust. An operation is flexible when it responds adaptively to adverse events such as natural disasters; an operation is robust when it is less affected by adverse events in the first place. We illustrate the relationship between flexibility and robustness in the context of supply chain risk. In addition to increasing flexibility and robustness, simple rules simultaneously reduce the need for resources such as time, money, information, and computation. We illustrate the simple-rules approach with an easy-to-use graphical aid for diagnosing and managing supply chain risk. More generally, we recommend a four-step process for determining the amount of resources that decision makers should invest in so as to increase flexibility and robustness.

Decision-making under surprise and uncertainty: Arsenic contamination of water supplies

NASA Astrophysics Data System (ADS)

Randhir, Timothy O.; Mozumder, Pallab; Halim, Nafisa

2018-05-01

With ignorance and potential surprise dominating decision making in water resources, a framework for dealing with such uncertainty is a critical need in hydrology. We operationalize the 'potential surprise' criterion proposed by Shackle, Vickers, and Katzner (SVK) to derive decision rules to manage water resources under uncertainty and ignorance. We apply this framework to managing water supply systems in Bangladesh that face severe, naturally occurring arsenic contamination. The uncertainty involved with arsenic in water supplies makes the application of conventional analysis of decision-making ineffective. Given the uncertainty and surprise involved in such cases, we find that optimal decisions tend to favor actions that avoid irreversible outcomes instead of conventional cost-effective actions. We observe that a diversification of the water supply system also emerges as a robust strategy to avert unintended outcomes of water contamination. Shallow wells had a slight higher optimal level (36%) compare to deep wells and surface treatment which had allocation levels of roughly 32% under each. The approach can be applied in a variety of other cases that involve decision making under uncertainty and surprise, a frequent situation in natural resources management.

Carl von Clausewitz’s Concept of Military Genius and the Cognitive Illusions that Preclude Clear Thinking

DTIC Science & Technology

2013-03-25

commanders to make the best possible decisions in a given set of circumstances. Cognitive psychologists have conducted research and developed ...military professionals develop a more robust appreciation for the cognitive processes that preclude clear thinking and effective decision making...Additionally, she had the subjects sketch a bicycle , which was the most telling part of the study. More than 97% knew how to ride a bike, but the

Pandemic influenza preparedness: an ethical framework to guide decision-making.

PubMed

Thompson, Alison K; Faith, Karen; Gibson, Jennifer L; Upshur, Ross E G

2006-12-04

Planning for the next pandemic influenza outbreak is underway in hospitals across the world. The global SARS experience has taught us that ethical frameworks to guide decision-making may help to reduce collateral damage and increase trust and solidarity within and between health care organisations. Good pandemic planning requires reflection on values because science alone cannot tell us how to prepare for a public health crisis. In this paper, we present an ethical framework for pandemic influenza planning. The ethical framework was developed with expertise from clinical, organisational and public health ethics and validated through a stakeholder engagement process. The ethical framework includes both substantive and procedural elements for ethical pandemic influenza planning. The incorporation of ethics into pandemic planning can be helped by senior hospital administrators sponsoring its use, by having stakeholders vet the framework, and by designing or identifying decision review processes. We discuss the merits and limits of an applied ethical framework for hospital decision-making, as well as the robustness of the framework. The need for reflection on the ethical issues raised by the spectre of a pandemic influenza outbreak is great. Our efforts to address the normative aspects of pandemic planning in hospitals have generated interest from other hospitals and from the governmental sector. The framework will require re-evaluation and refinement and we hope that this paper will generate feedback on how to make it even more robust.

Revisiting the generation and interpretation of climate models experiments for adaptation decision-making (Invited)

NASA Astrophysics Data System (ADS)

Ranger, N.; Millner, A.; Niehoerster, F.

2010-12-01

Traditionally, climate change risk assessments have taken a roughly four-stage linear ‘chain’ of moving from socioeconomic projections, to climate projections, to primary impacts and then finally onto economic and social impact assessment. Adaptation decisions are then made on the basis of these outputs. The escalation of uncertainty through this chain is well known; resulting in an ‘explosion’ of uncertainties in the final risk and adaptation assessment. The space of plausible future risk scenarios is growing ever wider with the application of new techniques which aim to explore uncertainty ever more deeply; such as those used in the recent ‘probabilistic’ UK Climate Projections 2009, and the stochastic integrated assessment models, for example PAGE2002. This explosion of uncertainty can make decision-making problematic, particularly given that the uncertainty information communicated can not be treated as strictly probabilistic and therefore, is not an easy fit with standard decision-making under uncertainty approaches. Additional problems can arise from the fact that the uncertainty estimated for different components of the ‘chain’ is rarely directly comparable or combinable. Here, we explore the challenges and limitations of using current projections for adaptation decision-making. We report the findings of a recent report completed for the UK Adaptation Sub-Committee on approaches to deal with these challenges and make robust adaptation decisions today. To illustrate these approaches, we take a number of illustrative case studies, including a case of adaptation to hurricane risk on the US Gulf Coast. This is a particularly interesting case as it involves urgent adaptation of long-lived infrastructure but requires interpreting highly uncertain climate change science and modelling; i.e. projections of Atlantic basin hurricane activity. An approach we outline is reversing the linear chain of assessments to put the economics and decision-making first. Such an approach forces one to focus on the information of greatest value for the specific decision. We suggest that such an approach will help to accommodate the uncertainties in the chain and facilitate robust decision-making. Initial findings of these case studies will be presented with the aim of raising open questions and promoting discussion of the methodology. Finally, we reflect on the implications for the design of climate model experiments.

Autonomy, evidence and intuition: nurses and decision-making.

PubMed

Traynor, Michael; Boland, Maggie; Buus, Niels

2010-07-01

This paper is a report of a study conducted to examine how nurses represent professional clinical decision-making processes, and to determine what light Jamous and Peloille's 'Indeterminacy/Technicality ratio' concept can shed on these representations. Classic definitions of professional work feature autonomy of decision-making and control over the field of work. Sociologists Jamous and Peloille have described professional work as being high in 'indeterminacy' (the use of tacit judgements) relative to technicality (activity able to be codified). The rise of the evidence-based practice movement has been seen as increasing the realm of technical decision-making in healthcare, and it is relevant to analyse nurses' professional discourse and study how they respond to this increase. Three focus groups with qualified nurses attending post-qualifying courses at a London university were held in 2008. Participants were asked to talk about influences on their decision-making. The discussions were tape-recorded, transcribed and subjected to discourse analysis. Participants described their decision-making as influenced by both indeterminate and technical features. They acknowledged useful influences from both domains, but pointed to their personal 'experience' as the final arbiter of decision-making. Their accounts of decision-making created a sense of professional autonomy while at the same time protecting it against external critique. Pre- and post-registration nurse education could encourage robust discussion of the definition and roles of 'irrational' aspects of decision-making and how these might be understood as components of credible professional practice.

The Metropolitan Studies Institute at USC Upstate: Translational Research that Drives Community Decision-Making

ERIC Educational Resources Information Center

Brady, Kathleen

2012-01-01

The Metropolitan Studies Institute (MSI) at the University of South Carolina Upstate (USC Upstate) demonstrates a robust and unique record of community impact through community indicators research and other translational research. The MSI's work drives programmatic priorities and funding decisions, generates revenue, and increases the community's…

Cognitive reflection vs. calculation in decision making

PubMed Central

Sinayev, Aleksandr; Peters, Ellen

2015-01-01

Scores on the three-item Cognitive Reflection Test (CRT) have been linked with dual-system theory and normative decision making (Frederick, 2005). In particular, the CRT is thought to measure monitoring of System 1 intuitions such that, if cognitive reflection is high enough, intuitive errors will be detected and the problem will be solved. However, CRT items also require numeric ability to be answered correctly and it is unclear how much numeric ability vs. cognitive reflection contributes to better decision making. In two studies, CRT responses were used to calculate Cognitive Reflection and numeric ability; a numeracy scale was also administered. Numeric ability, measured on the CRT or the numeracy scale, accounted for the CRT's ability to predict more normative decisions (a subscale of decision-making competence, incentivized measures of impatient and risk-averse choice, and self-reported financial outcomes); Cognitive Reflection contributed no independent predictive power. Results were similar whether the two abilities were modeled (Study 1) or calculated using proportions (Studies 1 and 2). These findings demonstrate numeric ability as a robust predictor of superior decision making across multiple tasks and outcomes. They also indicate that correlations of decision performance with the CRT are insufficient evidence to implicate overriding intuitions in the decision-making biases and outcomes we examined. Numeric ability appears to be the key mechanism instead. PMID:25999877

Cognitive reflection vs. calculation in decision making.

PubMed

Sinayev, Aleksandr; Peters, Ellen

2015-01-01

Scores on the three-item Cognitive Reflection Test (CRT) have been linked with dual-system theory and normative decision making (Frederick, 2005). In particular, the CRT is thought to measure monitoring of System 1 intuitions such that, if cognitive reflection is high enough, intuitive errors will be detected and the problem will be solved. However, CRT items also require numeric ability to be answered correctly and it is unclear how much numeric ability vs. cognitive reflection contributes to better decision making. In two studies, CRT responses were used to calculate Cognitive Reflection and numeric ability; a numeracy scale was also administered. Numeric ability, measured on the CRT or the numeracy scale, accounted for the CRT's ability to predict more normative decisions (a subscale of decision-making competence, incentivized measures of impatient and risk-averse choice, and self-reported financial outcomes); Cognitive Reflection contributed no independent predictive power. Results were similar whether the two abilities were modeled (Study 1) or calculated using proportions (Studies 1 and 2). These findings demonstrate numeric ability as a robust predictor of superior decision making across multiple tasks and outcomes. They also indicate that correlations of decision performance with the CRT are insufficient evidence to implicate overriding intuitions in the decision-making biases and outcomes we examined. Numeric ability appears to be the key mechanism instead.

Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems

DOE PAGES

Alamaniotis, Miltiadis; Agarwal, Vivek

2014-04-01

Anticipatory control systems are a class of systems whose decisions are based on predictions for the future state of the system under monitoring. Anticipation denotes intelligence and is an inherent property of humans that make decisions by projecting in future. Likewise, artificially intelligent systems equipped with predictive functions may be utilized for anticipating future states of complex systems, and therefore facilitate automated control decisions. Anticipatory control of complex energy systems is paramount to their normal and safe operation. In this paper a new intelligent methodology integrating fuzzy inference with support vector regression is introduced. Our proposed methodology implements an anticipatorymore » system aiming at controlling energy systems in a robust way. Initially a set of support vector regressors is adopted for making predictions over critical system parameters. Furthermore, the predicted values are fed into a two stage fuzzy inference system that makes decisions regarding the state of the energy system. The inference system integrates the individual predictions into a single one at its first stage, and outputs a decision together with a certainty factor computed at its second stage. The certainty factor is an index of the significance of the decision. The proposed anticipatory control system is tested on a real world set of data obtained from a complex energy system, describing the degradation of a turbine. Results exhibit the robustness of the proposed system in controlling complex energy systems.« less

Processing of social and monetary rewards in the human striatum.

PubMed

Izuma, Keise; Saito, Daisuke N; Sadato, Norihiro

2008-04-24

Despite an increasing focus on the neural basis of human decision making in neuroscience, relatively little attention has been paid to decision making in social settings. Moreover, although human social decision making has been explored in a social psychology context, few neural explanations for the observed findings have been considered. To bridge this gap and improve models of human social decision making, we investigated whether acquiring a good reputation, which is an important incentive in human social behaviors, activates the same reward circuitry as monetary rewards. In total, 19 subjects participated in functional magnetic resonance imaging (fMRI) experiments involving monetary and social rewards. The acquisition of one's good reputation robustly activated reward-related brain areas, notably the striatum, and these overlapped with the areas activated by monetary rewards. Our findings support the idea of a "common neural currency" for rewards and represent an important first step toward a neural explanation for complex human social behaviors.

Robust Decision Making Approach to Managing Water Resource Risks (Invited)

NASA Astrophysics Data System (ADS)

Lempert, R.

2010-12-01

The IPCC and US National Academies of Science have recommended iterative risk management as the best approach for water management and many other types of climate-related decisions. Such an approach does not rely on a single set of judgments at any one time but rather actively updates and refines strategies as new information emerges. In addition, the approach emphasizes that a portfolio of different types of responses, rather than any single action, often provides the best means to manage uncertainty. Implementing an iterative risk management approach can however prove difficult in actual decision support applications. This talk will suggest that robust decision making (RDM) provides a particularly useful set of quantitative methods for implementing iterative risk management. This RDM approach is currently being used in a wide variety of water management applications. RDM employs three key concepts that differentiate it from most types of probabilistic risk analysis: 1) characterizing uncertainty with multiple views of the future (which can include sets of probability distributions) rather than a single probabilistic best-estimate, 2) employing a robustness rather than an optimality criterion to assess alternative policies, and 3) organizing the analysis with a vulnerability and response option framework, rather than a predict-then-act framework. This talk will summarize the RDM approach, describe its use in several different types of water management applications, and compare the results to those obtained with other methods.

Robust Decision Making in a Nonlinear World

ERIC Educational Resources Information Center

Dougherty, Michael R.; Thomas, Rick P.

2012-01-01

The authors propose a general modeling framework called the general monotone model (GeMM), which allows one to model psychological phenomena that manifest as nonlinear relations in behavior data without the need for making (overly) precise assumptions about functional form. Using both simulated and real data, the authors illustrate that GeMM…

A fuzzy stochastic framework for managing hydro-environmental and socio-economic interactions under uncertainty

NASA Astrophysics Data System (ADS)

Subagadis, Yohannes Hagos; Schütze, Niels; Grundmann, Jens

2014-05-01

An amplified interconnectedness between a hydro-environmental and socio-economic system brings about profound challenges of water management decision making. In this contribution, we present a fuzzy stochastic approach to solve a set of decision making problems, which involve hydrologically, environmentally, and socio-economically motivated criteria subjected to uncertainty and ambiguity. The proposed methodological framework combines objective and subjective criteria in a decision making procedure for obtaining an acceptable ranking in water resources management alternatives under different type of uncertainty (subjective/objective) and heterogeneous information (quantitative/qualitative) simultaneously. The first step of the proposed approach involves evaluating the performance of alternatives with respect to different types of criteria. The ratings of alternatives with respect to objective and subjective criteria are evaluated by simulation-based optimization and fuzzy linguistic quantifiers, respectively. Subjective and objective uncertainties related to the input information are handled through linking fuzziness and randomness together. Fuzzy decision making helps entail the linguistic uncertainty and a Monte Carlo simulation process is used to map stochastic uncertainty. With this framework, the overall performance of each alternative is calculated using an Order Weighted Averaging (OWA) aggregation operator accounting for decision makers' experience and opinions. Finally, ranking is achieved by conducting pair-wise comparison of management alternatives. This has been done on the basis of the risk defined by the probability of obtaining an acceptable ranking and mean difference in total performance for the pair of management alternatives. The proposed methodology is tested in a real-world hydrosystem, to find effective and robust intervention strategies for the management of a coastal aquifer system affected by saltwater intrusion due to excessive groundwater extraction for irrigated agriculture and municipal use. The results show that the approach gives useful support for robust decision-making and is sensitive to the decision makers' degree of optimism.

Applying predictive analytics to develop an intelligent risk detection application for healthcare contexts.

PubMed

Moghimi, Fatemeh Hoda; Cheung, Michael; Wickramasinghe, Nilmini

2013-01-01

Healthcare is an information rich industry where successful outcomes require the processing of multi-spectral data and sound decision making. The exponential growth of data and big data issues coupled with a rapid increase of service demands in healthcare contexts today, requires a robust framework enabled by IT (information technology) solutions as well as real-time service handling in order to ensure superior decision making and successful healthcare outcomes. Such a context is appropriate for the application of real time intelligent risk detection decision support systems using predictive analytic techniques such as data mining. To illustrate the power and potential of data science technologies in healthcare decision making scenarios, the use of an intelligent risk detection (IRD) model is proffered for the context of Congenital Heart Disease (CHD) in children, an area which requires complex high risk decisions that need to be made expeditiously and accurately in order to ensure successful healthcare outcomes.

Diffusion pseudotime robustly reconstructs lineage branching.

PubMed

Haghverdi, Laleh; Büttner, Maren; Wolf, F Alexander; Buettner, Florian; Theis, Fabian J

2016-10-01

The temporal order of differentiating cells is intrinsically encoded in their single-cell expression profiles. We describe an efficient way to robustly estimate this order according to diffusion pseudotime (DPT), which measures transitions between cells using diffusion-like random walks. Our DPT software implementations make it possible to reconstruct the developmental progression of cells and identify transient or metastable states, branching decisions and differentiation endpoints.

Testing take-the-best in new and changing environments.

PubMed

Lee, Michael D; Blanco, Gabrielle; Bo, Nikole

2017-08-01

Take-the-best is a decision-making strategy that chooses between alternatives, by searching the cues representing the alternatives in order of cue validity, and choosing the alternative with the first discriminating cue. Theoretical support for take-the-best comes from the "fast and frugal" approach to modeling cognition, which assumes decision-making strategies need to be fast to cope with a competitive world, and be simple to be robust to uncertainty and environmental change. We contribute to the empirical evaluation of take-the-best in two ways. First, we generate four new environments-involving bridge lengths, hamburger prices, theme park attendances, and US university rankings-supplementing the relatively limited number of naturally cue-based environments previously considered. We find that take-the-best is as accurate as rival decision strategies that use all of the available cues. Secondly, we develop 19 new data sets characterizing the change in cities and their populations in four countries. We find that take-the-best maintains its accuracy and limited search as the environments change, even if cue validities learned in one environment are used to make decisions in another. Once again, we find that take-the-best is as accurate as rival strategies that use all of the cues. We conclude that these new evaluations support the theoretical claims of the accuracy, frugality, and robustness for take-the-best, and that the new data sets provide a valuable resource for the more general study of the relationship between effective decision-making strategies and the environments in which they operate.

The contribution of science to risk-based decision-making: lessons from the development of full-scale treatment measures for acidic mine waters at Wheal Jane, UK.

PubMed

Younger, Paul L; Coulton, Richard H; Froggatt, Eric C

2005-02-01

The use of risk-based decision-making in environmental management is often assumed to rely primarily on the availability of robust scientific data and insights, while in practice socio-economic criteria are often of considerable importance. However, the relative contributions to decision-making made by scientific and socio-economic inputs are rarely assessed, and even less commonly reported. Such an assessment has been made for a major remediation project in southwest England, in which some 300 l/s of highly acidic, metalliferous mine waters are now being treated using oxidation and chemical neutralisation. In the process of reaching the decision to commission the treatment plant, a wide range of scientific studies were undertaken, including: biological impact assessments, hydrogeological investigations of the effect of pumping on the flooded mine system, and hydrological and geochemical characterisation, together with integrated catchment modelling, of pollutant sources and pathways. These investigations revealed that, despite the spectacular nature of the original mine water outburst in 1992, the ecology of the Fal estuary remains remarkably robust. No scientific evidence emerged of any grounds for concern over the estuarine ecology, even if mine water were left to flow untreated. However, a rare ecological resource known as "maerl" (a form of calcified seaweed) is harvested annually in the estuary, providing significant revenue to the local economy and underpinning the 'clean' image of local sea water. Social and environmental benefit surveys revealed strong public perceptions that any visible discoloration in the estuary must indicate a diminution in quality of the maerl, to the detriment of both the public image and economy of the area. This factor proved sufficient to justify the continued pump-and-treat operations at the mine site. Although the decisive factor in the end was socio-economic in nature, robust assessment of this factor could not have been made without robust scientific evidence. It is concluded that investment in investigating and contributing to the formation of public perceptions is just as important as investing in scientific investigations per se.

Configural displays can improve nutrition-related. decisions: an application of the proximity compatibility principle.

PubMed

Marino, Christopher J; Mahan, Robert R

2005-01-01

The nutrition label format currently used by consumers to make dietary-related decisions presents significant information-processing demands for integration-based decisions; however, those demands were not considered as primary factors when the format was adopted. Labels designed in accordance with known principles of cognitive psychology might enhance the kind of decision making that food labeling was intended to facilitate. Three experiments were designed on the basis of the proximity compatibility principle (PCP) to investigate the relationship between nutrition label format and decision making; the experiments involved two types of integration decisions and one type of filtering decision. Based on the PCP, decision performance was measured to test the overall hypothesis that matched task-display tandems would result in better decision performance than would mismatched tandems. In each experiment, a statistically significant increase in mean decision performance was found when the display design was cognitively matched to the demands of the task. Combined, the results from all three experiments support the general hypothesis that task-display matching is a design principle that may enhance the utility of nutrition labeling in nutrition-related decision making. Actual or potential applications of this research include developing robust display solutions that aid in less effortful assimilation of nutrition-related information for consumers.

Prioritization of Stockpile Maintenance with Layered Pareto Fronts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burke, Sarah E.; Anderson-Cook, Christine M.; Lu, Lu

Difficult choices are required for a decision-making process where resources and budgets are increasingly constrained. This study demonstrates a structured decision-making approach using layered Pareto fronts to identify priorities about how to allocate funds between munitions stockpiles based on their estimated reliability, the urgency of needing available units, and the consequences if adequate numbers of units are not available. This case study, while specific to the characteristics of a group of munitions stockpiles, illustrates the general process of structured decision-making based on first identifying appropriate metrics that summarize the important dimensions of the decision, and then objectively eliminating non-contenders frommore » further consideration. Finally, the final subjective stage incorporates user priorities to select the four stockpiles to receive additional maintenance and surveillance funds based on understanding the trade-offs and robustness to various user priorities.« less

Temporal competition between differentiation programs determines cell fate choice

NASA Astrophysics Data System (ADS)

Kuchina, Anna; Espinar, Lorena; Cagatay, Tolga; Balbin, Alejandro; Alvarado, Alma; Garcia-Ojalvo, Jordi; Suel, Gurol

2011-03-01

During pluripotent differentiation, cells adopt one of several distinct fates. The dynamics of this decision-making process are poorly understood, since cell fate choice may be governed by interactions between differentiation programs that are active at the same time. We studied the dynamics of decision-making in the model organism Bacillus subtilis by simultaneously measuring the activities of competing differentiation programs (sporulation and competence) in single cells. We discovered a precise switch-like point of cell fate choice previously hidden by cell-cell variability. Engineered artificial crosslinks between competence and sporulation circuits revealed that the precision of this choice is generated by temporal competition between the key players of two differentiation programs. Modeling suggests that variable progression towards a switch-like decision might represent a general strategy to maximize adaptability and robustness of cellular decision-making.

Prioritization of Stockpile Maintenance with Layered Pareto Fronts

DOE PAGES

Burke, Sarah E.; Anderson-Cook, Christine M.; Lu, Lu; ...

2017-10-11

Difficult choices are required for a decision-making process where resources and budgets are increasingly constrained. This study demonstrates a structured decision-making approach using layered Pareto fronts to identify priorities about how to allocate funds between munitions stockpiles based on their estimated reliability, the urgency of needing available units, and the consequences if adequate numbers of units are not available. This case study, while specific to the characteristics of a group of munitions stockpiles, illustrates the general process of structured decision-making based on first identifying appropriate metrics that summarize the important dimensions of the decision, and then objectively eliminating non-contenders frommore » further consideration. Finally, the final subjective stage incorporates user priorities to select the four stockpiles to receive additional maintenance and surveillance funds based on understanding the trade-offs and robustness to various user priorities.« less

Climate Risk Informed Decision Analysis: A Hypothetical Application to the Waas Region

NASA Astrophysics Data System (ADS)

Gilroy, Kristin; Mens, Marjolein; Haasnoot, Marjolijn; Jeuken, Ad

2016-04-01

More frequent and intense hydrologic events under climate change are expected to enhance water security and flood risk management challenges worldwide. Traditional planning approaches must be adapted to address climate change and develop solutions with an appropriate level of robustness and flexibility. The Climate Risk Informed Decision Analysis (CRIDA) method is a novel planning approach embodying a suite of complementary methods, including decision scaling and adaptation pathways. Decision scaling offers a bottom-up approach to assess risk and tailors the complexity of the analysis to the problem at hand and the available capacity. Through adaptation pathway,s an array of future strategies towards climate robustness are developed, ranging in flexibility and immediacy of investments. Flexible pathways include transfer points to other strategies to ensure that the system can be adapted if future conditions vary from those expected. CRIDA combines these two approaches in a stakeholder driven process which guides decision makers through the planning and decision process, taking into account how the confidence in the available science, the consequences in the system, and the capacity of institutions should influence strategy selection. In this presentation, we will explain the CRIDA method and compare it to existing planning processes, such as the US Army Corps of Engineers Principles and Guidelines as well as Integrated Water Resources Management Planning. Then, we will apply the approach to a hypothetical case study for the Waas Region, a large downstream river basin facing rapid development threatened by increased flood risks. Through the case study, we will demonstrate how a stakeholder driven process can be used to evaluate system robustness to climate change; develop adaptation pathways for multiple objectives and criteria; and illustrate how varying levels of confidence, consequences, and capacity would play a role in the decision making process, specifically in regards to the level of robustness and flexibility in the selected strategy. This work will equip practitioners and decision makers with an example of a structured process for decision making under climate uncertainty that can be scaled as needed to the problem at hand. This presentation builds further on another submitted abstract "Climate Risk Informed Decision Analysis (CRIDA): A novel practical guidance for Climate Resilient Investments and Planning" by Jeuken et al.

Crosstalk Regulates the Capacity for Robust Collective Decision Making in Heterogeneous Microbial Communities

NASA Astrophysics Data System (ADS)

Yusufaly, Tahir; Boedicker, James

Microbial communities frequently communicate via quorum sensing (QS), where cells produce, secrete, and respond to a threshold level of an autoinducer (AI) molecule, thereby modulating density-dependent gene expression. However, the biology of QS remains incompletely understood in heterogeneous communities, where crosstalk between distinct QS systems leads to novel effects. Such knowledge is necessary both for understanding signaling in real microbial communities, and for the rational design of synthetic communities with designer properties. As a step towards this goal, we investigate the effects of crosstalk between Gram-negative bacteria communicating via LuxI/LuxR-type QS systems, with acyl-homoserine lactone (AHL) AI molecules. After mapping QS in a heterogeneous community onto an artificial neural network model, we systematically analyze how heterogeneity regulates the community's capability for stable yet flexible decision making. We find that there are preferred distributions of interactions which provide optimal tradeoffs between capacity, or the number of different decisions a population can make, and robustness, or the tolerance of the community to disturbances. We compare our results to inferences made from experimental data, and critically discuss implications for the biological significance of crosstalk.

Determinants of cell-to-cell variability in protein kinase signaling.

PubMed

Jeschke, Matthias; Baumgärtner, Stephan; Legewie, Stefan

2013-01-01

Cells reliably sense environmental changes despite internal and external fluctuations, but the mechanisms underlying robustness remain unclear. We analyzed how fluctuations in signaling protein concentrations give rise to cell-to-cell variability in protein kinase signaling using analytical theory and numerical simulations. We characterized the dose-response behavior of signaling cascades by calculating the stimulus level at which a pathway responds ('pathway sensitivity') and the maximal activation level upon strong stimulation. Minimal kinase cascades with gradual dose-response behavior show strong variability, because the pathway sensitivity and the maximal activation level cannot be simultaneously invariant. Negative feedback regulation resolves this trade-off and coordinately reduces fluctuations in the pathway sensitivity and maximal activation. Feedbacks acting at different levels in the cascade control different aspects of the dose-response curve, thereby synergistically reducing the variability. We also investigated more complex, ultrasensitive signaling cascades capable of switch-like decision making, and found that these can be inherently robust to protein concentration fluctuations. We describe how the cell-to-cell variability of ultrasensitive signaling systems can be actively regulated, e.g., by altering the expression of phosphatase(s) or by feedback/feedforward loops. Our calculations reveal that slow transcriptional negative feedback loops allow for variability suppression while maintaining switch-like decision making. Taken together, we describe design principles of signaling cascades that promote robustness. Our results may explain why certain signaling cascades like the yeast pheromone pathway show switch-like decision making with little cell-to-cell variability.

Pandemic influenza preparedness: an ethical framework to guide decision-making

PubMed Central

Thompson, Alison K; Faith, Karen; Gibson, Jennifer L; Upshur, Ross EG

2006-01-01

Background Planning for the next pandemic influenza outbreak is underway in hospitals across the world. The global SARS experience has taught us that ethical frameworks to guide decision-making may help to reduce collateral damage and increase trust and solidarity within and between health care organisations. Good pandemic planning requires reflection on values because science alone cannot tell us how to prepare for a public health crisis. Discussion In this paper, we present an ethical framework for pandemic influenza planning. The ethical framework was developed with expertise from clinical, organisational and public health ethics and validated through a stakeholder engagement process. The ethical framework includes both substantive and procedural elements for ethical pandemic influenza planning. The incorporation of ethics into pandemic planning can be helped by senior hospital administrators sponsoring its use, by having stakeholders vet the framework, and by designing or identifying decision review processes. We discuss the merits and limits of an applied ethical framework for hospital decision-making, as well as the robustness of the framework. Summary The need for reflection on the ethical issues raised by the spectre of a pandemic influenza outbreak is great. Our efforts to address the normative aspects of pandemic planning in hospitals have generated interest from other hospitals and from the governmental sector. The framework will require re-evaluation and refinement and we hope that this paper will generate feedback on how to make it even more robust. PMID:17144926

Towards sustainable infrastructure management: knowledge-based service-oriented computing framework for visual analytics

NASA Astrophysics Data System (ADS)

Vatcha, Rashna; Lee, Seok-Won; Murty, Ajeet; Tolone, William; Wang, Xiaoyu; Dou, Wenwen; Chang, Remco; Ribarsky, William; Liu, Wanqiu; Chen, Shen-en; Hauser, Edd

2009-05-01

Infrastructure management (and its associated processes) is complex to understand, perform and thus, hard to make efficient and effective informed decisions. The management involves a multi-faceted operation that requires the most robust data fusion, visualization and decision making. In order to protect and build sustainable critical assets, we present our on-going multi-disciplinary large-scale project that establishes the Integrated Remote Sensing and Visualization (IRSV) system with a focus on supporting bridge structure inspection and management. This project involves specific expertise from civil engineers, computer scientists, geographers, and real-world practitioners from industry, local and federal government agencies. IRSV is being designed to accommodate the essential needs from the following aspects: 1) Better understanding and enforcement of complex inspection process that can bridge the gap between evidence gathering and decision making through the implementation of ontological knowledge engineering system; 2) Aggregation, representation and fusion of complex multi-layered heterogeneous data (i.e. infrared imaging, aerial photos and ground-mounted LIDAR etc.) with domain application knowledge to support machine understandable recommendation system; 3) Robust visualization techniques with large-scale analytical and interactive visualizations that support users' decision making; and 4) Integration of these needs through the flexible Service-oriented Architecture (SOA) framework to compose and provide services on-demand. IRSV is expected to serve as a management and data visualization tool for construction deliverable assurance and infrastructure monitoring both periodically (annually, monthly, even daily if needed) as well as after extreme events.

Systematic review of the empirical investigation of resources to support decision-making regarding BRCA1 and BRCA2 genetic testing in women with breast cancer.

PubMed

Grimmett, Chloe; Pickett, Karen; Shepherd, Jonathan; Welch, Karen; Recio-Saucedo, Alejandra; Streit, Elke; Seers, Helen; Armstrong, Anne; Cutress, Ramsey I; Evans, D Gareth; Copson, Ellen; Meiser, Bettina; Eccles, Diana; Foster, Claire

2018-05-01

Identify existing resources developed and/or evaluated empirically in the published literature designed to support women with breast cancer making decisions regarding genetic testing for BRCA1/2 mutations. Systematic review of seven electronic databases. Studies were included if they described or evaluated resources that were designed to support women with breast cancer in making a decision to have genetic counselling or testing for familial breast cancer. Outcome and process evaluations, using any type of study design, as well as articles reporting the development of decision aids, were eligible for inclusion. Total of 9 publications, describing 6 resources were identified. Resources were effective at increasing knowledge or understanding of hereditary breast cancer. Satisfaction with resources was high. There was no evidence that any resource increased distress, worry or decisional conflict. Few resources included active functionalities for example, values-based exercises, to support decision-making. Tailored resources supporting decision-making may be helpful and valued by patients and increase knowledge of hereditary breast cancer, without causing additional distress. Clinicians should provide supportive written information to patients where it is available. However, there is a need for robustly developed decision tools to support decision-making around genetic testing in women with breast cancer. Copyright © 2017 Elsevier B.V. All rights reserved.

Decision-making in rectal and colorectal cancer: systematic review and qualitative analysis of surgeons' preferences.

PubMed

Broc, Guillaume; Gana, Kamel; Denost, Quentin; Quintard, Bruno

2017-04-01

Surgeons are experiencing difficulties implementing recommendations not only owing to incomplete, confusing or conflicting information but also to the increasing involvement of patients in decisions relating to their health. This study sought to establish which common factors including heuristic factors guide surgeons' decision-making in colon and rectal cancers. We conducted a systematic literature review of surgeons' decision-making factors related to colon and rectal cancer treatment. Eleven of 349 identified publications were eligible for data analyses. Using the IRaMuTeQ (Interface of R for the Multidimensional Analyses of Texts and Questionnaire), we carried out a qualitative analysis of the significant factors collected in the studies reviewed. Several validation procedures were applied to control the robustness of the findings. Five categories of factors (i.e. patient, surgeon, treatment, tumor and organizational cues) were found to influence surgeons' decision-making. Specifically, all decision criteria including biomedical (e.g. tumor information) and heuristic (e.g. surgeons' dispositional factors) criteria converged towards the factor 'age of patient' in the similarity analysis. In the light of the results, we propose an explanatory model showing the impact of heuristic criteria on medical issues (i.e. diagnosis, prognosis, treatment features, etc.) and thus on decision-making. Finally, the psychosocial complexity involved in decision-making is discussed and a medico-psycho-social grid for use in multidisciplinary meetings is proposed.

Subchronic administration of atomoxetine causes an enduring reduction in context-induced relapse to cocaine seeking without affecting impulsive decision making.

PubMed

Broos, Nienke; Loonstra, Rhianne; van Mourik, Yvar; Schetters, Dustin; Schoffelmeer, Anton N M; Pattij, Tommy; De Vries, Taco J

2015-07-01

Previous work has established a robust relationship between impulsivity and addiction, and revealed that impulsive decision making predisposes the vulnerability to cocaine-seeking behavior in rats. An important next step is to assess whether elevated relapse vulnerability can be treated via the reduction of impulsive decision making. Therefore, this study explored whether subchronic atomoxetine treatment can reduce relapse vulnerability by reducing impulsive decision making. Rats were trained in the delayed reward task and were subjected to 3 weeks of cocaine self-administration. Following drug self-administration, animals were divided to different experimental groups and received the noradrenaline transporter inhibitor and attention-deficit/hyperactivity disorder drug atomoxetine or vehicle subchronically for 20 days. On days 1 and 10 after treatment cessation, a context-induced reinstatement test was performed. Throughout the entire experiment, changes in impulsive decision making were continuously monitored. Subchronic treatment with atomoxetine reduced context-induced reinstatement both 1 and 10 days after treatment cessation, only in animals receiving no extinction training. Interestingly, neither subchronic nor acute atomoxetine treatments affected impulsive decision making. Our data indicate that the enduring reduction in relapse sensitivity by atomoxetine occurred independent of a reduction in impulsive decision making. Nonetheless, repeated atomoxetine administration seems a promising pharmacotherapeutical strategy to prevent relapse to cocaine seeking in abstinent drug-dependent subjects. © 2014 Society for the Study of Addiction.

Network-centric decision architecture for financial or 1/f data models

NASA Astrophysics Data System (ADS)

Jaenisch, Holger M.; Handley, James W.; Massey, Stoney; Case, Carl T.; Songy, Claude G.

2002-12-01

This paper presents a decision architecture algorithm for training neural equation based networks to make autonomous multi-goal oriented, multi-class decisions. These architectures make decisions based on their individual goals and draw from the same network centric feature set. Traditionally, these architectures are comprised of neural networks that offer marginal performance due to lack of convergence of the training set. We present an approach for autonomously extracting sample points as I/O exemplars for generation of multi-branch, multi-node decision architectures populated by adaptively derived neural equations. To test the robustness of this architecture, open source data sets in the form of financial time series were used, requiring a three-class decision space analogous to the lethal, non-lethal, and clutter discrimination problem. This algorithm and the results of its application are presented here.

An engineering approach to modelling, decision support and control for sustainable systems.

PubMed

Day, W; Audsley, E; Frost, A R

2008-02-12

Engineering research and development contributes to the advance of sustainable agriculture both through innovative methods to manage and control processes, and through quantitative understanding of the operation of practical agricultural systems using decision models. This paper describes how an engineering approach, drawing on mathematical models of systems and processes, contributes new methods that support decision making at all levels from strategy and planning to tactics and real-time control. The ability to describe the system or process by a simple and robust mathematical model is critical, and the outputs range from guidance to policy makers on strategic decisions relating to land use, through intelligent decision support to farmers and on to real-time engineering control of specific processes. Precision in decision making leads to decreased use of inputs, less environmental emissions and enhanced profitability-all essential to sustainable systems.

Staged decision making based on probabilistic forecasting

NASA Astrophysics Data System (ADS)

Booister, Nikéh; Verkade, Jan; Werner, Micha; Cranston, Michael; Cumiskey, Lydia; Zevenbergen, Chris

2016-04-01

Flood forecasting systems reduce, but cannot eliminate uncertainty about the future. Probabilistic forecasts explicitly show that uncertainty remains. However, as - compared to deterministic forecasts - a dimension is added ('probability' or 'likelihood'), with this added dimension decision making is made slightly more complicated. A technique of decision support is the cost-loss approach, which defines whether or not to issue a warning or implement mitigation measures (risk-based method). With the cost-loss method a warning will be issued when the ratio of the response costs to the damage reduction is less than or equal to the probability of the possible flood event. This cost-loss method is not widely used, because it motivates based on only economic values and is a technique that is relatively static (no reasoning, yes/no decision). Nevertheless it has high potential to improve risk-based decision making based on probabilistic flood forecasting because there are no other methods known that deal with probabilities in decision making. The main aim of this research was to explore the ways of making decision making based on probabilities with the cost-loss method better applicable in practice. The exploration began by identifying other situations in which decisions were taken based on uncertain forecasts or predictions. These cases spanned a range of degrees of uncertainty: from known uncertainty to deep uncertainty. Based on the types of uncertainties, concepts of dealing with situations and responses were analysed and possible applicable concepts where chosen. Out of this analysis the concepts of flexibility and robustness appeared to be fitting to the existing method. Instead of taking big decisions with bigger consequences at once, the idea is that actions and decisions are cut-up into smaller pieces and finally the decision to implement is made based on economic costs of decisions and measures and the reduced effect of flooding. The more lead-time there is in flood event management, the more damage can be reduced. And with decisions based on probabilistic forecasts, partial decisions can be made earlier in time (with a lower probability) and can be scaled up or down later in time when there is more certainty; whether the event takes place or not. Partial decisions are often more cheap, or shorten the final mitigation-time at the moment when there is more certainty. The proposed method is tested on Stonehaven, on the Carron River in Scotland. Decisions to implement demountable defences in the town are currently made based on a very short lead-time due to the absence of certainty. Application showed that staged decision making is possible and gives the decision maker more time to respond to a situation. The decision maker is able to take a lower regret decision with higher uncertainty and less related negative consequences. Although it is not possible to quantify intangible effects, it is part of the analysis to reduce these effects. Above all, the proposed approach has shown to be a possible improvement in economic terms and opens up possibilities of more flexible and robust decision making.

Optimization and resilience in natural resources management

USGS Publications Warehouse

Williams, Byron K.; Johnson, Fred A.

2015-01-01

We consider the putative tradeoff between optimization and resilience in the management of natural resources, using a framework that incorporates different sources of uncertainty that are common in natural resources management. We address one-time decisions, and then expand the decision context to the more complex problem of iterative decision making. For both cases we focus on two key sources of uncertainty: partial observability of system state and uncertainty as to system dynamics. Optimal management strategies will vary considerably depending on the timeframe being considered and the amount and quality of information that is available to characterize system features and project the consequences of potential decisions. But in all cases an optimal decision making framework, if properly identified and focused, can be useful in recognizing sound decisions. We argue that under the conditions of deep uncertainty that characterize many resource systems, an optimal decision process that focuses on robustness does not automatically induce a loss of resilience.

Surrogate decision making: do we have to trade off accuracy and procedural satisfaction?

PubMed

Frey, Renato; Hertwig, Ralph; Herzog, Stefan M

2014-02-01

Making surrogate decisions on behalf of incapacitated patients can raise difficult questions for relatives, physicians, and society. Previous research has focused on the accuracy of surrogate decisions (i.e., the proportion of correctly inferred preferences). Less attention has been paid to the procedural satisfaction that patients' surrogates and patients attribute to specific approaches to making surrogate decisions. The objective was to investigate hypothetical patients' and surrogates' procedural satisfaction with specific approaches to making surrogate decisions and whether implementing these preferences would lead to tradeoffs between procedural satisfaction and accuracy. Study 1 investigated procedural satisfaction by assigning participants (618 in a mixed-age but relatively young online sample and 50 in an older offline sample) to the roles of hypothetical surrogates or patients. Study 2 (involving 64 real multigenerational families with a total of 253 participants) investigated accuracy using 24 medical scenarios. Hypothetical patients and surrogates had closely aligned preferences: Procedural satisfaction was highest with a patient-designated surrogate, followed by shared surrogate decision-making approaches and legally assigned surrogates. These approaches did not differ substantially in accuracy. Limitations are that participants' preferences regarding existing and novel approaches to making surrogate decisions can only be elicited under hypothetical conditions. Next to decision making by patient-designated surrogates, shared surrogate decision making is the preferred approach among patients and surrogates alike. This approach appears to impose no tradeoff between procedural satisfaction and accuracy. Therefore, shared decision making should be further studied in representative samples of the general population, and if people's preferences prove to be robust, they deserve to be weighted more strongly in legal frameworks in addition to patient-designated surrogates.

A robotic approach to understanding the role and the mechanism of vicarious trial-and-error in a T-maze task.

PubMed

Matsuda, Eiko; Hubert, Julien; Ikegami, Takashi

2014-01-01

Vicarious trial-and-error (VTE) is a behavior observed in rat experiments that seems to suggest self-conflict. This behavior is seen mainly when the rats are uncertain about making a decision. The presence of VTE is regarded as an indicator of a deliberative decision-making process, that is, searching, predicting, and evaluating outcomes. This process is slower than automated decision-making processes, such as reflex or habituation, but it allows for flexible and ongoing control of behavior. In this study, we propose for the first time a robotic model of VTE to see if VTE can emerge just from a body-environment interaction and to show the underlying mechanism responsible for the observation of VTE and the advantages provided by it. We tried several robots with different parameters, and we have found that they showed three different types of VTE: high numbers of VTE at the beginning of learning, decreasing numbers afterward (similar VTE pattern to experiments with rats), low during the whole learning period, and high numbers all the time. Therefore, we were able to reproduce the phenomenon of VTE in a model robot using only a simple dynamical neural network with Hebbian learning, which suggests that VTE is an emergent property of a plastic and embodied neural network. From a comparison of the three types of VTE, we demonstrated that 1) VTE is associated with chaotic activity of neurons in our model and 2) VTE-showing robots were robust to environmental perturbations. We suggest that the instability of neuronal activity found in VTE allows ongoing learning to rebuild its strategy continuously, which creates robust behavior. Based on these results, we suggest that VTE is caused by a similar mechanism in biology and leads to robust decision making in an analogous way.

Developing a Robust Strategy for Implementing a Water Resources Master Plan in Lima, Peru

NASA Astrophysics Data System (ADS)

Kalra, N.; Groves, D.; Bonzanigo, L.; Molina-Perez, E.

2015-12-01

Lima, the capital of Peru, faces significant water stress. It is the fifth largest metropolitan area in Latin America, and the second largest desert city in the world. The city has developed a Master Plan of major investment projects to improve water reliability until 2040. Yet key questions remain. Is the Master Plan sufficient for ensuring reliability in the face of deeply uncertain future climate change and demand? How do uncertain budget and project feasibility conditions shape Lima's options? How should the investments in the plan be prioritized, and can some be delayed? Lima is not alone in facing these planning challenges. Governments invest billions of dollars annually in long-term projects. Yet deep uncertainties pose formidable challenges to making near-term decisions that make long-term sense. The World Bank has spearheaded a community of practice on methods for Decision Making Under Deep Uncertainty (DMU). This pilot project in Peru is the first in-depth application of DMU techniques to water supply planning in a developing country. It builds on prior analysis done in New York, California, and for the Colorado River, yet shows how these methods can be applied in regions which do not have as advanced data or tools available. The project combines three methods in particular -- Robust Decision Making, Decision Scaling, and Adaptive Pathways -- to help Lima implement its Master Plan in a way that is robust, no-regret, and adaptive. It was done in close partnership with SEDAPAL, the water utility company in Lima, and in coordination with other national WRM and meteorological agencies. This talk will: Present the planning challenges Lima and other cities face, including climate change Describe DMU methodologies and how they were applied in collaboration with SEDAPAL Summarize recommendations for achieving long-term water reliability in Lima Suggest how these methodologies can benefit other investment projects in developing countries.

Analytical redundancy and the design of robust failure detection systems

NASA Technical Reports Server (NTRS)

Chow, E. Y.; Willsky, A. S.

1984-01-01

The Failure Detection and Identification (FDI) process is viewed as consisting of two stages: residual generation and decision making. It is argued that a robust FDI system can be achieved by designing a robust residual generation process. Analytical redundancy, the basis for residual generation, is characterized in terms of a parity space. Using the concept of parity relations, residuals can be generated in a number of ways and the design of a robust residual generation process can be formulated as a minimax optimization problem. An example is included to illustrate this design methodology. Previously announcedd in STAR as N83-20653

Intuition and Moral Decision-Making - The Effect of Time Pressure and Cognitive Load on Moral Judgment and Altruistic Behavior.

PubMed

Tinghög, Gustav; Andersson, David; Bonn, Caroline; Johannesson, Magnus; Kirchler, Michael; Koppel, Lina; Västfjäll, Daniel

2016-01-01

Do individuals intuitively favor certain moral actions over others? This study explores the role of intuitive thinking-induced by time pressure and cognitive load-in moral judgment and behavior. We conduct experiments in three different countries (Sweden, Austria, and the United States) involving over 1,400 subjects. All subjects responded to four trolley type dilemmas and four dictator games involving different charitable causes. Decisions were made under time pressure/time delay or while experiencing cognitive load or control. Overall we find converging evidence that intuitive states do not influence moral decisions. Neither time-pressure nor cognitive load had any effect on moral judgments or altruistic behavior. Thus we find no supporting evidence for the claim that intuitive moral judgments and dictator game giving differ from more reflectively taken decisions. Across all samples and decision tasks men were more likely to make utilitarian moral judgments and act selfishly compared to women, providing further evidence that there are robust gender differences in moral decision-making. However, there were no significant interactions between gender and the treatment manipulations of intuitive versus reflective decision-making.

Intuition and Moral Decision-Making – The Effect of Time Pressure and Cognitive Load on Moral Judgment and Altruistic Behavior

PubMed Central

Bonn, Caroline; Johannesson, Magnus; Kirchler, Michael; Koppel, Lina; Västfjäll, Daniel

2016-01-01

Do individuals intuitively favor certain moral actions over others? This study explores the role of intuitive thinking—induced by time pressure and cognitive load—in moral judgment and behavior. We conduct experiments in three different countries (Sweden, Austria, and the United States) involving over 1,400 subjects. All subjects responded to four trolley type dilemmas and four dictator games involving different charitable causes. Decisions were made under time pressure/time delay or while experiencing cognitive load or control. Overall we find converging evidence that intuitive states do not influence moral decisions. Neither time-pressure nor cognitive load had any effect on moral judgments or altruistic behavior. Thus we find no supporting evidence for the claim that intuitive moral judgments and dictator game giving differ from more reflectively taken decisions. Across all samples and decision tasks men were more likely to make utilitarian moral judgments and act selfishly compared to women, providing further evidence that there are robust gender differences in moral decision-making. However, there were no significant interactions between gender and the treatment manipulations of intuitive versus reflective decision-making. PMID:27783704

Robustness of risk maps and survey networks to knowledge gaps about a new invasive pest

Treesearch

Denys Yemshanov; Frank H. Koch; Yakov Ben-Haim; William D. Smith

2010-01-01

In pest risk assessment it is frequently necessary to make management decisions regarding emerging threats under severe uncertainty. Although risk maps provide useful decision support for invasive alien species, they rarely address knowledge gaps associated with the underlying risk model or how they may change the risk estimates. Failure to recognize uncertainty leads...

The Mental Capacity Act: 'Best interests'-a review of the literature.

PubMed

Marshall, Helen; Sprung, Sally

2017-08-02

The Mental Capacity Act (MCA) is statutory legislation introduced in 2007 in order to provide a consistent, robust framework with the aim to protect and empower people to make decisions themselves. However, an assessment as per the MCA may demonstrate that a person is lacking mental capacity and therefore unable to make an autonomous decision at the time it needs to be made. In this case, a 'best interests' decision may be made on their behalf, ensuring their wishes and beliefs are at the centre of the decision-making process. When making a best interests decision, a health practitioner must follow the guidance as set out in the MCA legislation to ensure fair and consistent approaches to safeguard and provide assurance that the outcome is truly the best decision for the individual. This review of the literature supports the findings of a 2014 post-legislative review by the House of Lords, which concluded the principles of the MCA are not sufficiently embedded into the practice of all health practitioners, due to a lack of knowledge, awareness and understanding. However, the evidence base also appreciates making a decision on behalf of another person can be a stressful, complex and intricate process when further support may be required from the wider multidisciplinary team, including potentially seeking legal advice.

Eye tracking measures of uncertainty during perceptual decision making.

PubMed

Brunyé, Tad T; Gardony, Aaron L

2017-10-01

Perceptual decision making involves gathering and interpreting sensory information to effectively categorize the world and inform behavior. For instance, a radiologist distinguishing the presence versus absence of a tumor, or a luggage screener categorizing objects as threatening or non-threatening. In many cases, sensory information is not sufficient to reliably disambiguate the nature of a stimulus, and resulting decisions are done under conditions of uncertainty. The present study asked whether several oculomotor metrics might prove sensitive to transient states of uncertainty during perceptual decision making. Participants viewed images with varying visual clarity and were asked to categorize them as faces or houses, and rate the certainty of their decisions, while we used eye tracking to monitor fixations, saccades, blinks, and pupil diameter. Results demonstrated that decision certainty influenced several oculomotor variables, including fixation frequency and duration, the frequency, peak velocity, and amplitude of saccades, and phasic pupil diameter. Whereas most measures tended to change linearly along with decision certainty, pupil diameter revealed more nuanced and dynamic information about the time course of perceptual decision making. Together, results demonstrate robust alterations in eye movement behavior as a function of decision certainty and attention demands, and suggest that monitoring oculomotor variables during applied task performance may prove valuable for identifying and remediating transient states of uncertainty. Published by Elsevier B.V.

Neural correlates of decision making on a gambling task.

PubMed

Carlson, Stephanie M; Zayas, Vivian; Guthormsen, Amy

2009-01-01

Individual differences in affective decision making were examined by recording event-related potentials (ERPs) while 74 typically developing 8-year-olds (38 boys, 36 girls) completed a 4-choice gambling task (Hungry Donkey Task; E. A. Crone & M. W. van der Molen, 2004). ERP results indicated: (a) a robust P300 component in response to feedback (punishment vs. reward outcomes), (b) anticipation effects (stimulus-preceding negativity) prior to outcomes presented on frequent (vs. infrequent) punishment choices, (c) anticipation effects prior to selections associated with short and long-term losses (vs. gains), and (d) individual differences in ERP components were significantly correlated with behavioral performance and verbal ability. These findings suggest that neurophysiological responses may be an index of children's trait-based and/or developmental level of decision-making skills in affective-motivational situations.

21st century neurobehavioral theories of decision making in addiction: Review and evaluation.

PubMed

Bickel, Warren K; Mellis, Alexandra M; Snider, Sarah E; Athamneh, Liqa N; Stein, Jeffrey S; Pope, Derek A

2018-01-01

This review critically examines neurobehavioral theoretical developments in decision making in addiction in the 21st century. We specifically compare each theory reviewed to seven benchmarks of theoretical robustness, based on their ability to address: why some commodities are addictive; developmental trends in addiction; addiction-related anhedonia; self-defeating patterns of behavior in addiction; why addiction co-occurs with other unhealthy behaviors; and, finally, means for the repair of addiction. We have included only self-contained theories or hypotheses which have been developed or extended in the 21st century to address decision making in addiction. We thus review seven distinct theories of decision making in addiction: learning theories, incentive-sensitization theory, dopamine imbalance and systems models, opponent process theory, strength models of self-control failure, the competing neurobehavioral decision systems theory, and the triadic systems theory of addiction. Finally, we have directly compared the performance of each of these theories based on the aforementioned benchmarks, and highlighted key points at which several theories have coalesced. Copyright © 2017 Elsevier Inc. All rights reserved.

Acute stress affects risk taking but not ambiguity aversion

PubMed Central

Buckert, Magdalena; Schwieren, Christiane; Kudielka, Brigitte M.; Fiebach, Christian J.

2014-01-01

Economic decisions are often made in stressful situations (e.g., at the trading floor), but the effects of stress on economic decision making have not been systematically investigated so far. The present study examines how acute stress influences economic decision making under uncertainty (risk and ambiguity) using financially incentivized lotteries. We varied the domain of decision making as well as the expected value of the risky prospect. Importantly, no feedback was provided to investigate risk taking and ambiguity aversion independent from learning processes. In a sample of 75 healthy young participants, 55 of whom underwent a stress induction protocol (Trier Social Stress Test for Groups), we observed more risk seeking for gains. This effect was restricted to a subgroup of participants that showed a robust cortisol response to acute stress (n = 26). Gambling under ambiguity, in contrast to gambling under risk, was not influenced by the cortisol response to stress. These results show that acute psychosocial stress affects economic decision making under risk, independent of learning processes. Our results further point to the importance of cortisol as a mediator of this effect. PMID:24834024

Acute stress affects risk taking but not ambiguity aversion.

PubMed

Buckert, Magdalena; Schwieren, Christiane; Kudielka, Brigitte M; Fiebach, Christian J

2014-01-01

Economic decisions are often made in stressful situations (e.g., at the trading floor), but the effects of stress on economic decision making have not been systematically investigated so far. The present study examines how acute stress influences economic decision making under uncertainty (risk and ambiguity) using financially incentivized lotteries. We varied the domain of decision making as well as the expected value of the risky prospect. Importantly, no feedback was provided to investigate risk taking and ambiguity aversion independent from learning processes. In a sample of 75 healthy young participants, 55 of whom underwent a stress induction protocol (Trier Social Stress Test for Groups), we observed more risk seeking for gains. This effect was restricted to a subgroup of participants that showed a robust cortisol response to acute stress (n = 26). Gambling under ambiguity, in contrast to gambling under risk, was not influenced by the cortisol response to stress. These results show that acute psychosocial stress affects economic decision making under risk, independent of learning processes. Our results further point to the importance of cortisol as a mediator of this effect.

Application of stakeholder-based and modelling approaches for supporting robust adaptation decision making under future climatic uncertainty and changing urban-agricultural water demand

NASA Astrophysics Data System (ADS)

Bhave, Ajay; Dessai, Suraje; Conway, Declan; Stainforth, David

2016-04-01

Deep uncertainty in future climate change and socio-economic conditions necessitates the use of assess-risk-of-policy approaches over predict-then-act approaches for adaptation decision making. Robust Decision Making (RDM) approaches embody this principle and help evaluate the ability of adaptation options to satisfy stakeholder preferences under wide-ranging future conditions. This study involves the simultaneous application of two RDM approaches; qualitative and quantitative, in the Cauvery River Basin in Karnataka (population ~23 million), India. The study aims to (a) determine robust water resources adaptation options for the 2030s and 2050s and (b) compare the usefulness of a qualitative stakeholder-driven approach with a quantitative modelling approach. For developing a large set of future scenarios a combination of climate narratives and socio-economic narratives was used. Using structured expert elicitation with a group of climate experts in the Indian Summer Monsoon, climatic narratives were developed. Socio-economic narratives were developed to reflect potential future urban and agricultural water demand. In the qualitative RDM approach, a stakeholder workshop helped elicit key vulnerabilities, water resources adaptation options and performance criteria for evaluating options. During a second workshop, stakeholders discussed and evaluated adaptation options against the performance criteria for a large number of scenarios of climatic and socio-economic change in the basin. In the quantitative RDM approach, a Water Evaluation And Planning (WEAP) model was forced by precipitation and evapotranspiration data, coherent with the climatic narratives, together with water demand data based on socio-economic narratives. We find that compared to business-as-usual conditions options addressing urban water demand satisfy performance criteria across scenarios and provide co-benefits like energy savings and reduction in groundwater depletion, while options reducing agricultural water demand significantly affect downstream water availability. Water demand options demonstrate potential to improve environmental flow conditions and satisfy legal water supply requirements for downstream riparian states. On the other hand, currently planned large scale infrastructural projects demonstrate reduced value in certain scenarios, illustrating the impacts of lock-in effects of large scale infrastructure. From a methodological perspective, we find that while the stakeholder-driven approach revealed robust options in a resource-light manner and helped initiate much needed interaction amongst stakeholders, the modelling approach provides complementary quantitative information. The study reveals robust adaptation options for this important basin and provides a strong methodological basis for carrying out future studies that support adaptation decision making.

Evolutionary mechanics: new engineering principles for the emergence of flexibility in a dynamic and uncertain world.

PubMed

Whitacre, James M; Rohlfshagen, Philipp; Bender, Axel; Yao, Xin

2012-09-01

Engineered systems are designed to deftly operate under predetermined conditions yet are notoriously fragile when unexpected perturbations arise. In contrast, biological systems operate in a highly flexible manner; learn quickly adequate responses to novel conditions, and evolve new routines and traits to remain competitive under persistent environmental change. A recent theory on the origins of biological flexibility has proposed that degeneracy-the existence of multi-functional components with partially overlapping functions-is a primary determinant of the robustness and adaptability found in evolved systems. While degeneracy's contribution to biological flexibility is well documented, there has been little investigation of degeneracy design principles for achieving flexibility in systems engineering. Actually, the conditions that can lead to degeneracy are routinely eliminated in engineering design. With the planning of transportation vehicle fleets taken as a case study, this article reports evidence that degeneracy improves the robustness and adaptability of a simulated fleet towards unpredicted changes in task requirements without incurring costs to fleet efficiency. We find that degeneracy supports faster rates of design adaptation and ultimately leads to better fleet designs. In investigating the limitations of degeneracy as a design principle, we consider decision-making difficulties that arise from degeneracy's influence on fleet complexity. While global decision-making becomes more challenging, we also find degeneracy accommodates rapid distributed decision-making leading to (near-optimal) robust system performance. Given the range of conditions where favorable short-term and long-term performance outcomes are observed, we propose that degeneracy may fundamentally alter the propensity for adaptation and is useful within different engineering and planning contexts.

Role of the anesthesiologist in the wider governance of healthcare and health economics.

PubMed

Martin, Janet; Cheng, Davy

2013-09-01

Healthcare resources will always be limited, and as a result, difficult decisions must be made about how to allocate limited resources across unlimited demands in order to maximize health gains per resource expended. Governments and hospitals now in severe financial deficits recognize that reengagement of physicians is central to their ability to contain the runaway healthcare costs. Health economic analysis provides tools and techniques to assess which investments in healthcare provide good value for money vs which options should be forgone. Robust decision-making in healthcare requires objective consideration of evidence in order to balance clinical and economic benefits vs risks. Surveys of the literature reveal very few economic analyses related to anesthesia and perioperative medicine despite increasing recognition of the need. Now is an opportune time for anesthesiologists to become familiar with the tools and methodologies of health economics in order to facilitate and lead robust decision-making in quality-based procedures. For most technologies used in anesthesia and perioperative medicine, the responsibility to determine cost-effectiveness falls to those tasked with the governance and stewardship of limited resources for unlimited demands using best evidence plus economics at the local, regional, and national levels. Applicable cost-effectiveness, cost-utility, and cost-benefits in health economics are reviewed in this article with clinical examples in anesthesia. Anesthesiologists can make a difference in the wider governance of healthcare and health economics if we advance our knowledge and skills beyond the technical to address the "other" dimensions of decision-making--most notably, the economic aspects in a value-based healthcare system.

A conceptual evolutionary aseismic decision support framework for hospitals

NASA Astrophysics Data System (ADS)

Hu, Yufeng; Dargush, Gary F.; Shao, Xiaoyun

2012-12-01

In this paper, aconceptual evolutionary framework for aseismic decision support for hospitalsthat attempts to integrate a range of engineering and sociotechnical models is presented. Genetic algorithms are applied to find the optimal decision sets. A case study is completed to demonstrate how the frameworkmay applytoa specific hospital.The simulations show that the proposed evolutionary decision support framework is able to discover robust policy sets in either uncertain or fixed environments. The framework also qualitatively identifies some of the characteristicbehavior of the critical care organization. Thus, by utilizing the proposedframework, the decision makers are able to make more informed decisions, especially toenhance the seismic safety of the hospitals.

Learning consensus in adversarial environments

NASA Astrophysics Data System (ADS)

Vamvoudakis, Kyriakos G.; García Carrillo, Luis R.; Hespanha, João. P.

2013-05-01

This work presents a game theory-based consensus problem for leaderless multi-agent systems in the presence of adversarial inputs that are introducing disturbance to the dynamics. Given the presence of enemy components and the possibility of malicious cyber attacks compromising the security of networked teams, a position agreement must be reached by the networked mobile team based on environmental changes. The problem is addressed under a distributed decision making framework that is robust to possible cyber attacks, which has an advantage over centralized decision making in the sense that a decision maker is not required to access information from all the other decision makers. The proposed framework derives three tuning laws for every agent; one associated with the cost, one associated with the controller, and one with the adversarial input.

More heads choose better than one: Group decision making can eliminate probability matching.

PubMed

Schulze, Christin; Newell, Ben R

2016-06-01

Probability matching is a robust and common failure to adhere to normative predictions in sequential decision making. We show that this choice anomaly is nearly eradicated by gathering individual decision makers into small groups and asking the groups to decide. The group choice advantage emerged both when participants generated responses for an entire sequence of choices without outcome feedback (Exp. 1a) and when participants made trial-by-trial predictions with outcome feedback after each decision (Exp. 1b). We show that the dramatic improvement observed in group settings stands in stark contrast to a complete lack of effective solitary deliberation. These findings suggest a crucial role of group discussion in alleviating the impact of hasty intuitive responses in tasks better suited to careful deliberation.

Will current probabilistic climate change information, as such, improve adaptation?

NASA Astrophysics Data System (ADS)

Lopez, A.; Smith, L. A.

2012-04-01

Probabilistic climate scenarios are currently being provided to end users, to employ as probabilities in adaptation decision making, with the explicit suggestion that they quantify the impacts of climate change relevant to a variety of sectors. These "probabilities" are, however, rather sensitive to the assumptions in, and the structure of the modelling approaches used to generate them. It is often argued that stakeholders require probabilistic climate change information to adequately evaluate and plan adaptation pathways. On the other hand, some circumstantial evidence suggests that on the ground decision making rarely uses well defined probability distributions of climate change as inputs. Nevertheless it is within this context of probability distributions of climate change that we discuss possible drawbacks of supplying information that, while presented as robust and decision relevant, , is in fact unlikely to be so due to known flaws both in the underlying models and in the methodology used to "account for" those known flaws. How might one use a probability forecast that is expected to change in the future, not due to a refinement in our information but due to fundamental flaws in its construction? What then are the alternatives? While the answer will depend on the context of the problem at hand, a good approach will be strongly informed by the timescale of the given planning decision, and the consideration of all the non-climatic factors that have to be taken into account in the corresponding risk assessment. Using a water resources system as an example, we illustrate an alternative approach to deal with these challenges and make robust adaptation decisions today.

Personalized health care and health information technology policy: an exploratory analysis.

PubMed

Wald, Jonathan S; Shapiro, Michael

2013-01-01

Personalized healthcare (PHC) is envisioned to enhance clinical practice decision-making using new genome-driven knowledge that tailors diagnosis, treatment, and prevention to the individual patient. In 2012, we conducted a focused environmental scan and informal interviews with fifteen experts to anticipate how PHC might impact health Information Technology (IT) policy in the United States. Findings indicatedthat PHC has a variable impact on current clinical practice, creates complex questions for providers, patients, and policy-makers, and will require a robust health IT infrastructure with advanced data architecture, clinical decision support, provider workflow tools, and re-use of clinical data for research. A number of health IT challenge areas were identified, along with five policy areas including: interoperable clinical decision support, standards for patient values and preferences, patient engagement, data transparency, and robust privacy and security.

Comparison as a Universal Learning Action

ERIC Educational Resources Information Center

Merkulova, T. V.

2016-01-01

This article explores "comparison" as a universal metasubject learning action, a key curricular element envisaged by the Russian Federal State Educational Standards. Representing the modern learner's fundamental pragmatic skill embedding such core capacities as information processing, critical thinking, robust decision-making, and…

Building an Evidence-Driven Child Welfare Workforce: A University–Agency Partnership

PubMed Central

Lery, Bridgette; Wiegmann, Wendy; Berrick, Jill Duerr

2016-01-01

The federal government increasingly expects child welfare systems to be more responsive to the needs of their local populations, connect strategies to results, and use continuous quality improvement (CQI) to accomplish these goals. A method for improving decision making, CQI relies on an inflow of high-quality data, up-to-date research evidence, and a robust organizational structure and climate that supports the deliberate use of evidence for decision making. This article describes an effort to build and support these essential system components through one public-private child welfare agency–university partnership. PMID:27429534

Consistency in decision making by research ethics committees: a controlled comparison.

PubMed

Angell, E; Sutton, A J; Windridge, K; Dixon-Woods, M

2006-11-01

There has been longstanding interest in the consistency of decisions made by research ethics committees (RECs) in the UK, but most of the evidence has come from single studies submitted to multiple committees. A systematic comparison was carried out of the decisions made on 18 purposively selected applications, each of which was reviewed independently by three different RECs in a single strategic health authority. Decisions on 11 applications were consistent, but disparities were found among RECs on decisions on seven applications. An analysis of the agreement between decisions of RECs yielded an overall measure of agreement of kappa = 0.286 (95% confidence interval -0.06 to 0.73), indicating a level of agreement that, although probably better than chance, may be described as "slight". The small sample size limits the robustness of these findings. Further research on reasons for inconsistencies in decision making between RECs, and on the importance of such inconsistencies for a range of arguments, is needed.

Maternal psychological distress and child decision-making.

PubMed

Flouri, Eirini; Ioakeimidi, Sofia; Midouhas, Emily; Ploubidis, George B

2017-08-15

There is much research to suggest that maternal psychological distress is associated with many adverse outcomes in children. This study examined, for the first time, if it is related to children's affective decision-making. Using data from 12,080 families of the Millennium Cohort Study, we modelled the effect of trajectories of maternal psychological distress in early-to-middle childhood (3-11 years) on child affective decision-making, measured with a gambling task at age 11. Latent class analysis showed four longitudinal types of maternal psychological distress (chronically high, consistently low, moderate-accelerating and moderate-decelerating). Maternal distress typology predicted decision-making but only in girls. Specifically, compared to girls growing up in families with never-distressed mothers, those exposed to chronically high maternal psychological distress showed more risk-taking, bet more and exhibited poorer risk-adjustment, even after correction for confounding. Most of these effects on girls' decision-making were not robust to additional controls for concurrent internalising and externalising problems, but chronically high maternal psychological distress was associated positively with risk-taking even after this adjustment. Importantly, this association was similar for those who had reached puberty and those who had not. Given the study design, causality cannot be inferred. Therefore, we cannot propose that treating chronic maternal psychological distress will reduce decision-making pathology in young females. Our study suggests that young daughters of chronically distressed mothers tend to be particularly reckless decision-makers. Copyright © 2017. Published by Elsevier B.V.

Robust CO2 Injection: Application of Bayesian-Information-Gap Decision Theory

NASA Astrophysics Data System (ADS)

Grasinger, M.; O'Malley, D.; Vesselinov, V. V.; Karra, S.

2015-12-01

Carbon capture and sequestration has the potential to reduce greenhouse gasemissions. However, care must be taken when choosing a site for CO2 seques-tration to ensure that the CO2 remains sequestered for many years, and thatthe environment is not harmed in any way. Making a rational decision be-tween potential sites for sequestration is not without its challenges because, asin the case of many environmental and subsurface problems, there is a lot ofuncertainty that exists. A method for making decisions under various typesand severities of uncertainty, Bayesian-Information-Gap Decision Theory (BIGDT), is presented. BIG DT was coupled with a numerical model for CO2 wellinjection and the resulting framework was then applied to a problem of selectingbetween two potential sites for CO2 sequestration. The results of the analysisare presented, followed by a discussion of the decision process.

Training for Aviation Decision Making: The Naturalistic Decision Making Perspective

NASA Technical Reports Server (NTRS)

Orasanu, Judith; Shafto, Michael G. (Technical Monitor)

1995-01-01

This paper describes the implications of a naturalistic decision making (NDM) perspective for training air crews to make flight-related decisions. The implications are based on two types of analyses: (a) identification of distinctive features that serve as a basis for classifying a diverse set of decision events actually encountered by flight crews, and (b) performance strategies that distinguished more from less effective crews flying full-mission simulators, as well as performance analyses from NTSB accident investigations. Six training recommendations are offered: (1) Because of the diversity of decision situations, crews need to be aware that different strategies may be appropriate for different problems; (2) Given that situation assessment is essential to making a good decision, it is important to train specific content knowledge needed to recognize critical conditions, to assess risks and available time, and to develop strategies to verify or diagnose the problem; (3) Tendencies to oversimplify problems may be overcome by training to evaluate options in terms of goals, constraints, consequences, and prevailing conditions; (4) In order to provide the time to gather information and consider options, it is essential to manage the situation, which includes managing crew workload, prioritizing tasks, contingency planning, buying time (e.g., requesting holding or vectors), and using low workload periods to prepare for high workload; (5) Evaluating resource requirements ("What do I need?") and capabilities ("'What do I have?" ) are essential to making good decisions. Using resources to meet requirements may involve the cabin crew, ATC, dispatchers, and maintenance personnel; (6) Given that decisions must often be made under high risk, time pressure, and workload, train under realistic flight conditions to promote the development of robust decision skills.

Patterns of out-of-home placement decision-making in child welfare.

PubMed

Chor, Ka Ho Brian; McClelland, Gary M; Weiner, Dana A; Jordan, Neil; Lyons, John S

2013-10-01

Out-of-home placement decision-making in child welfare is founded on the best interest of the child in the least restrictive setting. After a child is removed from home, however, little is known about the mechanism of placement decision-making. This study aims to systematically examine the patterns of out-of-home placement decisions made in a state's child welfare system by comparing two models of placement decision-making: a multidisciplinary team decision-making model and a clinically based decision support algorithm. Based on records of 7816 placement decisions representing 6096 children over a 4-year period, hierarchical log-linear modeling characterized concordance or agreement, and discordance or disagreement when comparing the two models and accounting for age-appropriate placement options. Children aged below 16 had an overall concordance rate of 55.7%, most apparent in the least restrictive (20.4%) and the most restrictive placement (18.4%). Older youth showed greater discordant distributions (62.9%). Log-linear analysis confirmed the overall robustness of concordance (odd ratios [ORs] range: 2.9-442.0), though discordance was most evident from small deviations from the decision support algorithm, such as one-level under-placement in group home (OR=5.3) and one-level over-placement in residential treatment center (OR=4.8). Concordance should be further explored using child-level clinical and placement stability outcomes. Discordance might be explained by dynamic factors such as availability of placements, caregiver preferences, or policy changes and could be justified by positive child-level outcomes. Empirical placement decision-making is critical to a child's journey in child welfare and should be continuously improved to effect positive child welfare outcomes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Robust Decision-making Applied to Model Selection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hemez, Francois M.

2012-08-06

The scientific and engineering communities are relying more and more on numerical models to simulate ever-increasingly complex phenomena. Selecting a model, from among a family of models that meets the simulation requirements, presents a challenge to modern-day analysts. To address this concern, a framework is adopted anchored in info-gap decision theory. The framework proposes to select models by examining the trade-offs between prediction accuracy and sensitivity to epistemic uncertainty. The framework is demonstrated on two structural engineering applications by asking the following question: Which model, of several numerical models, approximates the behavior of a structure when parameters that define eachmore » of those models are unknown? One observation is that models that are nominally more accurate are not necessarily more robust, and their accuracy can deteriorate greatly depending upon the assumptions made. It is posited that, as reliance on numerical models increases, establishing robustness will become as important as demonstrating accuracy.« less

Decision Making in the Balloon Analogue Risk Task (BART): Anterior Cingulate Cortex Signals Loss-Aversion but not the Infrequency of Risky Choices

PubMed Central

Fukunaga, Rena; Brown, Joshua W.; Bogg, Tim

2012-01-01

The inferior frontal gyrus/anterior insula (IFG/AI) and anterior cingulate cortex (ACC) are key regions involved in risk appraisal during decision making, but accounts of how these regions contribute to decision-making under risk remain contested. To help clarify the roles of these and other related regions, we used a modified version of the Balloon Analogue Risk Task (Lejuez et al., 2002) to distinguish between decision-making and feedback-related processes when participants decided to pursue a gain as the probability of loss increased parametrically. Specifically, we set out to test whether ACC and IFG/AI regions correspond to loss-aversion at the time of decision making in a way that is not confounded with either reward-seeking or infrequency effects. When participants chose to discontinue inflating the balloon (win option), we observed greater ACC and mainly bilateral IFG/AI activity at the time of decision as the probability of explosion increased, consistent with increased loss-aversion but inconsistent with an infrequency effect. In contrast, we found robust vmPFC activity when participants chose to continue inflating the balloon (risky option), consistent with reward-seeking. However, in the cingulate and mainly bilateral IFG regions, BOLD activation decreased when participants chose to inflate the balloon as the probability of explosion increased, findings consistent with a reduced loss-aversion signal. Our results highlight the existence of distinct reward-seeking and loss-averse signals during decision-making, as well as the importance of distinguishing decision and feedback signals. PMID:22707378

Hardwood price reporting.

Treesearch

Brent L. Sohngen; Richard W. Haynes

1994-01-01

Prices for red alder (Alnus rubra Bong.) hardwood logs are published and analyzed for reliability, consistency, and robustness. Timberland managers can use these prices to make decisions about land management. They show that values for red alder logs have been increasing steadily for the last 11 years.

Framing effect debiasing in medical decision making.

PubMed

Almashat, Sammy; Ayotte, Brian; Edelstein, Barry; Margrett, Jennifer

2008-04-01

Numerous studies have demonstrated the robustness of the framing effect in a variety of contexts. The present study investigated the effects of a debiasing procedure designed to prevent the framing effect for young adults who made decisions based on hypothetical medical decision-making vignettes. The debiasing technique involved participants listing advantages and disadvantages of each treatment prior to making a choice. One hundred and two undergraduate students read a set of three medical treatment vignettes that presented information in terms of different outcome probabilities under either debiasing or control conditions. The framing effect was demonstrated by the control group in two of the three vignettes. The debiasing group successfully avoided the framing effect for both of these vignettes. These results further support previous findings of the framing effect as well as an effective debiasing technique. This study improved upon previous framing debiasing studies by including a control group and personal medical scenarios, as well as demonstrating debiasing in a framing condition in which the framing effect was demonstrated without a debiasing procedure. The findings suggest a relatively simple manipulation may circumvent the use of decision-making heuristics in patients.

Topics in inference and decision-making with partial knowledge

NASA Technical Reports Server (NTRS)

Safavian, S. Rasoul; Landgrebe, David

1990-01-01

Two essential elements needed in the process of inference and decision-making are prior probabilities and likelihood functions. When both of these components are known accurately and precisely, the Bayesian approach provides a consistent and coherent solution to the problems of inference and decision-making. In many situations, however, either one or both of the above components may not be known, or at least may not be known precisely. This problem of partial knowledge about prior probabilities and likelihood functions is addressed. There are at least two ways to cope with this lack of precise knowledge: robust methods, and interval-valued methods. First, ways of modeling imprecision and indeterminacies in prior probabilities and likelihood functions are examined; then how imprecision in the above components carries over to the posterior probabilities is examined. Finally, the problem of decision making with imprecise posterior probabilities and the consequences of such actions are addressed. Application areas where the above problems may occur are in statistical pattern recognition problems, for example, the problem of classification of high-dimensional multispectral remote sensing image data.

Robustness of risk maps and survey networks to knowledge gaps about a new invasive pest.

PubMed

Yemshanov, Denys; Koch, Frank H; Ben-Haim, Yakov; Smith, William D

2010-02-01

In pest risk assessment it is frequently necessary to make management decisions regarding emerging threats under severe uncertainty. Although risk maps provide useful decision support for invasive alien species, they rarely address knowledge gaps associated with the underlying risk model or how they may change the risk estimates. Failure to recognize uncertainty leads to risk-ignorant decisions and miscalculation of expected impacts as well as the costs required to minimize these impacts. Here we use the information gap concept to evaluate the robustness of risk maps to uncertainties in key assumptions about an invading organism. We generate risk maps with a spatial model of invasion that simulates potential entries of an invasive pest via international marine shipments, their spread through a landscape, and establishment on a susceptible host. In particular, we focus on the question of how much uncertainty in risk model assumptions can be tolerated before the risk map loses its value. We outline this approach with an example of a forest pest recently detected in North America, Sirex noctilio Fabricius. The results provide a spatial representation of the robustness of predictions of S. noctilio invasion risk to uncertainty and show major geographic hotspots where the consideration of uncertainty in model parameters may change management decisions about a new invasive pest. We then illustrate how the dependency between the extent of uncertainties and the degree of robustness of a risk map can be used to select a surveillance network design that is most robust to knowledge gaps about the pest.

Reliable binary cell-fate decisions based on oscillations

NASA Astrophysics Data System (ADS)

Pfeuty, B.; Kaneko, K.

2014-02-01

Biological systems have often to perform binary decisions under highly dynamic and noisy environments, such as during cell-fate determination. These decisions can be implemented by two main bifurcation mechanisms based on the transitions from either monostability or oscillation to bistability. We compare these two mechanisms by using stochastic models with time-varying fields and by establishing asymptotic formulas for the choice probabilities. Different scaling laws for decision sensitivity with respect to noise strength and signal timescale are obtained, supporting a role for oscillatory dynamics in performing noise-robust and temporally tunable binary decision-making. This result provides a rationale for recent experimental evidences showing that oscillatory expression of proteins often precedes binary cell-fate decisions.

Immunizing Children: A Qualitative Analysis of Future Parental Decision Making.

PubMed

Espeleta, Hannah C; Beasley, Lana O; Ridings, Leigh E; Smith, Tyler J; Shields, Jennifer D

2017-10-01

Vaccinations are considered one of public health's greatest accomplishments. Despite evidence for vaccine effectiveness, uptake levels are still well below the Centers for Disease Control and Prevention's guidelines. The immunization decision-making process for parents is complex and depends on factors associated with knowledge and experiences. This qualitative study sought to expand on a previous decision-making model for immunizations by examining how individuals receive vaccination information, determining the role of experience in influencing decisions, and understanding how young adults might locate vaccination information in the future. Three focus groups were conducted with 29 undergraduate students without children. Results suggest that young adults exhibit an awareness of information regarding vaccine use and effectiveness, value doctor opinions and recommendations, and desire more robust research on vaccinations. Implications of these results include the importance of (1) disseminating vaccination education to young adults, (2) enhancing consistency/trust between medical professionals and youth, and (3) expanding public policy to increase vaccine uptake.

Robust stochastic fuzzy possibilistic programming for environmental decision making under uncertainty.

PubMed

Zhang, Xiaodong; Huang, Guo H; Nie, Xianghui

2009-12-20

Nonpoint source (NPS) water pollution is one of serious environmental issues, especially within an agricultural system. This study aims to propose a robust chance-constrained fuzzy possibilistic programming (RCFPP) model for water quality management within an agricultural system, where solutions for farming area, manure/fertilizer application amount, and livestock husbandry size under different scenarios are obtained and interpreted. Through improving upon the existing fuzzy possibilistic programming, fuzzy robust programming and chance-constrained programming approaches, the RCFPP can effectively reflect the complex system features under uncertainty, where implications of water quality/quantity restrictions for achieving regional economic development objectives are studied. By delimiting the uncertain decision space through dimensional enlargement of the original fuzzy constraints, the RCFPP enhances the robustness of the optimization processes and resulting solutions. The results of the case study indicate that useful information can be obtained through the proposed RCFPP model for providing feasible decision schemes for different agricultural activities under different scenarios (combinations of different p-necessity and p(i) levels). A p-necessity level represents the certainty or necessity degree of the imprecise objective function, while a p(i) level means the probabilities at which the constraints will be violated. A desire to acquire high agricultural income would decrease the certainty degree of the event that maximization of the objective be satisfied, and potentially violate water management standards; willingness to accept low agricultural income will run into the risk of potential system failure. The decision variables under combined p-necessity and p(i) levels were useful for the decision makers to justify and/or adjust the decision schemes for the agricultural activities through incorporation of their implicit knowledge. The results also suggest that this developed approach is applicable to many practical problems where fuzzy and probabilistic distribution information simultaneously exist.

Economic decision-making in morning/evening-type people as a function of time of day.

PubMed

Correa, Angel; Ruiz-Herrera, Noelia; Ruz, Maria; Tonetti, Lorenzo; Martoni, Monica; Fabbri, Marco; Natale, Vincenzo

2017-01-01

Decision-making is affected by psychological factors like emotional state or cognitive control, which may also vary with circadian rhythmicity. Here, we tested the influence of chronotype (32 morning-type versus 32 evening-type) and time of day (9 a.m. versus 5 p.m.) on interpersonal decision-making as measured by the Ultimatum Game. Participants had to accept or reject different economic offers proposed by a virtual participant. Acceptance involved distribution of gains as proposed, whereas rejection resulted in no gain for either player. The results of the game showed a deviation from rational performance, as participants usually rejected the unfair offers. This behaviour was similar for both chronotype groups, and in both times of day. This result may reflect the robustness of decision-making strategies across standard circadian phases under ecological conditions. Furthermore, morning-types invested more time than evening-types to respond to high-uncertainty offers. This more cautious decision-making style of morning-types fits with our finding of higher proactive control as compared to evening-types when performing the AX-Continuous Performance Task. In line with the literature on personality traits, our results suggest that morning-types behave with more conscientiousness and less risk-taking than evening-type individuals.

Decision Making on Medical Innovations in a Changing Health Care Environment: Insights from Accountable Care Organizations and Payers on Personalized Medicine and Other Technologies.

PubMed

Trosman, Julia R; Weldon, Christine B; Douglas, Michael P; Deverka, Patricia A; Watkins, John B; Phillips, Kathryn A

2017-01-01

New payment and care organization approaches, such as those of accountable care organizations (ACOs), are reshaping accountability and shifting risk, as well as decision making, from payers to providers, within the Triple Aim context of health reform. The Triple Aim calls for improving experience of care, improving health of populations, and reducing health care costs. To understand how the transition to the ACO model impacts decision making on adoption and use of innovative technologies in the era of accelerating scientific advancement of personalized medicine and other innovations. We interviewed representatives from 10 private payers and 6 provider institutions involved in implementing the ACO model (i.e., ACOs) to understand changes, challenges, and facilitators of decision making on medical innovations, including personalized medicine. We used the framework approach of qualitative research for study design and thematic analysis. We found that representatives from the participating payer companies and ACOs perceive similar challenges to ACOs' decision making in terms of achieving a balance between the components of the Triple Aim-improving care experience, improving population health, and reducing costs. The challenges include the prevalence of cost over care quality considerations in ACOs' decisions and ACOs' insufficient analytical and technology assessment capacity to evaluate complex innovations such as personalized medicine. Decision-making facilitators included increased competition across ACOs and patients' interest in personalized medicine. As new payment models evolve, payers, ACOs, and other stakeholders should address challenges and leverage opportunities to arm ACOs with robust, consistent, rigorous, and transparent approaches to decision making on medical innovations. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Decision-Making on Medical Innovations in a Changing Healthcare Environment: Insights from Accountable Care Organizations and Payers on Personalized Medicine and Other Technologies

PubMed Central

Trosman, Julia R.; Weldon, Christine B.; Douglas, Michael P.; Deverka, Patricia A.; Watkins, John; Phillips, Kathryn A.

2016-01-01

Background New payment and care organization approaches, such as the Accountable Care Organization (ACO), are reshaping accountability and shifting risk, as well as decision-making, from payers to providers, under the Triple Aim of health reform. The Triple Aim calls for improving experience of care, improving health of populations and reducing healthcare costs. In the era of accelerating scientific advancement of personalized medicine and other innovations, it is critical to understand how the transition to the ACO model impacts decision-making on adoption and utilization of innovative technologies. Methods We interviewed representatives from ten private payers and six provider institutions involved in implementing the ACO model (i.e. ACOs) to understand changes, challenges and facilitators of decision-making on medical innovations, including personalized medicine. We used the framework approach of qualitative research for study design and thematic analysis. Results We found that representatives from the participating payer companies and ACOs perceive similar challenges to ACOs’ decision-making in terms of achieving a balance between the components of the Triple Aim – improving care experience, improving population health and reducing costs. The challenges include the prevalence of cost over care quality considerations in ACOs’ decisions and ACOs’ insufficient analytical and technology assessment capacity to evaluate complex innovations such as personalized medicine. Decision-making facilitators included increased competition across ACOs and patients’ interest in personalized medicine. Conclusions As new payment models evolve, payers, ACOs and other stakeholders should address challenges and leverage opportunities to arm ACOs with robust, consistent, rigorous and transparent approaches to decision-making on medical innovations. PMID:28212967

Robust sampling of decision information during perceptual choice

PubMed Central

Vandormael, Hildward; Herce Castañón, Santiago; Balaguer, Jan; Li, Vickie; Summerfield, Christopher

2017-01-01

Humans move their eyes to gather information about the visual world. However, saccadic sampling has largely been explored in paradigms that involve searching for a lone target in a cluttered array or natural scene. Here, we investigated the policy that humans use to overtly sample information in a perceptual decision task that required information from across multiple spatial locations to be combined. Participants viewed a spatial array of numbers and judged whether the average was greater or smaller than a reference value. Participants preferentially sampled items that were less diagnostic of the correct answer (“inlying” elements; that is, elements closer to the reference value). This preference to sample inlying items was linked to decisions, enhancing the tendency to give more weight to inlying elements in the final choice (“robust averaging”). These findings contrast with a large body of evidence indicating that gaze is directed preferentially to deviant information during natural scene viewing and visual search, and suggest that humans may sample information “robustly” with their eyes during perceptual decision-making. PMID:28223519

Gender bias and judicial decisions of undue influence in testamentary challenges.

PubMed

Recupero, Patricia R; Christopher, Paul P; Strong, David R; Price, Marilyn; Harms, Samara E

2015-03-01

Allegations of undue influence constitute a common basis for contests of wills. Legal research from the 1990s suggests that gender bias factors significantly into judicial decision-making regarding alleged undue influence and testamentary intent. In this study, we sought to assess whether this bias is present today and to identify any factors that may be associated with it. Probate judges from several jurisdictions in the United States were asked to consider two hypothetical case vignettes drawn from actual published decisions. In our study, the gender of the testator played only a minor role in how judges weighed factors in the decision-making process and, overall, did not significantly influence opinions regarding the presence of undue influence. The specifics of the case and the gender of the judge emerged as the most consistent and robust potential influences on decision-making. Our results suggest that probate rulings involving undue influence are likely to represent a complex interaction of factors involving the testator's and judge's genders and the specifics of individual cases. The implications of these findings are discussed. © 2015 American Academy of Psychiatry and the Law.

Uncertainty, robustness, and the value of information in managing a population of northern bobwhites

USGS Publications Warehouse

Johnson, Fred A.; Hagan, Greg; Palmer, William E.; Kemmerer, Michael

2014-01-01

The abundance of northern bobwhites (Colinus virginianus) has decreased throughout their range. Managers often respond by considering improvements in harvest and habitat management practices, but this can be challenging if substantial uncertainty exists concerning the cause(s) of the decline. We were interested in how application of decision science could be used to help managers on a large, public management area in southwestern Florida where the bobwhite is a featured species and where abundance has severely declined. We conducted a workshop with managers and scientists to elicit management objectives, alternative hypotheses concerning population limitation in bobwhites, potential management actions, and predicted management outcomes. Using standard and robust approaches to decision making, we determined that improved water management and perhaps some changes in hunting practices would be expected to produce the best management outcomes in the face of uncertainty about what is limiting bobwhite abundance. We used a criterion called the expected value of perfect information to determine that a robust management strategy may perform nearly as well as an optimal management strategy (i.e., a strategy that is expected to perform best, given the relative importance of different management objectives) with all uncertainty resolved. We used the expected value of partial information to determine that management performance could be increased most by eliminating uncertainty over excessive-harvest and human-disturbance hypotheses. Beyond learning about the factors limiting bobwhites, adoption of a dynamic management strategy, which recognizes temporal changes in resource and environmental conditions, might produce the greatest management benefit. Our research demonstrates that robust approaches to decision making, combined with estimates of the value of information, can offer considerable insight into preferred management approaches when great uncertainty exists about system dynamics and the effects of management.

Précis of Simple heuristics that make us smart.

PubMed

Todd, P M; Gigerenzer, G

2000-10-01

How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), we explore fast and frugal heuristics--simple rules in the mind's adaptive toolbox for making decisions with realistic mental resources. These heuristics can enable both living organisms and artificial systems to make smart choices quickly and with a minimum of information by exploiting the way that information is structured in particular environments. In this précis, we show how simple building blocks that control information search, stop search, and make decisions can be put together to form classes of heuristics, including: ignorance-based and one-reason decision making for choice, elimination models for categorization, and satisficing heuristics for sequential search. These simple heuristics perform comparably to more complex algorithms, particularly when generalizing to new data--that is, simplicity leads to robustness. We present evidence regarding when people use simple heuristics and describe the challenges to be addressed by this research program.

Many-Objective Robust Decision Making: Managing Water in a Deeply Uncertain World of Change (Invited)

NASA Astrophysics Data System (ADS)

Reed, P. M.

2013-12-01

Water resources planning and management has always required the consideration of uncertainties and the associated system vulnerabilities that they may cause. Despite the long legacy of these issues, our decision support frameworks that have dominated the literature over the past 50 years have struggled with the strongly multiobjective and deeply uncertain nature of water resources systems. The term deep uncertainty (or Knightian uncertainty) refers to factors in planning that strongly shape system risks that maybe unknown and even if known there is a strong lack of consensus on their likelihoods over decadal planning horizons (population growth, financial stability, valuation of resources, ecosystem requirements, evolving water institutions, regulations, etc). In this presentation, I will propose and demonstrate the many-objective robust decision making (MORDM) framework for water resources management under deep uncertainty. The MORDM framework will be demonstrated using an urban water portfolio management test case. In the test case, a city in the Lower Rio Grande Valley managing population and drought pressures must cost effectively maintain the reliability of its water supply by blending permanent rights to reservoir inflows with alternative strategies for purchasing water within the region's water market. The case study illustrates the significant potential pitfalls in the classic Cost-Reliability conception of the problem. Moreover, the proposed MORDM framework exploits recent advances in multiobjective search, visualization, and sensitivity analysis to better expose these pitfalls en route to identifying highly robust water planning alternatives.

Leadership of risk decision making in a complex, technology organization: The deliberative decision making model

NASA Astrophysics Data System (ADS)

Flaming, Susan C.

2007-12-01

The continuing saga of satellite technology development is as much a story of successful risk management as of innovative engineering. How do program leaders on complex, technology projects manage high stakes risks that threaten business success and satellite performance? This grounded theory study of risk decision making portrays decision leadership practices at one communication satellite company. Integrated product team (IPT) leaders of multi-million dollar programs were interviewed and observed to develop an extensive description of the leadership skills required to navigate organizational influences and drive challenging risk decisions to closure. Based on the study's findings the researcher proposes a new decision making model, Deliberative Decision Making, to describe the program leaders' cognitive and organizational leadership practices. This Deliberative Model extends the insights of prominent decision making models including the rational (or classical) and the naturalistic and qualifies claims made by bounded rationality theory. The Deliberative Model describes how leaders proactively engage resources to play a variety of decision leadership roles. The Model incorporates six distinct types of leadership decision activities, undertaken in varying sequence based on the challenges posed by specific risks. Novel features of the Deliberative Decision Model include: an inventory of leadership methods for managing task challenges, potential stakeholder bias and debates; four types of leadership meta-decisions that guide decision processes, and aligned organizational culture. Both supporting and constraining organizational influences were observed as leaders managed major risks, requiring active leadership on the most difficult decisions. Although the company's engineering culture emphasized the importance of data-based decisions, the uncertainties intrinsic to satellite risks required expert engineering judgment to be exercised throughout. An investigation into the co-variation of decision methods with uncertainty suggests that perceived risk severity may serve as a robust indicator for choices about decision practices. The Deliberative Decision processes incorporate multiple organizational and cultural controls as cross-checks to mitigate potential parochial bias of individuals, stakeholder groups, or leaders. Overall the Deliberative Decision framework describes how expert leadership practices, supportive organizational systems along with aligned cultural values and behavioral norms help leaders drive high stakes risk decisions to closure in this complex, advanced-technology setting.

A multi-criteria decision making approach to identify a vaccine formulation.

PubMed

Dewé, Walthère; Durand, Christelle; Marion, Sandie; Oostvogels, Lidia; Devaster, Jeanne-Marie; Fourneau, Marc

2016-01-01

This article illustrates the use of a multi-criteria decision making approach, based on desirability functions, to identify an appropriate adjuvant composition for an influenza vaccine to be used in elderly. The proposed adjuvant system contained two main elements: monophosphoryl lipid and α-tocopherol with squalene in an oil/water emulsion. The objective was to elicit a stronger immune response while maintaining an acceptable reactogenicity and safety profile. The study design, the statistical models, the choice of the desirability functions, the computation of the overall desirability index, and the assessment of the robustness of the ranking are all detailed in this manuscript.

The boundaries of instance-based learning theory for explaining decisions from experience.

PubMed

Gonzalez, Cleotilde

2013-01-01

Most demonstrations of how people make decisions in risky situations rely on decisions from description, where outcomes and their probabilities are explicitly stated. But recently, more attention has been given to decisions from experience where people discover these outcomes and probabilities through exploration. More importantly, risky behavior depends on how decisions are made (from description or experience), and although prospect theory explains decisions from description, a comprehensive model of decisions from experience is yet to be found. Instance-based learning theory (IBLT) explains how decisions are made from experience through interactions with dynamic environments (Gonzalez et al., 2003). The theory has shown robust explanations of behavior across multiple tasks and contexts, but it is becoming unclear what the theory is able to explain and what it does not. The goal of this chapter is to start addressing this problem. I will introduce IBLT and a recent cognitive model based on this theory: the IBL model of repeated binary choice; then I will discuss the phenomena that the IBL model explains and those that the model does not. The argument is for the theory's robustness but also for clarity in terms of concrete effects that the theory can or cannot account for. Copyright © 2013 Elsevier B.V. All rights reserved.

A multicriteria decision making model for assessment and selection of an ERP in a logistics context

NASA Astrophysics Data System (ADS)

Pereira, Teresa; Ferreira, Fernanda A.

2017-07-01

The aim of this work is to apply a methodology of decision support based on a multicriteria decision analyses (MCDA) model that allows the assessment and selection of an Enterprise Resource Planning (ERP) in a Portuguese logistics company by Group Decision Maker (GDM). A Decision Support system (DSS) that implements a MCDA - Multicriteria Methodology for the Assessment and Selection of Information Systems / Information Technologies (MMASSI / IT) is used based on its features and facility to change and adapt the model to a given scope. Using this DSS it was obtained the information system that best suited to the decisional context, being this result evaluated through a sensitivity and robustness analysis.

Risk Assessment and Decision-Making at the Local Level:?Who does what when, how, and to what extent?

EPA Science Inventory

Problem: Exposure to multiple stressors increases the likelihood of an adverse response in human and ecosystem communities. Challenge: A rigorous and scientifically robust assessment methodology is needed to characterize stressors and receptors of interest, calculate risk estimat...

Using social network analysis to examine the decision-making process on new vaccine introduction in Nigeria.

PubMed

Wonodi, C B; Privor-Dumm, L; Aina, M; Pate, A M; Reis, R; Gadhoke, P; Levine, O S

2012-05-01

The decision-making process to introduce new vaccines into national immunization programmes is often complex, involving many stakeholders who provide technical information, mobilize finance, implement programmes and garner political support. Stakeholders may have different levels of interest, knowledge and motivations to introduce new vaccines. Lack of consensus on the priority, public health value or feasibility of adding a new vaccine can delay policy decisions. Efforts to support country-level decision-making have largely focused on establishing global policies and equipping policy makers with the information to support decision-making on new vaccine introduction (NVI). Less attention has been given to understanding the interactions of policy actors and how the distribution of influence affects the policy process and decision-making. Social network analysis (SNA) is a social science technique concerned with explaining social phenomena using the structural and relational features of the network of actors involved. This approach can be used to identify how information is exchanged and who is included or excluded from the process. For this SNA of vaccine decision-making in Nigeria, we interviewed federal and state-level government officials, officers of bilateral and multilateral partner organizations, and other stakeholders such as health providers and the media. Using data culled from those interviews, we performed an SNA in order to map formal and informal relationships and the distribution of influence among vaccine decision-makers, as well as to explore linkages and pathways to stakeholders who can influence critical decisions in the policy process. Our findings indicate a relatively robust engagement of key stakeholders in Nigeria. We hypothesized that economic stakeholders and implementers would be important to ensure sustainable financing and strengthen programme implementation, but some economic and implementation stakeholders did not appear centrally on the map; this may suggest a need to strengthen the decision-making processes by engaging these stakeholders more centrally and earlier.

Dynamic afferent synapses to decision-making networks improve performance in tasks requiring stimulus associations and discriminations

PubMed Central

Bourjaily, Mark A.

2012-01-01

Animals must often make opposing responses to similar complex stimuli. Multiple sensory inputs from such stimuli combine to produce stimulus-specific patterns of neural activity. It is the differences between these activity patterns, even when small, that provide the basis for any differences in behavioral response. In the present study, we investigate three tasks with differing degrees of overlap in the inputs, each with just two response possibilities. We simulate behavioral output via winner-takes-all activity in one of two pools of neurons forming a biologically based decision-making layer. The decision-making layer receives inputs either in a direct stimulus-dependent manner or via an intervening recurrent network of neurons that form the associative layer, whose activity helps distinguish the stimuli of each task. We show that synaptic facilitation of synapses to the decision-making layer improves performance in these tasks, robustly increasing accuracy and speed of responses across multiple configurations of network inputs. Conversely, we find that synaptic depression worsens performance. In a linearly nonseparable task with exclusive-or logic, the benefit of synaptic facilitation lies in its superlinear transmission: effective synaptic strength increases with presynaptic firing rate, which enhances the already present superlinearity of presynaptic firing rate as a function of stimulus-dependent input. In linearly separable single-stimulus discrimination tasks, we find that facilitating synapses are always beneficial because synaptic facilitation always enhances any differences between inputs. Thus we predict that for optimal decision-making accuracy and speed, synapses from sensory or associative areas to decision-making or premotor areas should be facilitating. PMID:22457467

Diagnostic decision-making and strategies to improve diagnosis.

PubMed

Thammasitboon, Satid; Cutrer, William B

2013-10-01

A significant portion of diagnostic errors arises through cognitive errors resulting from inadequate knowledge, faulty data gathering, and/or faulty verification. Experts estimate that 75% of diagnostic failures can be attributed to clinician diagnostic thinking failure. The cognitive processes that underlie diagnostic thinking of clinicians are complex and intriguing, and it is imperative that clinicians acquire explicit appreciation and application of different cognitive approaches to make decisions better. A dual-process model that unifies many theories of decision-making has emerged as a promising template for understanding how clinicians think and judge efficiently in a diagnostic reasoning process. The identification and implementation of strategies for decreasing or preventing such diagnostic errors has become a growing area of interest and research. Suggested strategies to decrease diagnostic error incidence include increasing clinician's clinical expertise and avoiding inherent cognitive errors to make decisions better. Implementing Interventions focused solely on avoiding errors may work effectively for patient safety issues such as medication errors. Addressing cognitive errors, however, requires equal effort on expanding the individual clinician's expertise. Providing cognitive support to clinicians for robust diagnostic decision-making serves as the final strategic target for decreasing diagnostic errors. Clinical guidelines and algorithms offer another method for streamlining decision-making and decreasing likelihood of cognitive diagnostic errors. Addressing cognitive processing errors is undeniably the most challenging task in reducing diagnostic errors. While many suggested approaches exist, they are mostly based on theories and sciences in cognitive psychology, decision-making, and education. The proposed interventions are primarily suggestions and very few of them have been tested in the actual practice settings. Collaborative research effort is required to effectively address cognitive processing errors. Researchers in various areas, including patient safety/quality improvement, decision-making, and problem solving, must work together to make medical diagnosis more reliable. © 2013 Mosby, Inc. All rights reserved.

Genetic Redundancies Enhance Information Transfer in Noisy Regulatory Circuits

PubMed Central

Rodrigo, Guillermo; Poyatos, Juan F.

2016-01-01

Cellular decision making is based on regulatory circuits that associate signal thresholds to specific physiological actions. This transmission of information is subjected to molecular noise what can decrease its fidelity. Here, we show instead how such intrinsic noise enhances information transfer in the presence of multiple circuit copies. The result is due to the contribution of noise to the generation of autonomous responses by each copy, which are altogether associated with a common decision. Moreover, factors that correlate the responses of the redundant units (extrinsic noise or regulatory cross-talk) contribute to reduce fidelity, while those that further uncouple them (heterogeneity within the copies) can lead to stronger information gain. Overall, our study emphasizes how the interplay of signal thresholding, redundancy, and noise influences the accuracy of cellular decision making. Understanding this interplay provides a basis to explain collective cell signaling mechanisms, and to engineer robust decisions with noisy genetic circuits. PMID:27741249

Behavioral and Neural Adaptation in Approach Behavior.

PubMed

Wang, Shuo; Falvello, Virginia; Porter, Jenny; Said, Christopher P; Todorov, Alexander

2018-06-01

People often make approachability decisions based on perceived facial trustworthiness. However, it remains unclear how people learn trustworthiness from a population of faces and whether this learning influences their approachability decisions. Here we investigated the neural underpinning of approach behavior and tested two important hypotheses: whether the amygdala adapts to different trustworthiness ranges and whether the amygdala is modulated by task instructions and evaluative goals. We showed that participants adapted to the stimulus range of perceived trustworthiness when making approach decisions and that these decisions were further modulated by the social context. The right amygdala showed both linear response and quadratic response to trustworthiness level, as observed in prior studies. Notably, the amygdala's response to trustworthiness was not modulated by stimulus range or social context, a possible neural dynamic adaptation. Together, our data have revealed a robust behavioral adaptation to different trustworthiness ranges as well as a neural substrate underlying approach behavior based on perceived facial trustworthiness.

Mental Capacity Law, Autonomy, and best Interests: An Argument for Conceptual and Practical Clarity in the Court of Protection

PubMed Central

2016-01-01

This article examines medical decision-making, arguing that the law, properly understood, requires where possible that equal weight be given to the wishes, feelings, beliefs, and values of patients who have, and patients who are deemed to lack, decision-making capacity. It responds critically to dominant lines of reasoning that are advanced and applied in the Court of Protection, and suggests that for patient-centred practice to be achieved, we do not need to revise the law, but do need to ensure robust interpretation and application of the law. The argument is based on conceptual analysis of the law’s framing of patients and medical decisions, and legal analysis of evolving and contemporary norms governing the best interests standard. PMID:28007810

Decision making in the Balloon Analogue Risk Task (BART): anterior cingulate cortex signals loss aversion but not the infrequency of risky choices.

PubMed

Fukunaga, Rena; Brown, Joshua W; Bogg, Tim

2012-09-01

The inferior frontal gyrus/anterior insula (IFG/AI) and anterior cingulate cortex (ACC) are key regions involved in risk appraisal during decision making, but accounts of how these regions contribute to decision making under risk remain contested. To help clarify the roles of these and other related regions, we used a modified version of the Balloon Analogue Risk Task (Lejuez et al., Journal of Experimental Psychology: Applied, 8, 75-84, 2002) to distinguish between decision-making and feedback-related processes when participants decided to pursue a gain as the probability of loss increased parametrically. Specifically, we set out to test whether the ACC and IFG/AI regions correspond to loss aversion at the time of decision making in a way that is not confounded with either reward-seeking or infrequency effects. When participants chose to discontinue inflating the balloon (win option), we observed greater ACC and mainly bilateral IFG/AI activity at the time of decision as the probability of explosion increased, consistent with increased loss aversion but inconsistent with an infrequency effect. In contrast, we found robust vmPFC activity when participants chose to continue inflating the balloon (risky option), consistent with reward seeking. However, in the cingulate and in mainly bilateral IFG regions, blood-oxygenation-level-dependent activation decreased when participants chose to inflate the balloon as the probability of explosion increased, findings that are consistent with a reduced loss aversion signal. Our results highlight the existence of distinct reward-seeking and loss-averse signals during decision making, as well as the importance of distinguishing between decision and feedback signals.

A probabilistic approach to aircraft design emphasizing stability and control uncertainties

NASA Astrophysics Data System (ADS)

Delaurentis, Daniel Andrew

In order to address identified deficiencies in current approaches to aerospace systems design, a new method has been developed. This new method for design is based on the premise that design is a decision making activity, and that deterministic analysis and synthesis can lead to poor, or misguided decision making. This is due to a lack of disciplinary knowledge of sufficient fidelity about the product, to the presence of uncertainty at multiple levels of the aircraft design hierarchy, and to a failure to focus on overall affordability metrics as measures of goodness. Design solutions are desired which are robust to uncertainty and are based on the maximum knowledge possible. The new method represents advances in the two following general areas. 1. Design models and uncertainty. The research performed completes a transition from a deterministic design representation to a probabilistic one through a modeling of design uncertainty at multiple levels of the aircraft design hierarchy, including: (1) Consistent, traceable uncertainty classification and representation; (2) Concise mathematical statement of the Probabilistic Robust Design problem; (3) Variants of the Cumulative Distribution Functions (CDFs) as decision functions for Robust Design; (4) Probabilistic Sensitivities which identify the most influential sources of variability. 2. Multidisciplinary analysis and design. Imbedded in the probabilistic methodology is a new approach for multidisciplinary design analysis and optimization (MDA/O), employing disciplinary analysis approximations formed through statistical experimentation and regression. These approximation models are a function of design variables common to the system level as well as other disciplines. For aircraft, it is proposed that synthesis/sizing is the proper avenue for integrating multiple disciplines. Research hypotheses are translated into a structured method, which is subsequently tested for validity. Specifically, the implementation involves the study of the relaxed static stability technology for a supersonic commercial transport aircraft. The probabilistic robust design method is exercised resulting in a series of robust design solutions based on different interpretations of "robustness". Insightful results are obtained and the ability of the method to expose trends in the design space are noted as a key advantage.

Robust Sensitivity Analysis for the Joint Improvised Explosive Device Defeat Organization (JIEDDO) Proposal Selection Model

DTIC Science & Technology

2009-03-01

making process (Skinner, 2001, 9). According to Clemen , before we can begin to apply any methodology to a specific decision problem, the analyst...it is possible to work with them to determine the values and objectives that relate to the decision in question ( Clemen , 2001, 21). Clemen ...value hierarchy is constructed, Clemen and Reilly suggest that a trade off is made between varying objectives. They introduce weights to determine

Implementation and consistency of Heart Team decision-making in complex coronary revascularisation.

PubMed

Pavlidis, Antonis N; Perera, Divaka; Karamasis, Grigoris V; Bapat, Vinayak; Young, Chris; Clapp, Brian R; Blauth, Chris; Roxburgh, James; Thomas, Martyn R; Redwood, Simon R

2016-03-01

A multidisciplinary team (MDT) approach for decision-making in patients with complex coronary artery disease (CAD) is now a class IC recommendation in the European and American guidelines for myocardial revascularisation. The aim of this study was to evaluate the implementation and consistency of Heart Team HT decision-making in complex coronary revascularisation. We prospectively evaluated the data of 399 patients derived from 51 consecutive MDT meetings held in a tertiary cardiac centre. A subset of cases was randomly selected and re-presented with the same clinical data to a panel blinded to the initial outcome, at least 6 months after the initial discussion, in order to evaluate the reproducibility of decision-making. The most common decisions included continued medical management (30%), coronary artery bypass grafting (CABG) (26%) and percutaneous coronary intervention (PCI) (17%). Other decisions, such as further assessment of symptoms or evaluation with further invasive or non-invasive tests were made in 25% of the cases. Decisions were implemented in 93% of the cases. On re-discussion of the same data (n=40) within a median period of 9 months 80% of the initial HT recommendations were successfully reproduced. The Heart Team is a robust process in the management of patient with complex CAD and decisions are largely reproducible. Although outcomes are successfully implemented in the majority of the cases, it is important that all clinical information is available during discussion and patient preference is taken into account. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Motion-guided attention promotes adaptive communications during social navigation.

PubMed

Lemasson, B H; Anderson, J J; Goodwin, R A

2013-03-07

Animals are capable of enhanced decision making through cooperation, whereby accurate decisions can occur quickly through decentralized consensus. These interactions often depend upon reliable social cues, which can result in highly coordinated activities in uncertain environments. Yet information within a crowd may be lost in translation, generating confusion and enhancing individual risk. As quantitative data detailing animal social interactions accumulate, the mechanisms enabling individuals to rapidly and accurately process competing social cues remain unresolved. Here, we model how motion-guided attention influences the exchange of visual information during social navigation. We also compare the performance of this mechanism to the hypothesis that robust social coordination requires individuals to numerically limit their attention to a set of n-nearest neighbours. While we find that such numerically limited attention does not generate robust social navigation across ecological contexts, several notable qualities arise from selective attention to motion cues. First, individuals can instantly become a local information hub when startled into action, without requiring changes in neighbour attention level. Second, individuals can circumvent speed-accuracy trade-offs by tuning their motion thresholds. In turn, these properties enable groups to collectively dampen or amplify social information. Lastly, the minority required to sway a group's short-term directional decisions can change substantially with social context. Our findings suggest that motion-guided attention is a fundamental and efficient mechanism underlying collaborative decision making during social navigation.

Data for Renewable Energy Planning, Policy, and Investment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cox, Sarah L

Reliable, robust, and validated data are critical for informed planning, policy development, and investment in the clean energy sector. The Renewable Energy (RE) Explorer was developed to support data-driven renewable energy analysis that can inform key renewable energy decisions globally. This document presents the types of geospatial and other data at the core of renewable energy analysis and decision making. Individual data sets used to inform decisions vary in relation to spatial and temporal resolution, quality, and overall usefulness. From Data to Decisions, a complementary geospatial data and analysis decision guide, provides an in-depth view of these and other considerationsmore » to enable data-driven planning, policymaking, and investment. Data support a wide variety of renewable energy analyses and decisions, including technical and economic potential assessment, renewable energy zone analysis, grid integration, risk and resiliency identification, electrification, and distributed solar photovoltaic potential. This fact sheet provides information on the types of data that are important for renewable energy decision making using the RE Data Explorer or similar types of geospatial analysis tools.« less

The role of reporting standards in producing robust literature reviews

NASA Astrophysics Data System (ADS)

Haddaway, Neal Robert; Macura, Biljana

2018-06-01

Literature reviews can help to inform decision-making, yet they may be subject to fatal bias if not conducted rigorously as `systematic reviews'. Reporting standards help authors to provide sufficient methodological detail to allow verification and replication, clarifying when key steps, such as critical appraisal, have been omitted.

76 FR 67129 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-31

... Management Study provides a robust data base of information to address varied needs of policy makers. There... barometer on the financial condition of farm businesses. Data from the Fruit and Vegetable Chemical Use... assessments. Other organizations use this data to make sound regulatory decisions. Description of Respondents...

76 FR 14592 - Safety Management System; Withdrawal

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-17

...-06A] RIN 2120-AJ15 Safety Management System; Withdrawal AGENCY: Federal Aviation Administration (FAA... (``product/ service providers'') to develop a Safety Management System (SMS). The FAA is withdrawing the... management with a set of robust decision-making tools to use to improve safety. The FAA received 89 comments...

Instructional Simulation of a Commercial Banking System.

ERIC Educational Resources Information Center

Hester, Donald D.

1991-01-01

Describes an instructional simulation of a commercial banking system. Identifies the teaching of portfolio theory, market robustness, and the subtleties of institutional constraints and decision making under uncertainty as the project's goals. Discusses the results of applying the simulation in an environment of local and national markets and a…

What does the new breed of decision-making methodologies mean for choices and norms in hydrological science?

NASA Astrophysics Data System (ADS)

Wikman-Svahn, Per

2013-04-01

Hydrological sciences are increasingly utilized in decision-making contexts that need to manage deep uncertainty, changing conditions and very long-lead times and lifetimes. Traditional optimizing approaches become problematic in such situations. For example, optimizing approaches may underestimate the importance of low probability outcomes, or very uncertain outcomes. Alternative decision-making strategies are therefore increasingly used in hydrological applications, including "bottom-up/top-down", "context-first", "decision-scaling", "assess risk of policy", "robust", "resilient" or "flexible" approaches. These kinds of strategies are typically designed to handle very uncertain and diverse outcomes, and often start from the particular decision-making context, in contrast to more traditional "predict-then-act" or "science first" approaches. Contemporary research in philosophy of science stress the influence of value judgments and norms in scientific assessments. In particular, this literature points out that implicit anticipated applications often influence choices made in scientific assessments. Furthermore, this literature also emphasize that choices made at within scientific assessments have consequences for decision-making later on. One reason is that it is often difficult for decision-makers to see what choices are made and the implications of these choices. Another reason is that information that could be of use for decision-makers are lost at an early stage. For example, the choice to focus on central estimates and not providing assessments on more unlikely outcomes is a choice that has consequences for what outcomes are taken into account in the decision-making process. This paper develops this argument and then analyzes the implications of these new developments for hydrological science. One implication of the increasing use of the new breed of planning strategies is that a broader range of uncertainty in scientific assessments becomes desirable in order to fully benefit from the power of the new decision-making strategies. Another implication is that bayesian probability assessments become more important. Finally, advantages and risks involved in changing scientific assessments in order to anticipate the new decision-making strategies are discussed.

Prospective Architectures for Onboard vs Cloud-Based Decision Making for Unmanned Aerial Systems

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar; Teubert, Christopher

2017-01-01

This paper investigates propsective architectures for decision-making in unmanned aerial systems. When these unmanned vehicles operate in urban environments, there are several sources of uncertainty that affect their behavior, and decision-making algorithms need to be robust to account for these different sources of uncertainty. It is important to account for several risk-factors that affect the flight of these unmanned systems, and facilitate decision-making by taking into consideration these various risk-factors. In addition, there are several technical challenges related to autonomous flight of unmanned aerial systems; these challenges include sensing, obstacle detection, path planning and navigation, trajectory generation and selection, etc. Many of these activities require significant computational power and in many situations, all of these activities need to be performed in real-time. In order to efficiently integrate these activities, it is important to develop a systematic architecture that can facilitate real-time decision-making. Four prospective architectures are discussed in this paper; on one end of the spectrum, the first architecture considers all activities/computations being performed onboard the vehicle whereas on the other end of the spectrum, the fourth and final architecture considers all activities/computations being performed in the cloud, using a new service known as Prognostics as a Service that is being developed at NASA Ames Research Center. The four different architectures are compared, their advantages and disadvantages are explained and conclusions are presented.

The role of emotions in clinical reasoning and decision making.

PubMed

Marcum, James A

2013-10-01

What role, if any, should emotions play in clinical reasoning and decision making? Traditionally, emotions have been excluded from clinical reasoning and decision making, but with recent advances in cognitive neuropsychology they are now considered an important component of them. Today, cognition is thought to be a set of complex processes relying on multiple types of intelligences. The role of mathematical logic (hypothetico-deductive thinking) or verbal linguistic intelligence in cognition, for example, is well documented and accepted; however, the role of emotional intelligence has received less attention-especially because its nature and function are not well understood. In this paper, I argue for the inclusion of emotions in clinical reasoning and decision making. To that end, developments in contemporary cognitive neuropsychology are initially examined and analyzed, followed by a review of the medical literature discussing the role of emotions in clinical practice. Next, a published clinical case is reconstructed and used to illustrate the recognition and regulation of emotions played during a series of clinical consultations, which resulted in a positive medical outcome. The paper's main thesis is that emotions, particularly in terms of emotional intelligence as a practical form of intelligence, afford clinical practitioners a robust cognitive resource for providing quality medical care.

A Practical, Robust Methodology for Acquiring New Observation Data Using Computationally Expensive Groundwater Models

NASA Astrophysics Data System (ADS)

Siade, Adam J.; Hall, Joel; Karelse, Robert N.

2017-11-01

Regional groundwater flow models play an important role in decision making regarding water resources; however, the uncertainty embedded in model parameters and model assumptions can significantly hinder the reliability of model predictions. One way to reduce this uncertainty is to collect new observation data from the field. However, determining where and when to obtain such data is not straightforward. There exist a number of data-worth and experimental design strategies developed for this purpose. However, these studies often ignore issues related to real-world groundwater models such as computational expense, existing observation data, high-parameter dimension, etc. In this study, we propose a methodology, based on existing methods and software, to efficiently conduct such analyses for large-scale, complex regional groundwater flow systems for which there is a wealth of available observation data. The method utilizes the well-established d-optimality criterion, and the minimax criterion for robust sampling strategies. The so-called Null-Space Monte Carlo method is used to reduce the computational burden associated with uncertainty quantification. And, a heuristic methodology, based on the concept of the greedy algorithm, is proposed for developing robust designs with subsets of the posterior parameter samples. The proposed methodology is tested on a synthetic regional groundwater model, and subsequently applied to an existing, complex, regional groundwater system in the Perth region of Western Australia. The results indicate that robust designs can be obtained efficiently, within reasonable computational resources, for making regional decisions regarding groundwater level sampling.

The framing effect in medical decision-making: a review of the literature.

PubMed

Gong, Jingjing; Zhang, Yan; Yang, Zheng; Huang, Yonghua; Feng, Jun; Zhang, Weiwei

2013-01-01

The framing effect, identified by Tversky and Kahneman, is one of the most striking cognitive biases, in which people react differently to a particular choice depending whether it is presented as a loss or as a gain. Numerous studies have subsequently demonstrated the robustness of the framing effect in a variety of contexts, especially in medical decision-making. Compared to daily decisions, medical decisions are of low frequency but of paramount importance. The framing effect is a well-documented bias in a variety of studies, but research is inconsistent regarding whether and how variables influence framing effects in medical decision-making. To clarify the discrepancy in the previous literature, published literature in the English language concerning the framing effect was retrieved using electronic and bibliographic searches. Two reviewers examined each article for inclusion and evaluated the articles' methodological quality. The framing effect in medical decision-making was reviewed in these papers. No studies identified an influence of framing information upon compliance with health recommendations, and different studies demonstrate different orientations of the framing effect. Because so many variables influence the presence or absence of the framing effect, the unexplained heterogeneity between studies suggests the possibility of a framing effect under specific conditions. Further research is needed to determine why the framing effect is induced and how it can be precluded.

Smart strategies for doctors and doctors-in-training: heuristics in medicine.

PubMed

Wegwarth, Odette; Gaissmaier, Wolfgang; Gigerenzer, Gerd

2009-08-01

How do doctors make sound decisions when confronted with probabilistic data, time pressures and a heavy workload? One theory that has been embraced by many researchers is based on optimisation, which emphasises the need to integrate all information in order to arrive at sound decisions. This notion makes heuristics, which use less than complete information, appear as second-best strategies. In this article, we challenge this pessimistic view of heuristics. We introduce two medical problems that involve decision making to the reader: one concerns coronary care issues and the other macrolide prescriptions. In both settings, decision-making tools grounded in the principles of optimisation and heuristics, respectively, have been developed to assist doctors in making decisions. We explain the structure of each of these tools and compare their performance in terms of their facilitation of correct predictions. For decisions concerning both the coronary care unit and the prescribing of macrolides, we demonstrate that sacrificing information does not necessarily imply a forfeiting of predictive accuracy, but can sometimes even lead to better decisions. Subsequently, we discuss common misconceptions about heuristics and explain when and why ignoring parts of the available information can lead to the making of more robust predictions. Heuristics are neither good nor bad per se, but, if applied in situations to which they have been adapted, can be helpful companions for doctors and doctors-in-training. This, however, requires that heuristics in medicine be openly discussed, criticised, refined and then taught to doctors-in-training rather than being simply dismissed as harmful or irrelevant. A more uniform use of explicit and accepted heuristics has the potential to reduce variations in diagnoses and to improve medical care for patients.

Development of a fuzzy-stochastic programming with Green Z-score criterion method for planning water resources systems with a trading mechanism.

PubMed

Zeng, X T; Huang, G H; Li, Y P; Zhang, J L; Cai, Y P; Liu, Z P; Liu, L R

2016-12-01

This study developed a fuzzy-stochastic programming with Green Z-score criterion (FSGZ) method for water resources allocation and water quality management with a trading-mechanism (WAQT) under uncertainties. FSGZ can handle uncertainties expressed as probability distributions, and it can also quantify objective/subjective fuzziness in the decision-making process. Risk-averse attitudes and robustness coefficient are joined to express the relationship between the expected target and outcome under various risk preferences of decision makers and systemic robustness. The developed method is applied to a real-world case of WAQT in the Kaidu-Kongque River Basin in northwest China, where an effective mechanism (e.g., market trading) to simultaneously confront severely diminished water availability and degraded water quality is required. Results of water transaction amounts, water allocation patterns, pollution mitigation schemes, and system benefits under various scenarios are analyzed, which indicate that a trading-mechanism is a more sustainable method to manage water-environment crisis in the study region. Additionally, consideration of anthropogenic (e.g., a risk-averse attitude) and systemic factors (e.g., the robustness coefficient) can support the generation of a robust plan associated with risk control for WAQT when uncertainty is present. These findings assist local policy and decision makers to gain insights into water-environment capacity planning to balance the basin's social and economic growth with protecting the region's ecosystems.

Why bother with the brain? A role for decision neuroscience in understanding strategic variability.

PubMed

Venkatraman, Vinod

2013-01-01

Neuroscience, by its nature, seems to hold considerable promise for understanding the fundamental mechanisms of decision making. In recent years, several studies in the domain of "neuroeconomics" or "decision neuroscience" have provided important insights into brain function. Yet, the apparent success and value of each of these domains are frequently called into question by researchers in economics and behavioral decision making. Critics often charge that knowledge about the brain is unnecessary for understanding decision preferences. In this chapter, I contend that knowledge about underlying brain mechanisms helps in the development of biologically plausible models of behavior, which can then help elucidate the mechanisms underlying individual choice biases and strategic preferences. Using a novel risky choice paradigm, I will demonstrate that people vary in whether they adopt compensatory or noncompensatory rules in economic decision making. Importantly, neuroimaging studies using functional magnetic resonance imaging reveal that distinct neural mechanisms support variability in choices and variability in strategic preferences. Converging evidence from a study involving decisions between hypothetical stocks illustrates how knowledge about the underlying mechanisms can help inform neuroanatomical models of cognitive control. Last, I will demonstrate how knowledge about these underlying neural mechanisms can provide novel insights into the effects of decision states like sleep deprivation on decision preferences. Together, these findings suggest that neuroscience can play a critical role in creating robust and flexible models of real-world decision behavior. Copyright © 2013 Elsevier B.V. All rights reserved.

Risk-based flood protection planning under climate change and modeling uncertainty: a pre-alpine case study

NASA Astrophysics Data System (ADS)

Dittes, Beatrice; Kaiser, Maria; Špačková, Olga; Rieger, Wolfgang; Disse, Markus; Straub, Daniel

2018-05-01

Planning authorities are faced with a range of questions when planning flood protection measures: is the existing protection adequate for current and future demands or should it be extended? How will flood patterns change in the future? How should the uncertainty pertaining to this influence the planning decision, e.g., for delaying planning or including a safety margin? Is it sufficient to follow a protection criterion (e.g., to protect from the 100-year flood) or should the planning be conducted in a risk-based way? How important is it for flood protection planning to accurately estimate flood frequency (changes), costs and damage? These are questions that we address for a medium-sized pre-alpine catchment in southern Germany, using a sequential Bayesian decision making framework that quantitatively addresses the full spectrum of uncertainty. We evaluate different flood protection systems considered by local agencies in a test study catchment. Despite large uncertainties in damage, cost and climate, the recommendation is robust for the most conservative approach. This demonstrates the feasibility of making robust decisions under large uncertainty. Furthermore, by comparison to a previous study, it highlights the benefits of risk-based planning over the planning of flood protection to a prescribed return period.

True Numerical Cognition in the Wild.

PubMed

Piantadosi, Steven T; Cantlon, Jessica F

2017-04-01

Cognitive and neural research over the past few decades has produced sophisticated models of the representations and algorithms underlying numerical reasoning in humans and other animals. These models make precise predictions for how humans and other animals should behave when faced with quantitative decisions, yet primarily have been tested only in laboratory tasks. We used data from wild baboons' troop movements recently reported by Strandburg-Peshkin, Farine, Couzin, and Crofoot (2015) to compare a variety of models of quantitative decision making. We found that the decisions made by these naturally behaving wild animals rely specifically on numerical representations that have key homologies with the psychophysics of human number representations. These findings provide important new data on the types of problems human numerical cognition was designed to solve and constitute the first robust evidence of true numerical reasoning in wild animals.

Fuzzy Based Decision Support System for Condition Assessment and Rating of Bridges

NASA Astrophysics Data System (ADS)

Srinivas, Voggu; Sasmal, Saptarshi; Karusala, Ramanjaneyulu

2016-09-01

In this work, a knowledge based decision support system has been developed to efficiently handle the issues such as distress diagnosis, assessment of damages and condition rating of existing bridges towards developing an exclusive and robust Bridge Management System (BMS) for sustainable bridges. The Knowledge Based Expert System (KBES) diagnoses the distresses and finds the cause of distress in the bridge by processing the data which are heuristic and combined with site inspection results, laboratory test results etc. The coupling of symbolic and numeric type of data has been successfully implemented in the expert system to strengthen its decision making process. Finally, the condition rating of the bridge is carried out using the assessment results obtained from the KBES and the information received from the bridge inspector. A systematic procedure has been developed using fuzzy mathematics for condition rating of bridges by combining the fuzzy weighted average and resolution identity technique. The proposed methodologies and the decision support system will facilitate in developing a robust and exclusive BMS for a network of bridges across the country and allow the bridge engineers and decision makers to carry out maintenance of bridges in a rational and systematic way.

Using structured decision making to manage disease risk for Montana wildlife

USGS Publications Warehouse

Mitchell, Michael S.; Gude, Justin A.; Anderson, Neil J.; Ramsey, Jennifer M.; Thompson, Michael J.; Sullivan, Mark G.; Edwards, Victoria L.; Gower, Claire N.; Cochrane, Jean Fitts; Irwin, Elise R.; Walshe, Terry

2013-01-01

We used structured decision-making to develop a 2-part framework to assist managers in the proactive management of disease outbreaks in Montana, USA. The first part of the framework is a model to estimate the probability of disease outbreak given field observations available to managers. The second part of the framework is decision analysis that evaluates likely outcomes of management alternatives based on the estimated probability of disease outbreak, and applies managers' values for different objectives to indicate a preferred management strategy. We used pneumonia in bighorn sheep (Ovis canadensis) as a case study for our approach, applying it to 2 populations in Montana that differed in their likelihood of a pneumonia outbreak. The framework provided credible predictions of both probability of disease outbreaks, as well as biological and monetary consequences of management actions. The structured decision-making approach to this problem was valuable for defining the challenges of disease management in a decentralized agency where decisions are generally made at the local level in cooperation with stakeholders. Our approach provides local managers with the ability to tailor management planning for disease outbreaks to local conditions. Further work is needed to refine our disease risk models and decision analysis, including robust prediction of disease outbreaks and improved assessment of management alternatives.

Multistable binary decision making on networks

NASA Astrophysics Data System (ADS)

Lucas, Andrew; Lee, Ching Hua

2013-03-01

We propose a simple model for a binary decision making process on a graph, motivated by modeling social decision making with cooperative individuals. The model is similar to a random field Ising model or fiber bundle model, but with key differences in behavior on heterogeneous networks. For many types of disorder and interactions between the nodes, we predict with mean field theory discontinuous phase transitions that are largely independent of network structure. We show how these phase transitions can also be understood by studying microscopic avalanches and describe how network structure enhances fluctuations in the distribution of avalanches. We suggest theoretically the existence of a “glassy” spectrum of equilibria associated with a typical phase, even on infinite graphs, so long as the first moment of the degree distribution is finite. This behavior implies that the model is robust against noise below a certain scale and also that phase transitions can switch from discontinuous to continuous on networks with too few edges. Numerical simulations suggest that our theory is accurate.

Meta-analysis in evidence-based healthcare: a paradigm shift away from random effects is overdue.

PubMed

Doi, Suhail A R; Furuya-Kanamori, Luis; Thalib, Lukman; Barendregt, Jan J

2017-12-01

Each year up to 20 000 systematic reviews and meta-analyses are published whose results influence healthcare decisions, thus making the robustness and reliability of meta-analytic methods one of the world's top clinical and public health priorities. The evidence synthesis makes use of either fixed-effect or random-effects statistical methods. The fixed-effect method has largely been replaced by the random-effects method as heterogeneity of study effects led to poor error estimation. However, despite the widespread use and acceptance of the random-effects method to correct this, it too remains unsatisfactory and continues to suffer from defective error estimation, posing a serious threat to decision-making in evidence-based clinical and public health practice. We discuss here the problem with the random-effects approach and demonstrate that there exist better estimators under the fixed-effect model framework that can achieve optimal error estimation. We argue for an urgent return to the earlier framework with updates that address these problems and conclude that doing so can markedly improve the reliability of meta-analytical findings and thus decision-making in healthcare.

Averaging business cycles vs. myopia: Do we need a long term vision when developing IRP?

DOE Office of Scientific and Technical Information (OSTI.GOV)

McDonald, C.; Gupta, P.C.

1995-05-01

Utility demand forecasting is inherently imprecise due to the number of uncertainties resulting from business cycles, policy making, technology breakthroughs, national and international political upheavals and the limitations of the forecasting tools. This implies that revisions based primarily on recent experience could lead to unstable forecasts. Moreover, new planning tools are required that provide an explicit consideration of uncertainty and lead to flexible and robust planning tools are required that provide an explicit consideration of uncertainty and lead to flexible and robust planning decisions.

Value-Based Assessment of New Medical Technologies: Towards a Robust Methodological Framework for the Application of Multiple Criteria Decision Analysis in the Context of Health Technology Assessment.

PubMed

Angelis, Aris; Kanavos, Panos

2016-05-01

In recent years, multiple criteria decision analysis (MCDA) has emerged as a likely alternative to address shortcomings in health technology assessment (HTA) by offering a more holistic perspective to value assessment and acting as an alternative priority setting tool. In this paper, we argue that MCDA needs to subscribe to robust methodological processes related to the selection of objectives, criteria and attributes in order to be meaningful in the context of healthcare decision making and fulfil its role in value-based assessment (VBA). We propose a methodological process, based on multi-attribute value theory (MAVT) methods comprising five distinct phases, outline the stages involved in each phase and discuss their relevance in the HTA process. Importantly, criteria and attributes need to satisfy a set of desired properties, otherwise the outcome of the analysis can produce spurious results and misleading recommendations. Assuming the methodological process we propose is adhered to, the application of MCDA presents three very distinct advantages to decision makers in the context of HTA and VBA: first, it acts as an instrument for eliciting preferences on the performance of alternative options across a wider set of explicit criteria, leading to a more complete assessment of value; second, it allows the elicitation of preferences across the criteria themselves to reflect differences in their relative importance; and, third, the entire process of preference elicitation can be informed by direct stakeholder engagement, and can therefore reflect their own preferences. All features are fully transparent and facilitate decision making.

Analysis of market penetration of renewable energy alternatives under uncertain and carbon constrained world

EPA Science Inventory

Future energy prices and supply, availability and costs can have a significant impact on how fast and cost effectively we could abate carbon emissions. Two-staged decision making methods embedded in U.S. EPA's MARKAL modeling system will be utilized to find the most robust mitig...

Effects of an Uncertain Literature on All Facets of Clinical Decision Making

ERIC Educational Resources Information Center

Sammons, Morgan T.; Newman, Russ

2010-01-01

Greenberg (2010) is correct in his assertion that the investigational heuristic used to measure the efficacy of antidepressants is flawed. Robust placebo effects are endemic in the psychiatric literature, particularly in studies of antidepressants, and estimates of placebo responding have increased over time (Rief et al., 2009). In the case of…

Minding the Gap: Investing in a Skilled Manufacturing Workforce

ERIC Educational Resources Information Center

Richard, Alan

2015-01-01

Advanced manufacturing is growing and thriving in the United States. Companies are in great need of reliable employees who can communicate well, effectively make decisions, and are interested in long-term careers with opportunity for advancement. Employers have identified a need for a more robust talent pipeline to narrow America's skills gap--a…

Denoising of genetic switches based on Parrondo's paradox

NASA Astrophysics Data System (ADS)

Fotoohinasab, Atiyeh; Fatemizadeh, Emad; Pezeshk, Hamid; Sadeghi, Mehdi

2018-03-01

Random decision making in genetic switches can be modeled as tossing a biased coin. In other word, each genetic switch can be considered as a game in which the reactive elements compete with each other to increase their molecular concentrations. The existence of a very small number of reactive element molecules has caused the neglect of effects of noise to be inevitable. Noise can lead to undesirable cell fate in cellular differentiation processes. In this paper, we study the robustness to noise in genetic switches by considering another switch to have a new gene regulatory network (GRN) in which both switches have been affected by the same noise and for this purpose, we will use Parrondo's paradox. We introduce two networks of games based on possible regulatory relations between genes. Our results show that the robustness to noise can increase by combining these noisy switches. We also describe how one of the switches in network II can model lysis/lysogeny decision making of bacteriophage lambda in Escherichia coli and we change its fate by another switch.

Beyond negative valence: 2-week administration of a serotonergic antidepressant enhances both reward and effort learning signals.

PubMed

Scholl, Jacqueline; Kolling, Nils; Nelissen, Natalie; Browning, Michael; Rushworth, Matthew F S; Harmer, Catherine J

2017-02-01

To make good decisions, humans need to learn about and integrate different sources of appetitive and aversive information. While serotonin has been linked to value-based decision-making, its role in learning is less clear, with acute manipulations often producing inconsistent results. Here, we show that when the effects of a selective serotonin reuptake inhibitor (SSRI, citalopram) are studied over longer timescales, learning is robustly improved. We measured brain activity with functional magnetic resonance imaging (fMRI) in volunteers as they performed a concurrent appetitive (money) and aversive (effort) learning task. We found that 2 weeks of citalopram enhanced reward and effort learning signals in a widespread network of brain regions, including ventromedial prefrontal and anterior cingulate cortex. At a behavioral level, this was accompanied by more robust reward learning. This suggests that serotonin can modulate the ability to learn via a mechanism that is independent of stimulus valence. Such effects may partly underlie SSRIs' impact in treating psychological illnesses. Our results highlight both a specific function in learning for serotonin and the importance of studying its role across longer timescales.

An Integrated Environmental Assessment of Green and Gray Infrastructure Strategies for Robust Decision Making.

PubMed

Casal-Campos, Arturo; Fu, Guangtao; Butler, David; Moore, Andrew

2015-07-21

The robustness of a range of watershed-scale "green" and "gray" drainage strategies in the future is explored through comprehensive modeling of a fully integrated urban wastewater system case. Four socio-economic future scenarios, defined by parameters affecting the environmental performance of the system, are proposed to account for the uncertain variability of conditions in the year 2050. A regret-based approach is applied to assess the relative performance of strategies in multiple impact categories (environmental, economic, and social) as well as to evaluate their robustness across future scenarios. The concept of regret proves useful in identifying performance trade-offs and recognizing states of the world most critical to decisions. The study highlights the robustness of green strategies (particularly rain gardens, resulting in half the regret of most options) over end-of-pipe gray alternatives (surface water separation or sewer and storage rehabilitation), which may be costly (on average, 25% of the total regret of these options) and tend to focus on sewer flooding and CSO alleviation while compromising on downstream system performance (this accounts for around 50% of their total regret). Trade-offs and scenario regrets observed in the analysis suggest that the combination of green and gray strategies may still offer further potential for robustness.

Computing by robust transience: How the fronto-parietal network performs sequential category-based decisions

PubMed Central

Chaisangmongkon, Warasinee; Swaminathan, Sruthi K.; Freedman, David J.; Wang, Xiao-Jing

2017-01-01

Summary Decision making involves dynamic interplay between internal judgements and external perception, which has been investigated in delayed match-to-category (DMC) experiments. Our analysis of neural recordings shows that, during DMC tasks, LIP and PFC neurons demonstrate mixed, time-varying, and heterogeneous selectivity, but previous theoretical work has not established the link between these neural characteristics and population-level computations. We trained a recurrent network model to perform DMC tasks and found that the model can remarkably reproduce key features of neuronal selectivity at the single-neuron and population levels. Analysis of the trained networks elucidates that robust transient trajectories of the neural population are the key driver of sequential categorical decisions. The directions of trajectories are governed by network self-organized connectivity, defining a ‘neural landscape’, consisting of a task-tailored arrangement of slow states and dynamical tunnels. With this model, we can identify functionally-relevant circuit motifs and generalize the framework to solve other categorization tasks. PMID:28334612

Decision-Making under Ambiguity Is Modulated by Visual Framing, but Not by Motor vs. Non-Motor Context. Experiments and an Information-Theoretic Ambiguity Model

PubMed Central

Grau-Moya, Jordi; Ortega, Pedro A.; Braun, Daniel A.

2016-01-01

A number of recent studies have investigated differences in human choice behavior depending on task framing, especially comparing economic decision-making to choice behavior in equivalent sensorimotor tasks. Here we test whether decision-making under ambiguity exhibits effects of task framing in motor vs. non-motor context. In a first experiment, we designed an experience-based urn task with varying degrees of ambiguity and an equivalent motor task where subjects chose between hitting partially occluded targets. In a second experiment, we controlled for the different stimulus design in the two tasks by introducing an urn task with bar stimuli matching those in the motor task. We found ambiguity attitudes to be mainly influenced by stimulus design. In particular, we found that the same subjects tended to be ambiguity-preferring when choosing between ambiguous bar stimuli, but ambiguity-avoiding when choosing between ambiguous urn sample stimuli. In contrast, subjects’ choice pattern was not affected by changing from a target hitting task to a non-motor context when keeping the stimulus design unchanged. In both tasks subjects’ choice behavior was continuously modulated by the degree of ambiguity. We show that this modulation of behavior can be explained by an information-theoretic model of ambiguity that generalizes Bayes-optimal decision-making by combining Bayesian inference with robust decision-making under model uncertainty. Our results demonstrate the benefits of information-theoretic models of decision-making under varying degrees of ambiguity for a given context, but also demonstrate the sensitivity of ambiguity attitudes across contexts that theoretical models struggle to explain. PMID:27124723

Effects of acute administration of nicotinic and muscarinic cholinergic agonists and antagonists on performance in different cost–benefit decision making tasks in rats

PubMed Central

Mendez, Ian A.; Gilbert, Ryan J.; Bizon, Jennifer L.

2012-01-01

Rationale Alterations in cost–benefit decision making accompany numerous neuropsychiatric conditions, including schizophrenia, attention deficit hyperactivity disorder, and addiction. Central cholinergic systems have been linked to the etiology and/or treatment of many of these conditions, but little is known about the role of cholinergic signaling in cost–benefit decision making. Objectives The goal of these experiments was to determine how cholinergic signaling is involved in cost–benefit decision making, using a behavioral pharmacological approach. Methods Male Long-Evans rats were trained in either “probability discounting” or “delay discounting” tasks, in which rats made discrete-trial choices between a small food reward and a large food reward associated with either varying probabilities of omission or varying delays to delivery, respectively. The effects of acute administration of different doses of nicotinic and muscarinic acetylcholine receptor agonists and antagonists were assessed in each task. Results In the probability discounting task, acute nicotine administration (1.0 mg/kg) significantly increased choice of the large risky reward, and control experiments suggested that this was due to robust nicotine-induced impairments in behavioral flexibility. In the delay discounting task, the muscarinic antagonists scopolamine (0.03, 0.1, and 0.3 mg/kg) and atropine (0.3 mg/kg) both significantly increased choice of the small immediate reward. Neither mecamylamine nor oxotremorine produced reliable effects on either of the decision making tasks. Conclusions These data suggest that cholinergic receptors play multiple roles in decision making contexts which include consideration of reward delay or probability. These roles should be considered when targeting these receptors for therapeutic purposes. PMID:22760484

Decision-Making under Ambiguity Is Modulated by Visual Framing, but Not by Motor vs. Non-Motor Context. Experiments and an Information-Theoretic Ambiguity Model.

PubMed

Grau-Moya, Jordi; Ortega, Pedro A; Braun, Daniel A

2016-01-01

A number of recent studies have investigated differences in human choice behavior depending on task framing, especially comparing economic decision-making to choice behavior in equivalent sensorimotor tasks. Here we test whether decision-making under ambiguity exhibits effects of task framing in motor vs. non-motor context. In a first experiment, we designed an experience-based urn task with varying degrees of ambiguity and an equivalent motor task where subjects chose between hitting partially occluded targets. In a second experiment, we controlled for the different stimulus design in the two tasks by introducing an urn task with bar stimuli matching those in the motor task. We found ambiguity attitudes to be mainly influenced by stimulus design. In particular, we found that the same subjects tended to be ambiguity-preferring when choosing between ambiguous bar stimuli, but ambiguity-avoiding when choosing between ambiguous urn sample stimuli. In contrast, subjects' choice pattern was not affected by changing from a target hitting task to a non-motor context when keeping the stimulus design unchanged. In both tasks subjects' choice behavior was continuously modulated by the degree of ambiguity. We show that this modulation of behavior can be explained by an information-theoretic model of ambiguity that generalizes Bayes-optimal decision-making by combining Bayesian inference with robust decision-making under model uncertainty. Our results demonstrate the benefits of information-theoretic models of decision-making under varying degrees of ambiguity for a given context, but also demonstrate the sensitivity of ambiguity attitudes across contexts that theoretical models struggle to explain.

Quality improvement in multidisciplinary cancer teams: an investigation of teamwork and clinical decision-making and cross-validation of assessments.

PubMed

Lamb, B W; Sevdalis, N; Mostafid, H; Vincent, C; Green, J S A

2011-12-01

Teamworking and clinical decision-making are important in multidisciplinary cancer teams (MDTs). Our objective is to assess the quality of information presentation and MDT members' contribution to decision-making via expert observation and self-report, aiming to cross-validate the two methods and assess the insight of MDT members into their own team performance. Behaviors were scored using (i) a validated observational tool employing Likert scales with objective anchors, and (ii) a 29-question online self-report tool. Data were collected from observation of 164 cases in five MDTs, and 47 surveys from MDT members (response rate 70%). Presentation of information (case history, radiological, pathological, comorbidities, psychosocial, and patients' views) and quality of contribution to decision-making of MDT members (surgeons, oncologists, radiologists, pathologists, nurses, and MDT coordinators) were analyzed via descriptive statistics and the Jonckheere-Terpstra test. Correlation between observational and self-report assessments was assessed with Spearman's correlations. Quality of information presentation: Case histories and radiology information rated highest; patients' views and comorbidities/psychosocial issues rated lowest (observed: Z = 14.80, P ≤ 0.001; self-report: Z = 3.70, P < 0.001). Contribution to decision-making: Surgeons and oncologists rated highest, nurses and MDT coordinators rated lowest, and others in between (observed: Z = 20.00, P ≤ 0.001; self-report: Z = 8.10, P < 0.001). Correlations between observational and self-report assessments: Median Spearman's rho = 0.74 (range = 0.66-0.91; P < 0.05). The quality of teamworking and clinical decision-making in MDTs can reliably be assessed using observational and self-report metrics. MDT members have good insight into their own team performance. Such robust assessment methods could provide the basis of a toolkit for MDT team evaluation and improvement.

If you can't comply with dialysis, how do you expect me to trust you with transplantation? Australian nephrologists' views on indigenous Australians' 'non-compliance' and their suitability for kidney transplantation

PubMed Central

2012-01-01

Introduction Indigenous Australians suffer markedly higher rates of end-stage kidney disease (ESKD) but are less likely than their non-Indigenous counterparts to receive a transplant. This difference is not fully explained by measurable clinical differences. Previous work suggests that Indigenous Australian patients may be regarded by treating specialists as 'non-compliers', which may negatively impact on referral for a transplant. However, this decision-making is not well understood. The objectives of this study were to investigate: whether Indigenous patients are commonly characterised as 'non-compliers'; how estimations of patient compliance factor into Australian nephrologists' decision-making about transplant referral; and whether this may pose a particular barrier for Indigenous patients accessing transplants. Methods Nineteen nephrologists, from eight renal units treating the majority of Indigenous Australian renal patients, were interviewed in 2005-06 as part of a larger study. Thematic analysis was undertaken to investigate how compliance factors in specialists' decision-making, and its implications for Indigenous patients' likelihood of obtaining transplants. Results Specialists commonly identified Indigenous patients as both non-compliers and high-risk transplant candidates. Definition and assessment of 'compliance' was neither formal nor systematic. There was uncertainty about the value of compliance status in predicting post-transplant outcomes and the issue of organ scarcity permeated participants' responses. Overall, there was marked variation in how specialists weighed perceptions of compliance and risk in their decision-making. Conclusion Reliance on notions of patient 'compliance' in decision-making for transplant referral is likely to result in continuing disadvantage for Indigenous Australian ESKD patients. In the absence of robust evidence on predictors of post-transplant outcomes, referral decision-making processes require attention and debate. PMID:22513223

Effects of acute administration of nicotinic and muscarinic cholinergic agonists and antagonists on performance in different cost-benefit decision making tasks in rats.

PubMed

Mendez, Ian A; Gilbert, Ryan J; Bizon, Jennifer L; Setlow, Barry

2012-12-01

Alterations in cost-benefit decision making accompany numerous neuropsychiatric conditions, including schizophrenia, attention deficit hyperactivity disorder, and addiction. Central cholinergic systems have been linked to the etiology and/or treatment of many of these conditions, but little is known about the role of cholinergic signaling in cost-benefit decision making. The goal of these experiments was to determine how cholinergic signaling is involved in cost-benefit decision making, using a behavioral pharmacological approach. Male Long-Evans rats were trained in either "probability discounting" or "delay discounting" tasks, in which rats made discrete-trial choices between a small food reward and a large food reward associated with either varying probabilities of omission or varying delays to delivery, respectively. The effects of acute administration of different doses of nicotinic and muscarinic acetylcholine receptor agonists and antagonists were assessed in each task. In the probability discounting task, acute nicotine administration (1.0 mg/kg) significantly increased choice of the large risky reward, and control experiments suggested that this was due to robust nicotine-induced impairments in behavioral flexibility. In the delay discounting task, the muscarinic antagonists scopolamine (0.03, 0.1, and 0.3 mg/kg) and atropine (0.3 mg/kg) both significantly increased choice of the small immediate reward. Neither mecamylamine nor oxotremorine produced reliable effects on either of the decision making tasks. These data suggest that cholinergic receptors play multiple roles in decision making contexts which include consideration of reward delay or probability. These roles should be considered when targeting these receptors for therapeutic purposes.

If you can't comply with dialysis, how do you expect me to trust you with transplantation? Australian nephrologists' views on indigenous Australians' 'non-compliance' and their suitability for kidney transplantation.

PubMed

Anderson, Kate; Devitt, Jeannie; Cunningham, Joan; Preece, Cilla; Jardine, Meg; Cass, Alan

2012-04-18

Indigenous Australians suffer markedly higher rates of end-stage kidney disease (ESKD) but are less likely than their non-Indigenous counterparts to receive a transplant. This difference is not fully explained by measurable clinical differences. Previous work suggests that Indigenous Australian patients may be regarded by treating specialists as 'non-compliers', which may negatively impact on referral for a transplant. However, this decision-making is not well understood. The objectives of this study were to investigate: whether Indigenous patients are commonly characterised as 'non-compliers'; how estimations of patient compliance factor into Australian nephrologists' decision-making about transplant referral; and whether this may pose a particular barrier for Indigenous patients accessing transplants. Nineteen nephrologists, from eight renal units treating the majority of Indigenous Australian renal patients, were interviewed in 2005-06 as part of a larger study. Thematic analysis was undertaken to investigate how compliance factors in specialists' decision-making, and its implications for Indigenous patients' likelihood of obtaining transplants. Specialists commonly identified Indigenous patients as both non-compliers and high-risk transplant candidates. Definition and assessment of 'compliance' was neither formal nor systematic. There was uncertainty about the value of compliance status in predicting post-transplant outcomes and the issue of organ scarcity permeated participants' responses. Overall, there was marked variation in how specialists weighed perceptions of compliance and risk in their decision-making. Reliance on notions of patient 'compliance' in decision-making for transplant referral is likely to result in continuing disadvantage for Indigenous Australian ESKD patients. In the absence of robust evidence on predictors of post-transplant outcomes, referral decision-making processes require attention and debate.

Sea-level projections representing the deeply uncertain contribution of the West Antarctic ice sheet.

PubMed

Bakker, Alexander M R; Wong, Tony E; Ruckert, Kelsey L; Keller, Klaus

2017-06-20

There is a growing awareness that uncertainties surrounding future sea-level projections may be much larger than typically perceived. Recently published projections appear widely divergent and highly sensitive to non-trivial model choices . Moreover, the West Antarctic ice sheet (WAIS) may be much less stable than previous believed, enabling a rapid disintegration. Here, we present a set of probabilistic sea-level projections that approximates the deeply uncertain WAIS contributions. The projections aim to inform robust decisions by clarifying the sensitivity to non-trivial or controversial assumptions. We show that the deeply uncertain WAIS contribution can dominate other uncertainties within decades. These deep uncertainties call for the development of robust adaptive strategies. These decision-making needs, in turn, require mission-oriented basic science, for example about potential signposts and the maximum rate of WAIS-induced sea-level changes.

Doing our best: optimization and the management of risk.

PubMed

Ben-Haim, Yakov

2012-08-01

Tools and concepts of optimization are widespread in decision-making, design, and planning. There is a moral imperative to "do our best." Optimization underlies theories in physics and biology, and economic theories often presume that economic agents are optimizers. We argue that in decisions under uncertainty, what should be optimized is robustness rather than performance. We discuss the equity premium puzzle from financial economics, and explain that the puzzle can be resolved by using the strategy of satisficing rather than optimizing. We discuss design of critical technological infrastructure, showing that satisficing of performance requirements--rather than optimizing them--is a preferable design concept. We explore the need for disaster recovery capability and its methodological dilemma. The disparate domains--economics and engineering--illuminate different aspects of the challenge of uncertainty and of the significance of robust-satisficing. © 2012 Society for Risk Analysis.

Patients' perspectives in health technology assessment: a route to robust evidence and fair deliberation.

PubMed

Facey, Karen; Boivin, Antoine; Gracia, Javier; Hansen, Helle Ploug; Lo Scalzo, Alessandra; Mossman, Jean; Single, Ann

2010-07-01

There is increasing emphasis on providing patient-focused health care and ensuring patient involvement in the design of health services. As health technology assessment (HTA) is meant to be a multidisciplinary, wide-ranging policy analysis that informs decision making, it would be expected that patients' views should be incorporated into the assessment. However, HTA is still driven by collection of quantitative evidence to determine the clinical and cost effectiveness of a health technology. Patients' perspectives about their illness and the technology are rarely included, perhaps because they are seen as anecdotal, biased views. There are two distinct but complementary ways in which HTAs can be strengthened by: (i) gathering robust evidence about the patients' perspectives, and (ii) ensuring effective engagement of patients in the HTA process from scoping, through evidence gathering, assessment of value, development of recommendations and dissemination of findings. Robust evidence eliciting patients' perspectives can be obtained through social science research that is well conducted, critically appraised and carefully reported, either through meta-synthesis of existing studies or new primary research. Engagement with patients can occur at several levels and we propose that HTA should seek to support effective patient participation to create a fair deliberative process. This should allow two-way flow of information, so that the views of patients are obtained in a supportive way and fed into decision-making processes in a transparent manner.

Developpement energetique par modelisation et intelligence territoriale: Un outil de prise de decision participative pour le developpement durable des projets eoliens

NASA Astrophysics Data System (ADS)

Vazquez Rascon, Maria de Lourdes

This thesis focuses on the implementation of a participatory and transparent decision making tool about the wind farm projects. This tool is based on an (argumentative) framework that reflects the stakeholder's values systems involved in these projects and it employs two multicriteria methods: the multicriteria decision aide and the participatory geographical information systems, making it possible to represent this value systems by criteria and indicators to be evaluated. The stakeholder's values systems will allow the inclusion of environmental, economic and social-cultural aspects of wind energy projects and, thus, a sustainable development wind projects vision. This vision will be analyzed using the 16 sustainable principles included in the Quebec's Sustainable Development Act. Four specific objectives have been instrumented to favor a logical completion work, and to ensure the development of a successfultool : designing a methodology to couple the MCDA and participatory GIS, testing the developed methodology by a case study, making a robustness analysis to address strategic issues and analyzing the strengths, weaknesses, opportunities and threads of the developed methodology. Achieving the first goal allowed us to obtain a decision-making tool called Territorial Intelligence Modeling for Energy Development (TIMED approach). The TIMED approach is visually represented by a figure expressing the idea of a co-construction decision and where ail stakeholders are the focus of this methodology. TIMED is composed of four modules: Multi-Criteria decision analysis, participatory geographic Information systems, active involvement of the stakeholders and scientific knowledge/local knowledge. The integration of these four modules allows for the analysis of different implementation scenarios of wind turbines in order to choose the best one based on a participatory and transparent decision-making process that takes into account stakeholders' concerns. The second objective enabled the testing of TIMED in an ex-post experience of a wind farm in operation since 2006. In this test, II people participated representing four stakeholder' categories: the private sector, the public sector, experts and civil society. This test allowed us to analyze the current situation in which wind projects are currently developed in Quebec. The concerns of some stakeholders regarding situations that are not considered in the current context were explored through a third goal. This third objective allowed us to make simulations taking into account the assumptions of strategic levels. Examples of the strategic level are the communication tools used to approach the host community and the park property type. Finally, the fourth objective, a SWOT analysis with the participation of eight experts, allowed us to verify the extent to which TIMED approach succeeded in constructing four fields for participatory decision-making: physical, intellectual, emotional and procedural. From these facts, 116 strengths, 28 weaknesses, 32 constraints and 54 opportunities were identified. Contributions, applications, limitations and extensions of this research are based on giving a participatory decision-making methodology taking into account socio-cultural, environmental and economic variables; making reflection sessions on a wind farm in operation; acquiring MCDA knowledge for participants involved in testing the proposed methodology; taking into account the physical, intellectual, emotional and procedural spaces to al1iculate a participatory decision; using the proposed methodology in renewable energy sources other than wind; the need to an interdisciplinary team for the methodology application; access to quality data; access to information technologies; the right to public participation; the neutrality of experts; the relationships between experts and non-experts; cultural constraints; improvement of designed indicators; the implementation of a Web platform for participatory decision-making and writing a manual on the use of the developed methodology. Keywords: wind farm, multicriteria decision, geographic information systems, TIMED approach, sustainable wind energy projects development, renewable energy, social participation, robustness concern, SWOT analysis.

Take a stand on your decisions, or take a sit: posture does not affect risk preferences in an economic task.

PubMed

O'Brien, Megan K; Ahmed, Alaa A

2014-01-01

Physiological and emotional states can affect our decision-making processes, even when these states are seemingly insignificant to the decision at hand. We examined whether posture and postural threat affect decisions in a non-related economic domain. Healthy young adults made a series of choices between economic lotteries in various conditions, including changes in body posture (sitting vs. standing) and changes in elevation (ground level vs. atop a 0.8-meter-high platform). We compared three metrics between conditions to assess changes in risk-sensitivity: frequency of risky choices, and parameter fits of both utility and probability weighting parameters using cumulative prospect theory. We also measured skin conductance level to evaluate physiological response to the postural threat. Our results demonstrate that body posture does not significantly affect decision making. Secondly, despite increased skin conductance level, economic risk-sensitivity was unaffected by increased threat. Our findings indicate that economic choices are fairly robust to the physiological and emotional changes that result from posture or postural threat.

The Physics of Decision Making:. Stochastic Differential Equations as Models for Neural Dynamics and Evidence Accumulation in Cortical Circuits

NASA Astrophysics Data System (ADS)

Holmes, Philip; Eckhoff, Philip; Wong-Lin, K. F.; Bogacz, Rafal; Zacksenhouse, Miriam; Cohen, Jonathan D.

2010-03-01

We describe how drift-diffusion (DD) processes - systems familiar in physics - can be used to model evidence accumulation and decision-making in two-alternative, forced choice tasks. We sketch the derivation of these stochastic differential equations from biophysically-detailed models of spiking neurons. DD processes are also continuum limits of the sequential probability ratio test and are therefore optimal in the sense that they deliver decisions of specified accuracy in the shortest possible time. This leaves open the critical balance of accuracy and speed. Using the DD model, we derive a speed-accuracy tradeoff that optimizes reward rate for a simple perceptual decision task, compare human performance with this benchmark, and discuss possible reasons for prevalent sub-optimality, focussing on the question of uncertain estimates of key parameters. We present an alternative theory of robust decisions that allows for uncertainty, and show that its predictions provide better fits to experimental data than a more prevalent account that emphasises a commitment to accuracy. The article illustrates how mathematical models can illuminate the neural basis of cognitive processes.

Take a stand on your decisions, or take a sit: posture does not affect risk preferences in an economic task

PubMed Central

O’Brien, Megan K.

2014-01-01

Physiological and emotional states can affect our decision-making processes, even when these states are seemingly insignificant to the decision at hand. We examined whether posture and postural threat affect decisions in a non-related economic domain. Healthy young adults made a series of choices between economic lotteries in various conditions, including changes in body posture (sitting vs. standing) and changes in elevation (ground level vs. atop a 0.8-meter-high platform). We compared three metrics between conditions to assess changes in risk-sensitivity: frequency of risky choices, and parameter fits of both utility and probability weighting parameters using cumulative prospect theory. We also measured skin conductance level to evaluate physiological response to the postural threat. Our results demonstrate that body posture does not significantly affect decision making. Secondly, despite increased skin conductance level, economic risk-sensitivity was unaffected by increased threat. Our findings indicate that economic choices are fairly robust to the physiological and emotional changes that result from posture or postural threat. PMID:25083345

Design optimization for cost and quality: The robust design approach

NASA Technical Reports Server (NTRS)

Unal, Resit

1990-01-01

Designing reliable, low cost, and operable space systems has become the key to future space operations. Designing high quality space systems at low cost is an economic and technological challenge to the designer. A systematic and efficient way to meet this challenge is a new method of design optimization for performance, quality, and cost, called Robust Design. Robust Design is an approach for design optimization. It consists of: making system performance insensitive to material and subsystem variation, thus allowing the use of less costly materials and components; making designs less sensitive to the variations in the operating environment, thus improving reliability and reducing operating costs; and using a new structured development process so that engineering time is used most productively. The objective in Robust Design is to select the best combination of controllable design parameters so that the system is most robust to uncontrollable noise factors. The robust design methodology uses a mathematical tool called an orthogonal array, from design of experiments theory, to study a large number of decision variables with a significantly small number of experiments. Robust design also uses a statistical measure of performance, called a signal-to-noise ratio, from electrical control theory, to evaluate the level of performance and the effect of noise factors. The purpose is to investigate the Robust Design methodology for improving quality and cost, demonstrate its application by the use of an example, and suggest its use as an integral part of space system design process.

Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks.

PubMed

Albergante, Luca; Blow, J Julian; Newman, Timothy J

2014-09-02

The gene regulatory network (GRN) is the central decision-making module of the cell. We have developed a theory called Buffered Qualitative Stability (BQS) based on the hypothesis that GRNs are organised so that they remain robust in the face of unpredictable environmental and evolutionary changes. BQS makes strong and diverse predictions about the network features that allow stable responses under arbitrary perturbations, including the random addition of new connections. We show that the GRNs of E. coli, M. tuberculosis, P. aeruginosa, yeast, mouse, and human all verify the predictions of BQS. BQS explains many of the small- and large-scale properties of GRNs, provides conditions for evolvable robustness, and highlights general features of transcriptional response. BQS is severely compromised in a human cancer cell line, suggesting that loss of BQS might underlie the phenotypic plasticity of cancer cells, and highlighting a possible sequence of GRN alterations concomitant with cancer initiation. Copyright © 2014, Albergante et al.

What Climate Information Do Water Managers Need to Make Robust, Long-Term Plans?

NASA Astrophysics Data System (ADS)

Duran, R.; Lempert, R.; Groves, D.

2008-12-01

What climate information do water managers need to respond to threat of climate change? Southern California's Inland Empire Utilities Agency (IEUA) completed a long-range water resource management plan in 2005 that addressed expected economic and population growth in their service region, but did not consider the potential impacts of climate change. Using a robust decision making (RDM) approach for policy under deep uncertainty, we recently worked with IEUA to conduct a climate-change vulnerability and response options analysis of the agency's long-range plans. This analysis suggests that IEUA is vulnerable to future climate change, but can significantly reduce this vulnerability by increasing their near-term conservation programs and careful monitoring and updating to adjust their plan in the years ahead. In addition to helping IEUA, this analysis provides important guidance on the types of climate and other information that can be most useful for water managers as they attempt to take robust, near-term actions to increaase their resilience to climate change.

Response time distributions in rapid chess: a large-scale decision making experiment.

PubMed

Sigman, Mariano; Etchemendy, Pablo; Slezak, Diego Fernández; Cecchi, Guillermo A

2010-01-01

Rapid chess provides an unparalleled laboratory to understand decision making in a natural environment. In a chess game, players choose consecutively around 40 moves in a finite time budget. The goodness of each choice can be determined quantitatively since current chess algorithms estimate precisely the value of a position. Web-based chess produces vast amounts of data, millions of decisions per day, incommensurable with traditional psychological experiments. We generated a database of response times (RTs) and position value in rapid chess games. We measured robust emergent statistical observables: (1) RT distributions are long-tailed and show qualitatively distinct forms at different stages of the game, (2) RT of successive moves are highly correlated both for intra- and inter-player moves. These findings have theoretical implications since they deny two basic assumptions of sequential decision making algorithms: RTs are not stationary and can not be generated by a state-function. Our results also have practical implications. First, we characterized the capacity of blunders and score fluctuations to predict a player strength, which is yet an open problem in chess softwares. Second, we show that the winning likelihood can be reliably estimated from a weighted combination of remaining times and position evaluation.

Building the Capacity for Climate Services: Thoughts on Training Next Generation Climate Science Integrators

NASA Astrophysics Data System (ADS)

Garfin, G. M.; Brugger, J.; Gordon, E. S.; Barsugli, J. J.; Rangwala, I.; Travis, W.

2015-12-01

For more than a decade, stakeholder needs assessments and reports, including the recent National Climate Assessment, have pointed out the need for climate "science translators" or "science integrators" who can help bridge the gap between the cultures and contexts of researchers and decision-makers. Integration is important for exchanging and enhancing knowledge, building capacity to use climate information in decision making, and fostering more robust planning for decision-making in the context of climate change. This talk will report on the characteristics of successful climate science integrators, and a variety of models for training the upcoming generation of climate science integrators. Science integration characteristics identified by an experienced vanguard in the U.S. include maintaining credibility in both the scientific and stakeholder communities, a basic respect for stakeholders demonstrated through active listening, and a deep understanding of the decision-making context. Drawing upon the lessons of training programs for Cooperative Extension, public health professionals, and natural resource managers, we offer ideas about training next generation climate science integrators. Our model combines training and development of skills in interpersonal relations, communication of science, project implementation, education techniques and practices - integrated with a strong foundation in disciplinary knowledge.

Response Time Distributions in Rapid Chess: A Large-Scale Decision Making Experiment

PubMed Central

Sigman, Mariano; Etchemendy, Pablo; Slezak, Diego Fernández; Cecchi, Guillermo A.

2010-01-01

Rapid chess provides an unparalleled laboratory to understand decision making in a natural environment. In a chess game, players choose consecutively around 40 moves in a finite time budget. The goodness of each choice can be determined quantitatively since current chess algorithms estimate precisely the value of a position. Web-based chess produces vast amounts of data, millions of decisions per day, incommensurable with traditional psychological experiments. We generated a database of response times (RTs) and position value in rapid chess games. We measured robust emergent statistical observables: (1) RT distributions are long-tailed and show qualitatively distinct forms at different stages of the game, (2) RT of successive moves are highly correlated both for intra- and inter-player moves. These findings have theoretical implications since they deny two basic assumptions of sequential decision making algorithms: RTs are not stationary and can not be generated by a state-function. Our results also have practical implications. First, we characterized the capacity of blunders and score fluctuations to predict a player strength, which is yet an open problem in chess softwares. Second, we show that the winning likelihood can be reliably estimated from a weighted combination of remaining times and position evaluation. PMID:21031032

Water Resource Planning Under Future Climate and Socioeconomic Uncertainty in the Cauvery River Basin in Karnataka, India.

PubMed

Bhave, Ajay Gajanan; Conway, Declan; Dessai, Suraje; Stainforth, David A

2018-02-01

Decision-Making Under Uncertainty (DMUU) approaches have been less utilized in developing countries than developed countries for water resources contexts. High climate vulnerability and rapid socioeconomic change often characterize developing country contexts, making DMUU approaches relevant. We develop an iterative multi-method DMUU approach, including scenario generation, coproduction with stakeholders and water resources modeling. We apply this approach to explore the robustness of adaptation options and pathways against future climate and socioeconomic uncertainties in the Cauvery River Basin in Karnataka, India. A water resources model is calibrated and validated satisfactorily using observed streamflow. Plausible future changes in Indian Summer Monsoon (ISM) precipitation and water demand are used to drive simulations of water resources from 2021 to 2055. Two stakeholder-identified decision-critical metrics are examined: a basin-wide metric comprising legal instream flow requirements for the downstream state of Tamil Nadu, and a local metric comprising water supply reliability to Bangalore city. In model simulations, the ability to satisfy these performance metrics without adaptation is reduced under almost all scenarios. Implementing adaptation options can partially offset the negative impacts of change. Sequencing of options according to stakeholder priorities into Adaptation Pathways affects metric satisfaction. Early focus on agricultural demand management improves the robustness of pathways but trade-offs emerge between intrabasin and basin-wide water availability. We demonstrate that the fine balance between water availability and demand is vulnerable to future changes and uncertainty. Despite current and long-term planning challenges, stakeholders in developing countries may engage meaningfully in coproduction approaches for adaptation decision-making under deep uncertainty.

Water Resource Planning Under Future Climate and Socioeconomic Uncertainty in the Cauvery River Basin in Karnataka, India

NASA Astrophysics Data System (ADS)

Bhave, Ajay Gajanan; Conway, Declan; Dessai, Suraje; Stainforth, David A.

2018-02-01

Decision-Making Under Uncertainty (DMUU) approaches have been less utilized in developing countries than developed countries for water resources contexts. High climate vulnerability and rapid socioeconomic change often characterize developing country contexts, making DMUU approaches relevant. We develop an iterative multi-method DMUU approach, including scenario generation, coproduction with stakeholders and water resources modeling. We apply this approach to explore the robustness of adaptation options and pathways against future climate and socioeconomic uncertainties in the Cauvery River Basin in Karnataka, India. A water resources model is calibrated and validated satisfactorily using observed streamflow. Plausible future changes in Indian Summer Monsoon (ISM) precipitation and water demand are used to drive simulations of water resources from 2021 to 2055. Two stakeholder-identified decision-critical metrics are examined: a basin-wide metric comprising legal instream flow requirements for the downstream state of Tamil Nadu, and a local metric comprising water supply reliability to Bangalore city. In model simulations, the ability to satisfy these performance metrics without adaptation is reduced under almost all scenarios. Implementing adaptation options can partially offset the negative impacts of change. Sequencing of options according to stakeholder priorities into Adaptation Pathways affects metric satisfaction. Early focus on agricultural demand management improves the robustness of pathways but trade-offs emerge between intrabasin and basin-wide water availability. We demonstrate that the fine balance between water availability and demand is vulnerable to future changes and uncertainty. Despite current and long-term planning challenges, stakeholders in developing countries may engage meaningfully in coproduction approaches for adaptation decision-making under deep uncertainty.

Sensitivity and Bias in Decision-Making under Risk: Evaluating the Perception of Reward, Its Probability and Value

PubMed Central

Sharp, Madeleine E.; Viswanathan, Jayalakshmi; Lanyon, Linda J.; Barton, Jason J. S.

2012-01-01

Background There are few clinical tools that assess decision-making under risk. Tests that characterize sensitivity and bias in decisions between prospects varying in magnitude and probability of gain may provide insights in conditions with anomalous reward-related behaviour. Objective We designed a simple test of how subjects integrate information about the magnitude and the probability of reward, which can determine discriminative thresholds and choice bias in decisions under risk. Design/Methods Twenty subjects were required to choose between two explicitly described prospects, one with higher probability but lower magnitude of reward than the other, with the difference in expected value between the two prospects varying from 3 to 23%. Results Subjects showed a mean threshold sensitivity of 43% difference in expected value. Regarding choice bias, there was a ‘risk premium’ of 38%, indicating a tendency to choose higher probability over higher reward. An analysis using prospect theory showed that this risk premium is the predicted outcome of hypothesized non-linearities in the subjective perception of reward value and probability. Conclusions This simple test provides a robust measure of discriminative value thresholds and biases in decisions under risk. Prospect theory can also make predictions about decisions when subjective perception of reward or probability is anomalous, as may occur in populations with dopaminergic or striatal dysfunction, such as Parkinson's disease and schizophrenia. PMID:22493669

Sensitivity and bias in decision-making under risk: evaluating the perception of reward, its probability and value.

PubMed

Sharp, Madeleine E; Viswanathan, Jayalakshmi; Lanyon, Linda J; Barton, Jason J S

2012-01-01

There are few clinical tools that assess decision-making under risk. Tests that characterize sensitivity and bias in decisions between prospects varying in magnitude and probability of gain may provide insights in conditions with anomalous reward-related behaviour. We designed a simple test of how subjects integrate information about the magnitude and the probability of reward, which can determine discriminative thresholds and choice bias in decisions under risk. Twenty subjects were required to choose between two explicitly described prospects, one with higher probability but lower magnitude of reward than the other, with the difference in expected value between the two prospects varying from 3 to 23%. Subjects showed a mean threshold sensitivity of 43% difference in expected value. Regarding choice bias, there was a 'risk premium' of 38%, indicating a tendency to choose higher probability over higher reward. An analysis using prospect theory showed that this risk premium is the predicted outcome of hypothesized non-linearities in the subjective perception of reward value and probability. This simple test provides a robust measure of discriminative value thresholds and biases in decisions under risk. Prospect theory can also make predictions about decisions when subjective perception of reward or probability is anomalous, as may occur in populations with dopaminergic or striatal dysfunction, such as Parkinson's disease and schizophrenia.

How robust is a robust policy? A comparative analysis of alternative robustness metrics for supporting robust decision analysis.

NASA Astrophysics Data System (ADS)

Kwakkel, Jan; Haasnoot, Marjolijn

2015-04-01

In response to climate and socio-economic change, in various policy domains there is increasingly a call for robust plans or policies. That is, plans or policies that performs well in a very large range of plausible futures. In the literature, a wide range of alternative robustness metrics can be found. The relative merit of these alternative conceptualizations of robustness has, however, received less attention. Evidently, different robustness metrics can result in different plans or policies being adopted. This paper investigates the consequences of several robustness metrics on decision making, illustrated here by the design of a flood risk management plan. A fictitious case, inspired by a river reach in the Netherlands is used. The performance of this system in terms of casualties, damages, and costs for flood and damage mitigation actions is explored using a time horizon of 100 years, and accounting for uncertainties pertaining to climate change and land use change. A set of candidate policy options is specified up front. This set of options includes dike raising, dike strengthening, creating more space for the river, and flood proof building and evacuation options. The overarching aim is to design an effective flood risk mitigation strategy that is designed from the outset to be adapted over time in response to how the future actually unfolds. To this end, the plan will be based on the dynamic adaptive policy pathway approach (Haasnoot, Kwakkel et al. 2013) being used in the Dutch Delta Program. The policy problem is formulated as a multi-objective robust optimization problem (Kwakkel, Haasnoot et al. 2014). We solve the multi-objective robust optimization problem using several alternative robustness metrics, including both satisficing robustness metrics and regret based robustness metrics. Satisficing robustness metrics focus on the performance of candidate plans across a large ensemble of plausible futures. Regret based robustness metrics compare the performance of a candidate plan with the performance of other candidate plans across a large ensemble of plausible futures. Initial results suggest that the simplest satisficing metric, inspired by the signal to noise ratio, results in very risk averse solutions. Other satisficing metrics, which handle the average performance and the dispersion around the average separately, provide substantial additional insights into the trade off between the average performance, and the dispersion around this average. In contrast, the regret-based metrics enhance insight into the relative merits of candidate plans, while being less clear on the average performance or the dispersion around this performance. These results suggest that it is beneficial to use multiple robustness metrics when doing a robust decision analysis study. Haasnoot, M., J. H. Kwakkel, W. E. Walker and J. Ter Maat (2013). "Dynamic Adaptive Policy Pathways: A New Method for Crafting Robust Decisions for a Deeply Uncertain World." Global Environmental Change 23(2): 485-498. Kwakkel, J. H., M. Haasnoot and W. E. Walker (2014). "Developing Dynamic Adaptive Policy Pathways: A computer-assisted approach for developing adaptive strategies for a deeply uncertain world." Climatic Change.

Exploring complex dynamics in multi agent-based intelligent systems: Theoretical and experimental approaches using the Multi Agent-based Behavioral Economic Landscape (MABEL) model

NASA Astrophysics Data System (ADS)

Alexandridis, Konstantinos T.

This dissertation adopts a holistic and detailed approach to modeling spatially explicit agent-based artificial intelligent systems, using the Multi Agent-based Behavioral Economic Landscape (MABEL) model. The research questions that addresses stem from the need to understand and analyze the real-world patterns and dynamics of land use change from a coupled human-environmental systems perspective. Describes the systemic, mathematical, statistical, socio-economic and spatial dynamics of the MABEL modeling framework, and provides a wide array of cross-disciplinary modeling applications within the research, decision-making and policy domains. Establishes the symbolic properties of the MABEL model as a Markov decision process, analyzes the decision-theoretic utility and optimization attributes of agents towards comprising statistically and spatially optimal policies and actions, and explores the probabilogic character of the agents' decision-making and inference mechanisms via the use of Bayesian belief and decision networks. Develops and describes a Monte Carlo methodology for experimental replications of agent's decisions regarding complex spatial parcel acquisition and learning. Recognizes the gap on spatially-explicit accuracy assessment techniques for complex spatial models, and proposes an ensemble of statistical tools designed to address this problem. Advanced information assessment techniques such as the Receiver-Operator Characteristic curve, the impurity entropy and Gini functions, and the Bayesian classification functions are proposed. The theoretical foundation for modular Bayesian inference in spatially-explicit multi-agent artificial intelligent systems, and the ensembles of cognitive and scenario assessment modular tools build for the MABEL model are provided. Emphasizes the modularity and robustness as valuable qualitative modeling attributes, and examines the role of robust intelligent modeling as a tool for improving policy-decisions related to land use change. Finally, the major contributions to the science are presented along with valuable directions for future research.

Non-negative Tensor Factorization for Robust Exploratory Big-Data Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alexandrov, Boian; Vesselinov, Velimir Valentinov; Djidjev, Hristo Nikolov

Currently, large multidimensional datasets are being accumulated in almost every field. Data are: (1) collected by distributed sensor networks in real-time all over the globe, (2) produced by large-scale experimental measurements or engineering activities, (3) generated by high-performance simulations, and (4) gathered by electronic communications and socialnetwork activities, etc. Simultaneous analysis of these ultra-large heterogeneous multidimensional datasets is often critical for scientific discoveries, decision-making, emergency response, and national and global security. The importance of such analyses mandates the development of the next-generation of robust machine learning (ML) methods and tools for bigdata exploratory analysis.

A robust optimization model for distribution and evacuation in the disaster response phase

NASA Astrophysics Data System (ADS)

Fereiduni, Meysam; Shahanaghi, Kamran

2017-03-01

Natural disasters, such as earthquakes, affect thousands of people and can cause enormous financial loss. Therefore, an efficient response immediately following a natural disaster is vital to minimize the aforementioned negative effects. This research paper presents a network design model for humanitarian logistics which will assist in location and allocation decisions for multiple disaster periods. At first, a single-objective optimization model is presented that addresses the response phase of disaster management. This model will help the decision makers to make the most optimal choices in regard to location, allocation, and evacuation simultaneously. The proposed model also considers emergency tents as temporary medical centers. To cope with the uncertainty and dynamic nature of disasters, and their consequences, our multi-period robust model considers the values of critical input data in a set of various scenarios. Second, because of probable disruption in the distribution infrastructure (such as bridges), the Monte Carlo simulation is used for generating related random numbers and different scenarios; the p-robust approach is utilized to formulate the new network. The p-robust approach can predict possible damages along pathways and among relief bases. We render a case study of our robust optimization approach for Tehran's plausible earthquake in region 1. Sensitivity analysis' experiments are proposed to explore the effects of various problem parameters. These experiments will give managerial insights and can guide DMs under a variety of conditions. Then, the performances of the "robust optimization" approach and the "p-robust optimization" approach are evaluated. Intriguing results and practical insights are demonstrated by our analysis on this comparison.

Designing Visual Aids That Promote Risk Literacy: A Systematic Review of Health Research and Evidence-Based Design Heuristics.

PubMed

Garcia-Retamero, Rocio; Cokely, Edward T

2017-06-01

Background Effective risk communication is essential for informed decision making. Unfortunately, many people struggle to understand typical risk communications because they lack essential decision-making skills. Objective The aim of this study was to review the literature on the effect of numeracy on risk literacy, decision making, and health outcomes, and to evaluate the benefits of visual aids in risk communication. Method We present a conceptual framework describing the influence of numeracy on risk literacy, decision making, and health outcomes, followed by a systematic review of the benefits of visual aids in risk communication for people with different levels of numeracy and graph literacy. The systematic review covers scientific research published between January 1995 and April 2016, drawn from the following databases: Web of Science, PubMed, PsycINFO, ERIC, Medline, and Google Scholar. Inclusion criteria were investigation of the effect of numeracy and/or graph literacy, and investigation of the effect of visual aids or comparison of their effect with that of numerical information. Thirty-six publications met the criteria, providing data on 27,885 diverse participants from 60 countries. Results Transparent visual aids robustly improved risk understanding in diverse individuals by encouraging thorough deliberation, enhancing cognitive self-assessment, and reducing conceptual biases in memory. Improvements in risk understanding consistently produced beneficial changes in attitudes, behavioral intentions, trust, and healthy behaviors. Visual aids were found to be particularly beneficial for vulnerable and less skilled individuals. Conclusion Well-designed visual aids tend to be highly effective tools for improving informed decision making among diverse decision makers. We identify five categories of practical, evidence-based guidelines for heuristic evaluation and design of effective visual aids.

Improving robustness and computational efficiency using modern C++

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paterno, M.; Kowalkowski, J.; Green, C.

2014-01-01

For nearly two decades, the C++ programming language has been the dominant programming language for experimental HEP. The publication of ISO/IEC 14882:2011, the current version of the international standard for the C++ programming language, makes available a variety of language and library facilities for improving the robustness, expressiveness, and computational efficiency of C++ code. However, much of the C++ written by the experimental HEP community does not take advantage of the features of the language to obtain these benefits, either due to lack of familiarity with these features or concern that these features must somehow be computationally inefficient. In thismore » paper, we address some of the features of modern C+-+, and show how they can be used to make programs that are both robust and computationally efficient. We compare and contrast simple yet realistic examples of some common implementation patterns in C, currently-typical C++, and modern C++, and show (when necessary, down to the level of generated assembly language code) the quality of the executable code produced by recent C++ compilers, with the aim of allowing the HEP community to make informed decisions on the costs and benefits of the use of modern C++.« less

Identification, assessment and management of "endocrine disruptors" in wildlife in the EU substance legislation--discussion paper from the German Federal Environment Agency (UBA).

PubMed

Frische, Tobias; Bachmann, Jean; Frein, Daniel; Juffernholz, Tanja; Kehrer, Anja; Klein, Anita; Maack, Gerd; Stock, Frauke; Stolzenberg, Hans-Christian; Thierbach, Claudia; Walter-Rohde, Susanne

2013-12-16

A discussion paper was developed by a panel of experts of the German Federal Environment Agency (UBA) contributing to the on-going debate on the identification, assessment and management of endocrine disruptors with a view to protect wildlife according to the EU substance legislation (plant protection products, biocides, industrial chemicals). Based on a critical synthesis of the state-of-the-art regarding regulatory requirements, testing methods, assessment schemes, decision-making criteria and risk management options, we advise an appropriate and consistent implementation of this important subject into existing chemicals legislation in Europe. Our proposal for a balanced risk management of endocrine disruptors essentially advocates transparent regulatory decision making based on a scientifically robust weight of evidence approach and an adequate risk management consistent across different legislations. With respect to the latter, a more explicit consideration of the principle of proportionality of regulatory decision making and socio-economic benefits in the on-going debate is further encouraged. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Adherence to infection prevention and control guidelines: A vignette-based study of decision-making and risk-taking in young adults with cystic fibrosis.

PubMed

Bowmer, Grace; Latchford, Gary; Duff, Alistair; Denton, Miles; Dye, Louise; Lawton, Clare; Lee, Tim

2017-01-01

Balancing cystic fibrosis (CF) care with demands of normal life is associated with decreased adherence to infection prevention and control (IPC) guidelines. Adults with CF, aged 18-25years, were invited to participate via UK CF Trust social media platforms. An online survey evaluated participants' decision-making in nine clinician-rated vignettes and assessed the perceived influence of infection-related information sources. Participants (n=87, mean 21.4years [SD=2.45]; 75% female) were less likely to engage in the high-risk scenarios, although demonstrated greater awareness of cross-infection than environmental risks. Associations between risk-perception and willingness to participate in five vignette-based hypothetical activities were significant (p<0.05). Thematic analysis emphasised influences of past experience and a need to achieve good quality of life. Knowledge gaps were evident. People with CF make decisions that discriminate between risk-levels but are not always based on robust knowledge. They also show some inclination towards engaging in risky behaviours. Copyright © 2016 European Cystic Fibrosis Society. Published by Elsevier B.V. All rights reserved.

Functional Genetic Variation in Dopamine Signaling Moderates Prefrontal Cortical Activity During Risky Decision Making.

PubMed

Kohno, Milky; Nurmi, Erika L; Laughlin, Christopher P; Morales, Angelica M; Gail, Emma H; Hellemann, Gerhard S; London, Edythe D

2016-02-01

Brain imaging has revealed links between prefrontal activity during risky decision-making and striatal dopamine receptors. Specifically, striatal dopamine D2-like receptor availability is correlated with risk-taking behavior and sensitivity of prefrontal activation to risk in the Balloon Analogue Risk Task (BART). The extent to which these associations, involving a single neurochemical measure, reflect more general effects of dopaminergic functioning on risky decision making, however, is unknown. Here, 65 healthy participants provided genotypes and performed the BART during functional magnetic resonance imaging. For each participant, dopamine function was assessed using a gene composite score combining known functional variation across five genes involved in dopaminergic signaling: DRD2, DRD3, DRD4, DAT1, and COMT. The gene composite score was negatively related to dorsolateral prefrontal cortical function during risky decision making, and nonlinearly related to earnings on the task. Iterative permutations of all possible allelic variations (7777 allelic combinations) was tested on brain function in an independently defined region of the prefrontal cortex and confirmed empirical validity of the composite score, which yielded stronger association than 95% of all other possible combinations. The gene composite score also accounted for a greater proportion of variability in neural and behavioral measures than the independent effects of each gene variant, indicating that the combined effects of functional dopamine pathway genes can provide a robust assessment, presumably reflecting the cumulative and potentially interactive effects on brain function. Our findings support the view that the links between dopaminergic signaling, prefrontal function, and decision making vary as a function of dopamine signaling capacity.

Reinforcement learning signals in the human striatum distinguish learners from nonlearners during reward-based decision making.

PubMed

Schönberg, Tom; Daw, Nathaniel D; Joel, Daphna; O'Doherty, John P

2007-11-21

The computational framework of reinforcement learning has been used to forward our understanding of the neural mechanisms underlying reward learning and decision-making behavior. It is known that humans vary widely in their performance in decision-making tasks. Here, we used a simple four-armed bandit task in which subjects are almost evenly split into two groups on the basis of their performance: those who do learn to favor choice of the optimal action and those who do not. Using models of reinforcement learning we sought to determine the neural basis of these intrinsic differences in performance by scanning both groups with functional magnetic resonance imaging. We scanned 29 subjects while they performed the reward-based decision-making task. Our results suggest that these two groups differ markedly in the degree to which reinforcement learning signals in the striatum are engaged during task performance. While the learners showed robust prediction error signals in both the ventral and dorsal striatum during learning, the nonlearner group showed a marked absence of such signals. Moreover, the magnitude of prediction error signals in a region of dorsal striatum correlated significantly with a measure of behavioral performance across all subjects. These findings support a crucial role of prediction error signals, likely originating from dopaminergic midbrain neurons, in enabling learning of action selection preferences on the basis of obtained rewards. Thus, spontaneously observed individual differences in decision making performance demonstrate the suggested dependence of this type of learning on the functional integrity of the dopaminergic striatal system in humans.

Decision strategies for handling the uncertainty of future extreme rainfall under the influence of climate change.

PubMed

Gregersen, I B; Arnbjerg-Nielsen, K

2012-01-01

Several extraordinary rainfall events have occurred in Denmark within the last few years. For each event, problems in urban areas occurred as the capacity of the existing drainage systems were exceeded. Adaptation to climate change is necessary but also very challenging as urban drainage systems are characterized by long technical lifetimes and high, unrecoverable construction costs. One of the most important barriers for the initiation and implementation of the adaptation strategies is therefore the uncertainty when predicting the magnitude of the extreme rainfall in the future. This challenge is explored through the application and discussion of three different theoretical decision support strategies: the precautionary principle, the minimax strategy and Bayesian decision support. The reviewed decision support strategies all proved valuable for addressing the identified uncertainties, at best applied together as they all yield information that improved decision making and thus enabled more robust decisions.

Public Deliberation as a Teaching Andragogy: Implications for Adult Student Learning from a Doctoral Higher Education Policy Course

ERIC Educational Resources Information Center

Johnson, Matthew; Partlo, Margaret; Hullender, Tammy; Akanwa, Emmanuel; Burke, Heather; Todd, Jerry; Alwood, Christine

2014-01-01

Public deliberation provides an inclusive and robust mechanism for making shared decisions in community and political settings; however, its application to teaching and learning remains underutilized (McMillan & Harriger, 2007). This manuscript reports on a case study of the use of public deliberation as a teaching andragogy in a graduate…

Computational Cognition and Robust Decision Making

DTIC Science & Technology

2013-03-06

much more powerful neuromorphic chips than current state of the art. L. Chua 10 DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution 2...Cognition Program DARPA (Gill Pratt) • Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) Program IARPA (Brad Minnery...2012 - Four projects at SNU and KAIST co-funded with AOARD DARPA SyNAPSE Program: - Design, fabrication, and demonstration of neuromorphic

A framework for developing safe and effective large-fire response in a new fire management paradigm

Treesearch

Christopher J. Dunn; Matthew P. Thompson; David E. Calkin

2017-01-01

The impacts of wildfires have increased in recent decades because of historical forest and fire management, a rapidly changing climate, and an increasingly populated wildland urban interface. This increasingly complex fire environment highlights the importance of developing robust tools to support risk-informed decision making. While tools have been developed to aid...

Group decision-making approach for flood vulnerability identification using the fuzzy VIKOR method

NASA Astrophysics Data System (ADS)

Lee, G.; Jun, K. S.; Cung, E. S.

2014-09-01

This study proposes an improved group decision making (GDM) framework that combines VIKOR method with fuzzified data to quantify the spatial flood vulnerability including multi-criteria evaluation indicators. In general, GDM method is an effective tool for formulating a compromise solution that involves various decision makers since various stakeholders may have different perspectives on their flood risk/vulnerability management responses. The GDM approach is designed to achieve consensus building that reflects the viewpoints of each participant. The fuzzy VIKOR method was developed to solve multi-criteria decision making (MCDM) problems with conflicting and noncommensurable criteria. This comprising method can be used to obtain a nearly ideal solution according to all established criteria. Triangular fuzzy numbers are used to consider the uncertainty of weights and the crisp data of proxy variables. This approach can effectively propose some compromising decisions by combining the GDM method and fuzzy VIKOR method. The spatial flood vulnerability of the south Han River using the GDM approach combined with the fuzzy VIKOR method was compared with the results from general MCDM methods, such as the fuzzy TOPSIS and classical GDM methods, such as those developed by Borda, Condorcet, and Copeland. The evaluated priorities were significantly dependent on the employed decision-making method. The proposed fuzzy GDM approach can reduce the uncertainty in the data confidence and weight derivation techniques. Thus, the combination of the GDM approach with the fuzzy VIKOR method can provide robust prioritization because it actively reflects the opinions of various groups and considers uncertainty in the input data.

Beyond negative valence: 2-week administration of a serotonergic antidepressant enhances both reward and effort learning signals

PubMed Central

Kolling, Nils; Nelissen, Natalie; Browning, Michael; Rushworth, Matthew F. S.; Harmer, Catherine J.

2017-01-01

To make good decisions, humans need to learn about and integrate different sources of appetitive and aversive information. While serotonin has been linked to value-based decision-making, its role in learning is less clear, with acute manipulations often producing inconsistent results. Here, we show that when the effects of a selective serotonin reuptake inhibitor (SSRI, citalopram) are studied over longer timescales, learning is robustly improved. We measured brain activity with functional magnetic resonance imaging (fMRI) in volunteers as they performed a concurrent appetitive (money) and aversive (effort) learning task. We found that 2 weeks of citalopram enhanced reward and effort learning signals in a widespread network of brain regions, including ventromedial prefrontal and anterior cingulate cortex. At a behavioral level, this was accompanied by more robust reward learning. This suggests that serotonin can modulate the ability to learn via a mechanism that is independent of stimulus valence. Such effects may partly underlie SSRIs’ impact in treating psychological illnesses. Our results highlight both a specific function in learning for serotonin and the importance of studying its role across longer timescales. PMID:28207733

Transforming clinical practice guidelines and clinical pathways into fast-and-frugal decision trees to improve clinical care strategies.

PubMed

Djulbegovic, Benjamin; Hozo, Iztok; Dale, William

2018-02-27

Contemporary delivery of health care is inappropriate in many ways, largely due to suboptimal Q5 decision-making. A typical approach to improve practitioners' decision-making is to develop evidence-based clinical practice guidelines (CPG) by guidelines panels, who are instructed to use their judgments to derive practice recommendations. However, mechanisms for the formulation of guideline judgments remains a "black-box" operation-a process with defined inputs and outputs but without sufficient knowledge of its internal workings. Increased explicitness and transparency in the process can be achieved by implementing CPG as clinical pathways (CPs) (also known as clinical algorithms or flow-charts). However, clinical recommendations thus derived are typically ad hoc and developed by experts in a theory-free environment. As any recommendation can be right (true positive or negative), or wrong (false positive or negative), the lack of theoretical structure precludes the quantitative assessment of the management strategies recommended by CPGs/CPs. To realize the full potential of CPGs/CPs, they need to be placed on more solid theoretical grounds. We believe this potential can be best realized by converting CPGs/CPs within the heuristic theory of decision-making, often implemented as fast-and-frugal (FFT) decision trees. This is possible because FFT heuristic strategy of decision-making can be linked to signal detection theory, evidence accumulation theory, and a threshold model of decision-making, which, in turn, allows quantitative analysis of the accuracy of clinical management strategies. Fast-and-frugal provides a simple and transparent, yet solid and robust, methodological framework connecting decision science to clinical care, a sorely needed missing link between CPGs/CPs and patient outcomes. We therefore advocate that all guidelines panels express their recommendations as CPs, which in turn should be converted into FFTs to guide clinical care. © 2018 John Wiley & Sons, Ltd.

Boosting medical diagnostics by pooling independent judgments

PubMed Central

Kurvers, Ralf H. J. M.; Herzog, Stefan M.; Hertwig, Ralph; Krause, Jens; Carney, Patricia A.; Bogart, Andy; Argenziano, Giuseppe; Zalaudek, Iris; Wolf, Max

2016-01-01

Collective intelligence refers to the ability of groups to outperform individual decision makers when solving complex cognitive problems. Despite its potential to revolutionize decision making in a wide range of domains, including medical, economic, and political decision making, at present, little is known about the conditions underlying collective intelligence in real-world contexts. We here focus on two key areas of medical diagnostics, breast and skin cancer detection. Using a simulation study that draws on large real-world datasets, involving more than 140 doctors making more than 20,000 diagnoses, we investigate when combining the independent judgments of multiple doctors outperforms the best doctor in a group. We find that similarity in diagnostic accuracy is a key condition for collective intelligence: Aggregating the independent judgments of doctors outperforms the best doctor in a group whenever the diagnostic accuracy of doctors is relatively similar, but not when doctors’ diagnostic accuracy differs too much. This intriguingly simple result is highly robust and holds across different group sizes, performance levels of the best doctor, and collective intelligence rules. The enabling role of similarity, in turn, is explained by its systematic effects on the number of correct and incorrect decisions of the best doctor that are overruled by the collective. By identifying a key factor underlying collective intelligence in two important real-world contexts, our findings pave the way for innovative and more effective approaches to complex real-world decision making, and to the scientific analyses of those approaches. PMID:27432950

Integrating uncertainty into public energy research and development decisions

NASA Astrophysics Data System (ADS)

Anadón, Laura Díaz; Baker, Erin; Bosetti, Valentina

2017-05-01

Public energy research and development (R&D) is recognized as a key policy tool for transforming the world's energy system in a cost-effective way. However, managing the uncertainty surrounding technological change is a critical challenge for designing robust and cost-effective energy policies. The design of such policies is particularly important if countries are going to both meet the ambitious greenhouse-gas emissions reductions goals set by the Paris Agreement and achieve the required harmonization with the broader set of objectives dictated by the Sustainable Development Goals. The complexity of informing energy technology policy requires, and is producing, a growing collaboration between different academic disciplines and practitioners. Three analytical components have emerged to support the integration of technological uncertainty into energy policy: expert elicitations, integrated assessment models, and decision frameworks. Here we review efforts to incorporate all three approaches to facilitate public energy R&D decision-making under uncertainty. We highlight emerging insights that are robust across elicitations, models, and frameworks, relating to the allocation of public R&D investments, and identify gaps and challenges that remain.

Decisions reduce sensitivity to subsequent information.

PubMed

Bronfman, Zohar Z; Brezis, Noam; Moran, Rani; Tsetsos, Konstantinos; Donner, Tobias; Usher, Marius

2015-07-07

Behavioural studies over half a century indicate that making categorical choices alters beliefs about the state of the world. People seem biased to confirm previous choices, and to suppress contradicting information. These choice-dependent biases imply a fundamental bound of human rationality. However, it remains unclear whether these effects extend to lower level decisions, and only little is known about the computational mechanisms underlying them. Building on the framework of sequential-sampling models of decision-making, we developed novel psychophysical protocols that enable us to dissect quantitatively how choices affect the way decision-makers accumulate additional noisy evidence. We find robust choice-induced biases in the accumulation of abstract numerical (experiment 1) and low-level perceptual (experiment 2) evidence. These biases deteriorate estimations of the mean value of the numerical sequence (experiment 1) and reduce the likelihood to revise decisions (experiment 2). Computational modelling reveals that choices trigger a reduction of sensitivity to subsequent evidence via multiplicative gain modulation, rather than shifting the decision variable towards the chosen alternative in an additive fashion. Our results thus show that categorical choices alter the evidence accumulation mechanism itself, rather than just its outcome, rendering the decision-maker less sensitive to new information. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Sampling design considerations for demographic studies: a case of colonial seabirds

USGS Publications Warehouse

Kendall, William L.; Converse, Sarah J.; Doherty, Paul F.; Naughton, Maura B.; Anders, Angela; Hines, James E.; Flint, Elizabeth

2009-01-01

For the purposes of making many informed conservation decisions, the main goal for data collection is to assess population status and allow prediction of the consequences of candidate management actions. Reducing the bias and variance of estimates of population parameters reduces uncertainty in population status and projections, thereby reducing the overall uncertainty under which a population manager must make a decision. In capture-recapture studies, imperfect detection of individuals, unobservable life-history states, local movement outside study areas, and tag loss can cause bias or precision problems with estimates of population parameters. Furthermore, excessive disturbance to individuals during capture?recapture sampling may be of concern because disturbance may have demographic consequences. We address these problems using as an example a monitoring program for Black-footed Albatross (Phoebastria nigripes) and Laysan Albatross (Phoebastria immutabilis) nesting populations in the northwestern Hawaiian Islands. To mitigate these estimation problems, we describe a synergistic combination of sampling design and modeling approaches. Solutions include multiple capture periods per season and multistate, robust design statistical models, dead recoveries and incidental observations, telemetry and data loggers, buffer areas around study plots to neutralize the effect of local movements outside study plots, and double banding and statistical models that account for band loss. We also present a variation on the robust capture?recapture design and a corresponding statistical model that minimizes disturbance to individuals. For the albatross case study, this less invasive robust design was more time efficient and, when used in combination with a traditional robust design, reduced the standard error of detection probability by 14% with only two hours of additional effort in the field. These field techniques and associated modeling approaches are applicable to studies of most taxa being marked and in some cases have individually been applied to studies of birds, fish, herpetofauna, and mammals.

Patient-reported outcomes in randomised controlled trials of colorectal cancer: an analysis determining the availability of robust data to inform clinical decision-making

PubMed Central

Whale, Katie; Fish, Daniel; Fayers, Peter; Cafaro, Valentina; Pusic, Andrea; Blazeby, Jane M.; Efficace, Fabio

2016-01-01

Purpose Randomised controlled trials (RCTs) are the most robust study design measuring outcomes of colorectal cancer (CRC) treatments, but to influence clinical practice trial design and reporting of patient-reported outcomes (PROs) must be of high quality. Objectives of this study were as follows: to examine the quality of PRO reporting in RCTs of CRC treatment; to assess the availability of robust data to inform clinical decision-making; and to investigate whether quality of reporting improved over time. Methods A systematic review from January 2004–February 2012 identified RCTs of CRC treatment describing PROs. Relevant abstracts were screened and manuscripts obtained. Methodological quality was assessed using International Society for Quality of Life Research—patient-reported outcome reporting standards. Changes in reporting quality over time were established by comparison with previous data, and risk of bias was assessed with the Cochrane risk of bias tool. Results Sixty-six RCTs were identified, seven studies (10 %) reported survival benefit favouring the experimental treatment, 35 trials (53 %) identified differences in PROs between treatment groups, and the clinical significance of these differences was discussed in 19 studies (29 %). The most commonly reported treatment type was chemotherapy (n = 45; 68 %). Improvements over time in key methodological issues including the documentation of missing data and the discussion of the clinical significance of PROs were found. Thirteen trials (20 %) had high-quality reporting. Conclusions Whilst improvements in PRO quality reporting over time were found, several recent studies still fail to robustly inform clinical practice. Quality of PRO reporting must continue to improve to maximise the clinical impact of PRO findings. PMID:25910987

Robust optimization modelling with applications to industry and environmental problems

NASA Astrophysics Data System (ADS)

Chaerani, Diah; Dewanto, Stanley P.; Lesmana, Eman

2017-10-01

Robust Optimization (RO) modeling is one of the existing methodology for handling data uncertainty in optimization problem. The main challenge in this RO methodology is how and when we can reformulate the robust counterpart of uncertain problems as a computationally tractable optimization problem or at least approximate the robust counterpart by a tractable problem. Due to its definition the robust counterpart highly depends on how we choose the uncertainty set. As a consequence we can meet this challenge only if this set is chosen in a suitable way. The development on RO grows fast, since 2004, a new approach of RO called Adjustable Robust Optimization (ARO) is introduced to handle uncertain problems when the decision variables must be decided as a ”wait and see” decision variables. Different than the classic Robust Optimization (RO) that models decision variables as ”here and now”. In ARO, the uncertain problems can be considered as a multistage decision problem, thus decision variables involved are now become the wait and see decision variables. In this paper we present the applications of both RO and ARO. We present briefly all results to strengthen the importance of RO and ARO in many real life problems.

How decisions happen: focal points and blind spots in interdependent decision making.

PubMed

Halevy, Nir; Chou, Eileen Y

2014-03-01

Decision makers often simplify decision problems by ignoring readily available information. The current multimethod research investigated which types of information about interdependence situations are psychologically prominent to decision makers and which tend to go unnoticed. Study 1 used eye-tracking measures to investigate how decision makers allocate their attention in interdependence situations and revealed that individuals fixated on mutual cooperation earlier and longer as compared with alternative combinations of strategies and outcomes. In addition, participants' behavioral cooperation was consistent with their attention allocation. Study 2 introduced a novel information-search paradigm: Participants exchanged yes/no questions and answers to discover which of 25 different games their counterpart chose. Analyzing the contents of participants' questions showed that, consistent with Study 1, participants focused primarily on desirable outcomes and symmetric behavioral choices. Study 3 revealed that outcome desirability is a robust basis of psychological prominence across different types of social relations; in contrast, the psychological prominence of symmetry was moderated by the nature of social relations. Study 4 revealed that whether different bases of psychological prominence directed individuals' attention to the same aspects of the decision-making task moderated the effect of information availability on decision latency and cooperation rates. Taken together, these findings contribute to the mapping of bounded rationality, demonstrate how people think about their interdependence, and enhance our understanding of how decisions happen. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Analysis of decision fusion algorithms in handling uncertainties for integrated health monitoring systems

NASA Astrophysics Data System (ADS)

Zein-Sabatto, Saleh; Mikhail, Maged; Bodruzzaman, Mohammad; DeSimio, Martin; Derriso, Mark; Behbahani, Alireza

2012-06-01

It has been widely accepted that data fusion and information fusion methods can improve the accuracy and robustness of decision-making in structural health monitoring systems. It is arguably true nonetheless, that decision-level is equally beneficial when applied to integrated health monitoring systems. Several decisions at low-levels of abstraction may be produced by different decision-makers; however, decision-level fusion is required at the final stage of the process to provide accurate assessment about the health of the monitored system as a whole. An example of such integrated systems with complex decision-making scenarios is the integrated health monitoring of aircraft. Thorough understanding of the characteristics of the decision-fusion methodologies is a crucial step for successful implementation of such decision-fusion systems. In this paper, we have presented the major information fusion methodologies reported in the literature, i.e., probabilistic, evidential, and artificial intelligent based methods. The theoretical basis and characteristics of these methodologies are explained and their performances are analyzed. Second, candidate methods from the above fusion methodologies, i.e., Bayesian, Dempster-Shafer, and fuzzy logic algorithms are selected and their applications are extended to decisions fusion. Finally, fusion algorithms are developed based on the selected fusion methods and their performance are tested on decisions generated from synthetic data and from experimental data. Also in this paper, a modeling methodology, i.e. cloud model, for generating synthetic decisions is presented and used. Using the cloud model, both types of uncertainties; randomness and fuzziness, involved in real decision-making are modeled. Synthetic decisions are generated with an unbiased process and varying interaction complexities among decisions to provide for fair performance comparison of the selected decision-fusion algorithms. For verification purposes, implementation results of the developed fusion algorithms on structural health monitoring data collected from experimental tests are reported in this paper.

Mainstreaming Climate Change: Recent and Ongoing Efforts to Understand, Improve, and Expand Consideration of Climate Change in Federal Water Resources Planning

NASA Astrophysics Data System (ADS)

Ferguson, I. M.; McGuire, M.; Broman, D.; Gangopadhyay, S.

2017-12-01

The Bureau of Reclamation is a Federal agency tasked with developing and managing water supply and hydropower projects in the Western U.S. Climate and hydrologic variability and change significantly impact management actions and outcomes across Reclamation's programs and initiatives, including water resource planning and operations, infrastructure design and maintenance, hydropower generation, and ecosystem restoration, among others. Planning, design, and implementation of these programs therefore requires consideration of future climate and hydrologic conditions will impact program objectives. Over the past decade, Reclamation and other Federal agencies have adopted new guidelines, directives, and mandates that require consideration of climate change in water resources planning and decision making. Meanwhile, the scientific community has developed a large number of climate projections, along with an array of models, methods, and tools to facilitate consideration of climate projections in planning and decision making. However, water resources engineers, planners, and decision makers continue to face challenges regarding how best to use the available data and tools to support major decisions, including decisions regarding infrastructure investments and long-term operating criteria. This presentation will discuss recent and ongoing research towards understanding, improving, and expanding consideration of climate projections and related uncertainties in Federal water resources planning and decision making. These research efforts address a variety of challenges, including: How to choose between available climate projection datasets and related methods, models, and tools—many of which are considered experimental or research tools? How to select an appropriate decision framework when design or operating alternatives may differ between climate scenarios? How to effectively communicate results of a climate impacts analysis to decision makers? And, how to improve robustness and resilience of water resources systems in the face of significant uncertainty? Discussion will focus on the intersection between technical challenges and decision making paradigms and the need for improved scientist-decision maker engagement through the lens of this Federal water management agency.

From the microscope to the macroscopic: changing from the bench to portfolio management

PubMed Central

Sachs, Michael

2017-01-01

A role in portfolio management is ideal for individuals who enjoy tackling challenges that have both technical and business components. Portfolio management provides objective insights and analytics to support research and development decision making and planning. Successful practitioners usually have strong analytical abilities developed from a background in either science or business. Portfolio managers often advise key decision makers at both the team and senior management level and thus require robust oral, written, and interpersonal communication skills. Day-to-day tasks are rarely the same, and comfort with change and the unknown is essential. Here I will discuss my experience as a portfolio manager in a larger biopharmaceutical company and the skills from academic research I leveraged to make the transition. PMID:29084911

Robustness Metrics: How Are They Calculated, When Should They Be Used and Why Do They Give Different Results?

NASA Astrophysics Data System (ADS)

McPhail, C.; Maier, H. R.; Kwakkel, J. H.; Giuliani, M.; Castelletti, A.; Westra, S.

2018-02-01

Robustness is being used increasingly for decision analysis in relation to deep uncertainty and many metrics have been proposed for its quantification. Recent studies have shown that the application of different robustness metrics can result in different rankings of decision alternatives, but there has been little discussion of what potential causes for this might be. To shed some light on this issue, we present a unifying framework for the calculation of robustness metrics, which assists with understanding how robustness metrics work, when they should be used, and why they sometimes disagree. The framework categorizes the suitability of metrics to a decision-maker based on (1) the decision-context (i.e., the suitability of using absolute performance or regret), (2) the decision-maker's preferred level of risk aversion, and (3) the decision-maker's preference toward maximizing performance, minimizing variance, or some higher-order moment. This article also introduces a conceptual framework describing when relative robustness values of decision alternatives obtained using different metrics are likely to agree and disagree. This is used as a measure of how "stable" the ranking of decision alternatives is when determined using different robustness metrics. The framework is tested on three case studies, including water supply augmentation in Adelaide, Australia, the operation of a multipurpose regulated lake in Italy, and flood protection for a hypothetical river based on a reach of the river Rhine in the Netherlands. The proposed conceptual framework is confirmed by the case study results, providing insight into the reasons for disagreements between rankings obtained using different robustness metrics.

Dopaminergic Modulation of Risky Decision-Making

PubMed Central

Simon, Nicholas W.; Montgomery, Karienn S.; Beas, Blanca S.; Mitchell, Marci R.; LaSarge, Candi L.; Mendez, Ian A.; Bañuelos, Cristina; Vokes, Colin M.; Taylor, Aaron B.; Haberman, Rebecca P.; Bizon, Jennifer L.; Setlow, Barry

2012-01-01

Many psychiatric disorders are characterized by abnormal risky decision-making and dysregulated dopamine receptor expression. The current study was designed to determine how different dopamine receptor subtypes modulate risk-taking in young adult rats, using a “Risky Decision-making Task” that involves choices between small “safe” rewards and large “risky” rewards accompanied by adverse consequences. Rats showed considerable, stable individual differences in risk preference in the task, which were not related to multiple measures of reward motivation, anxiety, or pain sensitivity. Systemic activation of D2-like receptors robustly attenuated risk-taking, whereas drugs acting on D1-like receptors had no effect. Systemic amphetamine also reduced risk-taking, an effect which was attenuated by D2-like (but not D1-like) receptor blockade. Dopamine receptor mRNA expression was evaluated in a separate cohort of drug-naive rats characterized in the task. D1 mRNA expression in both nucleus accumbens shell and insular cortex was positively associated with risk-taking, while D2 mRNA expression in orbitofrontal and medial prefrontal cortex predicted risk preference in opposing nonlinear patterns. Additionally, lower levels of D2 mRNA in dorsal striatum were associated with greater risk-taking. These data strongly implicate dopamine signaling in prefrontal corticalstriatal circuitry in modulating decision-making processes involving integration of reward information with risks of adverse consequences. PMID:22131407

A cumulative energy demand indicator (CED), life cycle based, for industrial waste management decision making.

PubMed

Puig, Rita; Fullana-I-Palmer, Pere; Baquero, Grau; Riba, Jordi-Roger; Bala, Alba

2013-12-01

Life cycle thinking is a good approach to be used for environmental decision-support, although the complexity of the Life Cycle Assessment (LCA) studies sometimes prevents their wide use. The purpose of this paper is to show how LCA methodology can be simplified to be more useful for certain applications. In order to improve waste management in Catalonia (Spain), a Cumulative Energy Demand indicator (LCA-based) has been used to obtain four mathematical models to help the government in the decision of preventing or allowing a specific waste from going out of the borders. The conceptual equations and all the subsequent developments and assumptions made to obtain the simplified models are presented. One of the four models is discussed in detail, presenting the final simplified equation to be subsequently used by the government in decision making. The resulting model has been found to be scientifically robust, simple to implement and, above all, fulfilling its purpose: the limitation of waste transport out of Catalonia unless the waste recovery operations are significantly better and justify this transport. Copyright © 2013. Published by Elsevier Ltd.

Fuzzy Behavior-Based Navigation for Planetary

NASA Technical Reports Server (NTRS)

Tunstel, Edward; Danny, Harrison; Lippincott, Tanya; Jamshidi, Mo

1997-01-01

Adaptive behavioral capabilities are necessary for robust rover navigation in unstructured and partially-mapped environments. A control approach is described which exploits the approximate reasoning capability of fuzzy logic to produce adaptive motion behavior. In particular, a behavior-based architecture for hierarchical fuzzy control of microrovers is presented. Its structure is described, as well as mechanisms of control decision-making which give rise to adaptive behavior. Control decisions for local navigation result from a consensus of recommendations offered only by behaviors that are applicable to current situations. Simulation predicts the navigation performance on a microrover in simplified Mars-analog terrain.

The influence of patient portals on users' decision making is insufficiently investigated: A systematic methodological review.

PubMed

Fraccaro, Paolo; Vigo, Markel; Balatsoukas, Panagiotis; Buchan, Iain E; Peek, Niels; van der Veer, Sabine N

2018-03-01

Patient portals are considered valuable conduits for supporting patients' self-management. However, it is unknown why they often fail to impact on health care processes and outcomes. This may be due to a scarcity of robust studies focusing on the steps that are required to induce improvement: users need to effectively interact with the portal (step 1) in order to receive information (step 2), which might influence their decision-making (step 3). We aimed to explore this potential knowledge gap by investigating to what extent each step has been investigated for patient portals, and explore the methodological approaches used. We performed a systematic literature review using Coiera's information value chain as a guiding theoretical framework. We searched MEDLINE and Scopus by combining terms related to patient portals and evaluation methodologies. Two reviewers selected relevant papers through duplicate screening, and one extracted data from the included papers. We included 115 articles. The large majority (n = 104) evaluated aspects related to interaction with patient portals (step 1). Usage was most often assessed (n = 61), mainly by analysing system interaction data (n = 50), with most authors considering participants as active users if they logged in at least once. Overall usability (n = 57) was commonly assessed through non-validated questionnaires (n = 44). Step 2 (information received) was investigated in 58 studies, primarily by analysing interaction data to evaluate usage of specific system functionalities (n = 34). Eleven studies explicitly assessed the influence of patient portals on patients' and clinicians' decisions (step 3). Whereas interaction with patient portals has been extensively studied, their influence on users' decision-making remains under-investigated. Methodological approaches to evaluating usage and usability of portals showed room for improvement. To unlock the potential of patient portals, more (robust) research should focus on better understanding the complex process of how portals lead to improved health and care. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Group decision-making approach for flood vulnerability identification using the fuzzy VIKOR method

NASA Astrophysics Data System (ADS)

Lee, G.; Jun, K. S.; Chung, E.-S.

2015-04-01

This study proposes an improved group decision making (GDM) framework that combines the VIKOR method with data fuzzification to quantify the spatial flood vulnerability including multiple criteria. In general, GDM method is an effective tool for formulating a compromise solution that involves various decision makers since various stakeholders may have different perspectives on their flood risk/vulnerability management responses. The GDM approach is designed to achieve consensus building that reflects the viewpoints of each participant. The fuzzy VIKOR method was developed to solve multi-criteria decision making (MCDM) problems with conflicting and noncommensurable criteria. This comprising method can be used to obtain a nearly ideal solution according to all established criteria. This approach effectively can propose some compromising decisions by combining the GDM method and fuzzy VIKOR method. The spatial flood vulnerability of the southern Han River using the GDM approach combined with the fuzzy VIKOR method was compared with the spatial flood vulnerability using general MCDM methods, such as the fuzzy TOPSIS and classical GDM methods (i.e., Borda, Condorcet, and Copeland). As a result, the proposed fuzzy GDM approach can reduce the uncertainty in the data confidence and weight derivation techniques. Thus, the combination of the GDM approach with the fuzzy VIKOR method can provide robust prioritization because it actively reflects the opinions of various groups and considers uncertainty in the input data.

Consideration of reference points for the management of renewable resources under an adaptive management paradigm

USGS Publications Warehouse

Irwin, Brian J.; Conroy, Michael J.

2013-01-01

The success of natural resource management depends on monitoring, assessment and enforcement. In support of these efforts, reference points (RPs) are often viewed as critical values of management-relevant indicators. This paper considers RPs from the standpoint of objective-driven decision making in dynamic resource systems, guided by principles of structured decision making (SDM) and adaptive resource management (AM). During the development of natural resource policy, RPs have been variously treated as either ‘targets’ or ‘triggers’. Under a SDM/AM paradigm, target RPs correspond approximately to value-based objectives, which may in turn be either of fundamental interest to stakeholders or intermediaries to other central objectives. By contrast, trigger RPs correspond to decision rules that are presumed to lead to desirable outcomes (such as the programme targets). Casting RPs as triggers or targets within a SDM framework is helpful towards clarifying why (or whether) a particular metric is appropriate. Further, the benefits of a SDM/AM process include elucidation of underlying untested assumptions that may reveal alternative metrics for use as RPs. Likewise, a structured decision-analytic framework may also reveal that failure to achieve management goals is not because the metrics are wrong, but because the decision-making process in which they are embedded is insufficiently robust to uncertainty, is not efficiently directed at producing a resource objective, or is incapable of adaptation to new knowledge.

Confronting dynamics and uncertainty in optimal decision making for conservation

USGS Publications Warehouse

Williams, Byron K.; Johnson, Fred A.

2013-01-01

The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a critically endangered population through captive breeding, control of invasive species, construction of biodiversity reserves, design of landscapes to increase habitat connectivity, and resource exploitation. Although these decision making problems and their solutions present significant challenges, we suggest that a systematic and effective approach to dynamic decision making in conservation need not be an onerous undertaking. The requirements are shared with any systematic approach to decision making--a careful consideration of values, actions, and outcomes.

Improving conservation of Florida manatees (Trichechus manatus latirostris): conceptualization and contributions toward a regional warm-water network management strategy for sustainable winter habitat.

PubMed

Flamm, Richard Owen; Reynolds, John Elliot; Harmak, Craig

2013-01-01

We used southwestern Florida as a case study to lay the groundwork for an intended and organized decision-making process for managing warm-water habitat needed by endangered manatees to survive winters in Florida. Scientists and managers have prioritized (a) projecting how the network of warm-water sites will change over the next 50 years as warmed industrial discharges may expire and as flows of natural springs are reduced through redirection of water for human uses, and (b) mitigating such changes to prevent undue consequences to manatees. Given the complexities introduced by manatee ecology; agency organizational structure; shifting public demands; fluctuating resource availability; and managing within interacting cultural, social, political, and environmental contexts, it was clear that a structured decision process was needed. To help promote such a process, we collected information relevant to future decisions including maps of known and suspected warm-water sites and prototyped a characterization of sites and networks. We propose steps that would lead to models that might serve as core tools in manatee/warm-water decision-making, and we summarized topics relevant for informed decision-making (e.g., manatee spatial cognition, risk of cold-stress morbidity and mortality, and human dimensions). A major impetus behind this effort is to ensure proactively that robust modeling tools are available well in advance of the anticipated need for a critical management decision.

Improving Conservation of Florida Manatees ( Trichechus manatus latirostris): Conceptualization and Contributions Toward a Regional Warm-Water Network Management Strategy for Sustainable Winter Habitat

NASA Astrophysics Data System (ADS)

Flamm, Richard Owen; Reynolds, John Elliot; Harmak, Craig

2013-01-01

We used southwestern Florida as a case study to lay the groundwork for an intended and organized decision-making process for managing warm-water habitat needed by endangered manatees to survive winters in Florida. Scientists and managers have prioritized (a) projecting how the network of warm-water sites will change over the next 50 years as warmed industrial discharges may expire and as flows of natural springs are reduced through redirection of water for human uses, and (b) mitigating such changes to prevent undue consequences to manatees. Given the complexities introduced by manatee ecology; agency organizational structure; shifting public demands; fluctuating resource availability; and managing within interacting cultural, social, political, and environmental contexts, it was clear that a structured decision process was needed. To help promote such a process, we collected information relevant to future decisions including maps of known and suspected warm-water sites and prototyped a characterization of sites and networks. We propose steps that would lead to models that might serve as core tools in manatee/warm-water decision-making, and we summarized topics relevant for informed decision-making (e.g., manatee spatial cognition, risk of cold-stress morbidity and mortality, and human dimensions). A major impetus behind this effort is to ensure proactively that robust modeling tools are available well in advance of the anticipated need for a critical management decision.

Collaborative decision-analytic framework to maximize resilience of tidal marshes to climate change

USGS Publications Warehouse

Thorne, Karen M.; Mattsson, Brady J.; Takekawa, John Y.; Cummings, Jonathan; Crouse, Debby; Block, Giselle; Bloom, Valary; Gerhart, Matt; Goldbeck, Steve; Huning, Beth; Sloop, Christina; Stewart, Mendel; Taylor, Karen; Valoppi, Laura

2015-01-01

Decision makers that are responsible for stewardship of natural resources face many challenges, which are complicated by uncertainty about impacts from climate change, expanding human development, and intensifying land uses. A systematic process for evaluating the social and ecological risks, trade-offs, and cobenefits associated with future changes is critical to maximize resilience and conserve ecosystem services. This is particularly true in coastal areas where human populations and landscape conversion are increasing, and where intensifying storms and sea-level rise pose unprecedented threats to coastal ecosystems. We applied collaborative decision analysis with a diverse team of stakeholders who preserve, manage, or restore tidal marshes across the San Francisco Bay estuary, California, USA, as a case study. Specifically, we followed a structured decision-making approach, and we using expert judgment developed alternative management strategies to increase the capacity and adaptability to manage tidal marsh resilience while considering uncertainties through 2050. Because sea-level rise projections are relatively confident to 2050, we focused on uncertainties regarding intensity and frequency of storms and funding. Elicitation methods allowed us to make predictions in the absence of fully compatible models and to assess short- and long-term trade-offs. Specifically we addressed two questions. (1) Can collaborative decision analysis lead to consensus among a diverse set of decision makers responsible for environmental stewardship and faced with uncertainties about climate change, funding, and stakeholder values? (2) What is an optimal strategy for the conservation of tidal marshes, and what strategy is robust to the aforementioned uncertainties? We found that when taking this approach, consensus was reached among the stakeholders about the best management strategies to maintain tidal marsh integrity. A Bayesian decision network revealed that a strategy considering sea-level rise and storms explicitly in wetland restoration planning and designs was optimal, and it was robust to uncertainties about management effectiveness and budgets. We found that strategies that avoided explicitly accounting for future climate change had the lowest expected performance based on input from the team. Our decision-analytic framework is sufficiently general to offer an adaptable template, which can be modified for use in other areas that include a diverse and engaged stakeholder group.

Probabilistic Decision Making with Spikes: From ISI Distributions to Behaviour via Information Gain.

PubMed

Caballero, Javier A; Lepora, Nathan F; Gurney, Kevin N

2015-01-01

Computational theories of decision making in the brain usually assume that sensory 'evidence' is accumulated supporting a number of hypotheses, and that the first accumulator to reach threshold triggers a decision in favour of its associated hypothesis. However, the evidence is often assumed to occur as a continuous process whose origins are somewhat abstract, with no direct link to the neural signals - action potentials or 'spikes' - that must ultimately form the substrate for decision making in the brain. Here we introduce a new variant of the well-known multi-hypothesis sequential probability ratio test (MSPRT) for decision making whose evidence observations consist of the basic unit of neural signalling - the inter-spike interval (ISI) - and which is based on a new form of the likelihood function. We dub this mechanism s-MSPRT and show its precise form for a range of realistic ISI distributions with positive support. In this way we show that, at the level of spikes, the refractory period may actually facilitate shorter decision times, and that the mechanism is robust against poor choice of the hypothesized data distribution. We show that s-MSPRT performance is related to the Kullback-Leibler divergence (KLD) or information gain between ISI distributions, through which we are able to link neural signalling to psychophysical observation at the behavioural level. Thus, we find the mean information needed for a decision is constant, thereby offering an account of Hick's law (relating decision time to the number of choices). Further, the mean decision time of s-MSPRT shows a power law dependence on the KLD offering an account of Piéron's law (relating reaction time to stimulus intensity). These results show the foundations for a research programme in which spike train analysis can be made the basis for predictions about behavior in multi-alternative choice tasks.

Probabilistic Decision Making with Spikes: From ISI Distributions to Behaviour via Information Gain

PubMed Central

Caballero, Javier A.; Lepora, Nathan F.; Gurney, Kevin N.

2015-01-01

Computational theories of decision making in the brain usually assume that sensory 'evidence' is accumulated supporting a number of hypotheses, and that the first accumulator to reach threshold triggers a decision in favour of its associated hypothesis. However, the evidence is often assumed to occur as a continuous process whose origins are somewhat abstract, with no direct link to the neural signals - action potentials or 'spikes' - that must ultimately form the substrate for decision making in the brain. Here we introduce a new variant of the well-known multi-hypothesis sequential probability ratio test (MSPRT) for decision making whose evidence observations consist of the basic unit of neural signalling - the inter-spike interval (ISI) - and which is based on a new form of the likelihood function. We dub this mechanism s-MSPRT and show its precise form for a range of realistic ISI distributions with positive support. In this way we show that, at the level of spikes, the refractory period may actually facilitate shorter decision times, and that the mechanism is robust against poor choice of the hypothesized data distribution. We show that s-MSPRT performance is related to the Kullback-Leibler divergence (KLD) or information gain between ISI distributions, through which we are able to link neural signalling to psychophysical observation at the behavioural level. Thus, we find the mean information needed for a decision is constant, thereby offering an account of Hick's law (relating decision time to the number of choices). Further, the mean decision time of s-MSPRT shows a power law dependence on the KLD offering an account of Piéron's law (relating reaction time to stimulus intensity). These results show the foundations for a research programme in which spike train analysis can be made the basis for predictions about behavior in multi-alternative choice tasks. PMID:25923907

Information Warfare-Worthy Jamming Attack Detection Mechanism for Wireless Sensor Networks Using a Fuzzy Inference System

PubMed Central

Misra, Sudip; Singh, Ranjit; Rohith Mohan, S. V.

2010-01-01

The proposed mechanism for jamming attack detection for wireless sensor networks is novel in three respects: firstly, it upgrades the jammer to include versatile military jammers; secondly, it graduates from the existing node-centric detection system to the network-centric system making it robust and economical at the nodes, and thirdly, it tackles the problem through fuzzy inference system, as the decision regarding intensity of jamming is seldom crisp. The system with its high robustness, ability to grade nodes with jamming indices, and its true-detection rate as high as 99.8%, is worthy of consideration for information warfare defense purposes. PMID:22319307

Fair Use Education for the Twenty-First Century: A Comparative Study of Students' Use of an Interactive Tool to Guide Decision Making

ERIC Educational Resources Information Center

Greenhow, Christine; Walker, J. D.; Donnelly, Dan; Cohen, Brad

2008-01-01

Christine Greenhow, J. D. Walker, Dan Donnelly, and Brad Cohen describe the implementation and evaluation of the University of Minnesota's Fair Use Analysis (FUA) tool, an interactive online application intended to educate users and foster defensible fair use practice in accordance with copyright law by guiding users through a robust,…

The Effect of Expected Value on Attraction Effect Preference Reversals

PubMed Central

Warren, Paul A.; El‐Deredy, Wael; Howes, Andrew

2016-01-01

Abstract The attraction effect shows that adding a third alternative to a choice set can alter preference between the original two options. For over 30 years, this simple demonstration of context dependence has been taken as strong evidence against a class of parsimonious value‐maximising models that evaluate alternatives independently from one another. Significantly, however, in previous demonstrations of the attraction effect alternatives are approximately equally valuable, so there was little consequence to the decision maker irrespective of which alternative was selected. Here we vary the difference in expected value between alternatives and provide the first demonstration that, although extinguished with large differences, this theoretically important effect persists when choice between alternatives has a consequence. We use this result to clarify the implications of the attraction effect, arguing that although it robustly violates the assumptions of value‐maximising models, it does not eliminate the possibility that human decision making is optimal. © 2016 The Authors Journal of Behavioral Decision Making Published by John Wiley & Sons Ltd. PMID:29081595

The Implications of Meno’s Paradox for the Mental Capacity Act 2005

PubMed Central

2016-01-01

Meno’s paradox—which asks ‘how will you know it is the thing you didn’t know?’—appears in Plato’s dialogue of the same name. This article suggests that a similar question arises in some supportive relationships. Attention to this question clarifies one condition necessary to justify making a best interests decisions against someone’s will: the decided-for person must be unable to recognise that they have failed to recognise a need. From this condition, two duties are derived: a duty to ensure that someone cannot recognise that they have failed to recognise a need before making a decision against their will; and a duty to provide consensual support to those who have had decisions made against their will, in order to help them to avoid such second-order failures of recognition in the future. The article assesses the Mental Capacity Act 2005 against each of these duties. For each duty, it finds that the Act allows compliance, but does not robustly require it. PMID:28007809

The Effect of Expected Value on Attraction Effect Preference Reversals.

PubMed

Farmer, George D; Warren, Paul A; El-Deredy, Wael; Howes, Andrew

2017-10-01

The attraction effect shows that adding a third alternative to a choice set can alter preference between the original two options. For over 30 years, this simple demonstration of context dependence has been taken as strong evidence against a class of parsimonious value-maximising models that evaluate alternatives independently from one another. Significantly, however, in previous demonstrations of the attraction effect alternatives are approximately equally valuable, so there was little consequence to the decision maker irrespective of which alternative was selected. Here we vary the difference in expected value between alternatives and provide the first demonstration that, although extinguished with large differences, this theoretically important effect persists when choice between alternatives has a consequence. We use this result to clarify the implications of the attraction effect, arguing that although it robustly violates the assumptions of value-maximising models, it does not eliminate the possibility that human decision making is optimal. © 2016 The Authors Journal of Behavioral Decision Making Published by John Wiley & Sons Ltd.

Communicating Uncertainty in Volcanic Ash Forecasts: Decision-Making and Information Preferences

NASA Astrophysics Data System (ADS)

Mulder, Kelsey; Black, Alison; Charlton-Perez, Andrew; McCloy, Rachel; Lickiss, Matthew

2016-04-01

The Robust Assessment and Communication of Environmental Risk (RACER) consortium, an interdisciplinary research team focusing on communication of uncertainty with respect to natural hazards, hosted a Volcanic Ash Workshop to discuss issues related to volcanic ash forecasting, especially forecast uncertainty. Part of the workshop was a decision game in which participants including forecasters, academics, and members of the Aviation Industry were given hypothetical volcanic ash concentration forecasts and asked whether they would approve a given flight path. The uncertainty information was presented in different formats including hazard maps, line graphs, and percent probabilities. Results from the decision game will be presented with a focus on information preferences, understanding of the forecasts, and whether different formats of the same volcanic ash forecast resulted in different flight decisions. Implications of this research will help the design and presentation of volcanic ash plume decision tools and can also help advise design of other natural hazard information.

Final Report: Towards an Emergent Model of Technology Adoption for Accelerating the Diffusion of Residential Solar PV

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rai, Varun

This project sought to enable electric utilities in Texas to accelerate diffusion of residential solar photovoltaic (PV) by systematically identifying and targeting existing barriers to PV adoption. A core goal of the project was to develop an integrated research framework that combines survey research, econometric modeling, financial modeling, and implementation and evaluation of pilot projects to study the PV diffusion system. This project considered PV diffusion as an emergent system, with attention to the interactions between the constituent parts of the PV socio-technical system including: economics of individual decision-making; peer and social influences; behavioral responses; and information and transaction costs.more » We also conducted two pilot projects, which have yielded new insights into behavioral and informational aspects of PV adoption. Finally, this project has produced robust and generalizable results that will provide deeper insights into the technology-diffusion process that will be applicable for the design of utility programs for other technologies such as home-energy management systems and plug-in electric vehicles. When we started this project in 2013 there was little systematic research on characterizing the decision-making process of households interested in adopting PV. This project was designed to fill that research gap by analyzing the PV adoption process from the consumers' decision-making perspective and with the objective to systematically identifying and addressing the barriers that consumers face in the adoption of PV. The two key components of that decision-making process are consumers' evaluation of: (i) uncertainties and non-monetary costs associated with the technology and (ii) the direct monetary cost-benefit. This project used an integrated approach to study both the non-monetary and the monetary components of the consumer decision-making process.« less

Multi-Agent Many-Objective Robust Decision Making: Supporting Cooperative Regional Water Portfolio Planning in the Eastern United States

NASA Astrophysics Data System (ADS)

Herman, J. D.; Zeff, H. B.; Reed, P. M.; Characklis, G. W.

2013-12-01

In the Eastern United States, water infrastructure and institutional frameworks have evolved in a historically water-rich environment. However, large regional droughts over the past decade combined with continuing population growth have marked a transition to a state of water scarcity, for which current planning paradigms are ill-suited. Significant opportunities exist to improve the efficiency of water infrastructure via regional coordination, namely, regional 'portfolios' of water-related assets such as reservoirs, conveyance, conservation measures, and transfer agreements. Regional coordination offers the potential to improve reliability, cost, and environmental impact in the expected future state of the world, and, with informed planning, to improve robustness to future uncertainty. In support of this challenge, this study advances a multi-agent many-objective robust decision making (multi-agent MORDM) framework that blends novel computational search and uncertainty analysis tools to discover flexible, robust regional portfolios. Our multi-agent MORDM framework is demonstrated for four water utilities in the Research Triangle region of North Carolina, USA. The utilities supply nearly two million customers and have the ability to interact with one another via transfer agreements and shared infrastructure. We show that strategies for this region which are Pareto-optimal in the expected future state of the world remain vulnerable to performance degradation under alternative scenarios of deeply uncertain hydrologic and economic factors. We then apply the Patient Rule Induction Method (PRIM) to identify which of these uncertain factors drives the individual and collective vulnerabilities for the four cooperating utilities. Our results indicate that clear multi-agent tradeoffs emerge for attaining robustness across the utilities. Furthermore, the key factor identified for improving the robustness of the region's water supply is cooperative demand reduction. This type of approach is critically important given the risks and challenges posed by rising supply development costs, limits on new infrastructure, growing water demands and the underlying uncertainties associated with climate change. The proposed framework serves as a planning template for other historically water-rich regions which must now confront the reality of impending water scarcity.

Robust DEA under discrete uncertain data: a case study of Iranian electricity distribution companies

NASA Astrophysics Data System (ADS)

Hafezalkotob, Ashkan; Haji-Sami, Elham; Omrani, Hashem

2015-06-01

Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the real-world problems often deal with imprecise or ambiguous data. In this paper, we propose a novel robust data envelopment model (RDEA) to investigate the efficiencies of decision-making units (DMU) when there are discrete uncertain input and output data. The method is based upon the discrete robust optimization approaches proposed by Mulvey et al. (1995) that utilizes probable scenarios to capture the effect of ambiguous data in the case study. Our primary concern in this research is evaluating electricity distribution companies under uncertainty about input/output data. To illustrate the ability of proposed model, a numerical example of 38 Iranian electricity distribution companies is investigated. There are a large amount ambiguous data about these companies. Some electricity distribution companies may not report clear and real statistics to the government. Thus, it is needed to utilize a prominent approach to deal with this uncertainty. The results reveal that the RDEA model is suitable and reliable for target setting based on decision makers (DM's) preferences when there are uncertain input/output data.

Decision insight into stakeholder conflict for ERN.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Siirola, John; Tidwell, Vincent Carroll; Benz, Zachary O.

Participatory modeling has become an important tool in facilitating resource decision making and dispute resolution. Approaches to modeling that are commonly used in this context often do not adequately account for important human factors. Current techniques provide insights into how certain human activities and variables affect resource outcomes; however, they do not directly simulate the complex variables that shape how, why, and under what conditions different human agents behave in ways that affect resources and human interactions related to them. Current approaches also do not adequately reveal how the effects of individual decisions scale up to have systemic level effectsmore » in complex resource systems. This lack of integration prevents the development of more robust models to support decision making and dispute resolution processes. Development of integrated tools is further hampered by the fact that collection of primary data for decision-making modeling is costly and time consuming. This project seeks to develop a new approach to resource modeling that incorporates both technical and behavioral modeling techniques into a single decision-making architecture. The modeling platform is enhanced by use of traditional and advanced processes and tools for expedited data capture. Specific objectives of the project are: (1) Develop a proof of concept for a new technical approach to resource modeling that combines the computational techniques of system dynamics and agent based modeling, (2) Develop an iterative, participatory modeling process supported with traditional and advance data capture techniques that may be utilized to facilitate decision making, dispute resolution, and collaborative learning processes, and (3) Examine potential applications of this technology and process. The development of this decision support architecture included both the engineering of the technology and the development of a participatory method to build and apply the technology. Stakeholder interaction with the model and associated data capture was facilitated through two very different modes of engagement, one a standard interface involving radio buttons, slider bars, graphs and plots, while the other utilized an immersive serious gaming interface. The decision support architecture developed through this project was piloted in the Middle Rio Grande Basin to examine how these tools might be utilized to promote enhanced understanding and decision-making in the context of complex water resource management issues. Potential applications of this architecture and its capacity to lead to enhanced understanding and decision-making was assessed through qualitative interviews with study participants who represented key stakeholders in the basin.« less

Optimal data systems: the future of clinical predictions and decision support.

PubMed

Celi, Leo A; Csete, Marie; Stone, David

2014-10-01

The purpose of the review is to describe the evolving concept and role of data as it relates to clinical predictions and decision-making. Critical care medicine is, as an especially data-rich specialty, becoming acutely cognizant not only of its historic deficits in data utilization but also of its enormous potential for capturing, mining, and leveraging such data into well-designed decision support modalities as well as the formulation of robust best practices. Modern electronic medical records create an opportunity to design complete and functional data systems that can support clinical care to a degree never seen before. Such systems are often referred to as 'data-driven,' but a better term is 'optimal data systems' (ODS). Here we discuss basic features of an ODS and its benefits, including the potential to transform clinical prediction and decision support.

Critical reading and critical thinking--study design and methodology: a personal approach on how to read the clinical literature.

PubMed

Lipman, Timothy O

2013-04-01

The volume of medical literature grows exponentially. Yet we are faced with the necessity to make clinical decisions based on the availability and quality of scientific information. The general strength (reliability, robustness) of any interpretation that guides us in clinical decision making is dependent on how information was obtained. All information and medical studies and, consequently, all conclusions are not created equal. It is incumbent upon us to be able to assess the quality of the information that guides us in the care of our patients. Being able to assess medical literature critically requires use of critical reading and critical thinking skills. To achieve these skills, to be able to analyze medical literature critically, takes a combination of education and practice, practice, and more practice.

The neural systems for perceptual updating.

PubMed

Stöttinger, Elisabeth; Aichhorn, Markus; Anderson, Britt; Danckert, James

2018-04-01

In a constantly changing environment we must adapt to both abrupt and gradual changes to incoming information. Previously, we demonstrated that a distributed network (including the anterior insula and anterior cingulate cortex) was active when participants updated their initial representations (e.g., it's a cat) in a gradually morphing picture task (e.g., now it's a rabbit; Stöttinger et al., 2015). To shed light on whether these activations reflect the proactive decisions to update or perceptual uncertainty, we introduced two additional conditions. By presenting picture morphs twice we controlled for uncertainty in perceptual decision making. Inducing an abrupt shift in a third condition allowed us to differentiate between a proactive decision in uncertainty-driven updating and a reactive decision in surprise-based updating. We replicated our earlier result, showing the robustness of the effect. In addition, we found activation in the anterior insula (bilaterally) and the mid frontal area/ACC in all three conditions, indicative of the importance of these areas in updating of all kinds. When participants were naïve as to the identity of the second object, we found higher activations in the mid-cingulate cortex and cuneus - areas typically associated with task difficulty, in addition to higher activations in the right TPJ most likely reflecting the shift to a new perspective. Activations associated with the proactive decision to update to a new interpretation were found in a network including the dorsal ACC known to be involved in exploration and the endogenous decision to switch to a new interpretation. These findings suggest a general network commonly engaged in all types of perceptual decision making supported by additional networks associated with perceptual uncertainty or updating provoked by either proactive or reactive decision making. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Network robustness assessed within a dual connectivity framework: joint dynamics of the Active and Idle Networks.

PubMed

Tejedor, Alejandro; Longjas, Anthony; Zaliapin, Ilya; Ambroj, Samuel; Foufoula-Georgiou, Efi

2017-08-17

Network robustness against attacks has been widely studied in fields as diverse as the Internet, power grids and human societies. But current definition of robustness is only accounting for half of the story: the connectivity of the nodes unaffected by the attack. Here we propose a new framework to assess network robustness, wherein the connectivity of the affected nodes is also taken into consideration, acknowledging that it plays a crucial role in properly evaluating the overall network robustness in terms of its future recovery from the attack. Specifically, we propose a dual perspective approach wherein at any instant in the network evolution under attack, two distinct networks are defined: (i) the Active Network (AN) composed of the unaffected nodes and (ii) the Idle Network (IN) composed of the affected nodes. The proposed robustness metric considers both the efficiency of destroying the AN and that of building-up the IN. We show, via analysis of well-known prototype networks and real world data, that trade-offs between the efficiency of Active and Idle Network dynamics give rise to surprising robustness crossovers and re-rankings, which can have significant implications for decision making.

Overcoming Indecision by Changing the Decision Boundary

PubMed Central

2017-01-01

The dominant theoretical framework for decision making asserts that people make decisions by integrating noisy evidence to a threshold. It has recently been shown that in many ecologically realistic situations, decreasing the decision boundary maximizes the reward available from decisions. However, empirical support for decreasing boundaries in humans is scant. To investigate this problem, we used an ideal observer model to identify the conditions under which participants should change their decision boundaries with time to maximize reward rate. We conducted 6 expanded-judgment experiments that precisely matched the assumptions of this theoretical model. In this paradigm, participants could sample noisy, binary evidence presented sequentially. Blocks of trials were fixed in duration, and each trial was an independent reward opportunity. Participants therefore had to trade off speed (getting as many rewards as possible) against accuracy (sampling more evidence). Having access to the actual evidence samples experienced by participants enabled us to infer the slope of the decision boundary. We found that participants indeed modulated the slope of the decision boundary in the direction predicted by the ideal observer model, although we also observed systematic deviations from optimality. Participants using suboptimal boundaries do so in a robust manner, so that any error in their boundary setting is relatively inexpensive. The use of a normative model provides insight into what variable(s) human decision makers are trying to optimize. Furthermore, this normative model allowed us to choose diagnostic experiments and in doing so we present clear evidence for time-varying boundaries. PMID:28406682

Using evidence-based algorithms to improve clinical decision making: the case of a first-time anterior shoulder dislocation.

PubMed

Federer, Andrew E; Taylor, Dean C; Mather, Richard C

2013-09-01

Decision making in health care has evolved substantially over the last century. Up until the late 1970s, medical decision making was predominantly intuitive and anecdotal. It was based on trial and error and involved high levels of problem solving. The 1980s gave way to empirical medicine, which was evidence based probabilistic, and involved pattern recognition and less problem solving. Although this represented a major advance in the quality of medical decision making, limitations existed. The advantages of the gold standard of the randomized controlled clinical trial (RCT) are well-known and this technique is irreplaceable in its ability to answer critical clinical questions. However, the RCT does have drawbacks. RCTs are expensive and can only capture a snapshot in time. As treatments change and new technologies emerge, new expensive clinical trials must be undertaken to reevaluate them. Furthermore, in order to best evaluate a single intervention, other factors must be controlled. In addition, the study population may not match that of another organization or provider. Although evidence-based medicine has provided powerful data for clinicians, effectively and efficiently tailoring it to the individual has not yet evolved. We are now in a period of transition from this evidence-based era to one dominated by the personalization and customization of care. It will be fueled by policy decisions to shift financial responsibility to the patient, creating a powerful and sophisticated consumer, unlike any patient we have known before. The challenge will be to apply medical evidence and personal preferences to medical decisions and deliver it efficiently in the increasingly busy clinical setting. In this article, we provide a robust review of the concepts of customized care and some of techniques to deliver it. We will illustrate this through a personalized decision model for the treatment decision after a first-time anterior shoulder dislocation.

Functional Assessment of Genetic Variants with Outcomes Adapted to Clinical Decision-Making

PubMed Central

Thouvenot, Pierre; Ben Yamin, Barbara; Fourrière, Lou; Lescure, Aurianne; Boudier, Thomas; Del Nery, Elaine; Chauchereau, Anne; Goldgar, David E.; Stoppa-Lyonnet, Dominique; Nicolas, Alain; Millot, Gaël A.

2016-01-01

Understanding the medical effect of an ever-growing number of human variants detected is a long term challenge in genetic counseling. Functional assays, based on in vitro or in vivo evaluations of the variant effects, provide essential information, but they require robust statistical validation, as well as adapted outputs, to be implemented in the clinical decision-making process. Here, we assessed 25 pathogenic and 15 neutral missense variants of the BRCA1 breast/ovarian cancer susceptibility gene in four BRCA1 functional assays. Next, we developed a novel approach that refines the variant ranking in these functional assays. Lastly, we developed a computational system that provides a probabilistic classification of variants, adapted to clinical interpretation. Using this system, the best functional assay exhibits a variant classification accuracy estimated at 93%. Additional theoretical simulations highlight the benefit of this ready-to-use system in the classification of variants after functional assessment, which should facilitate the consideration of functional evidences in the decision-making process after genetic testing. Finally, we demonstrate the versatility of the system with the classification of siRNAs tested for human cell growth inhibition in high throughput screening. PMID:27272900

Robust evaluation of time series classification algorithms for structural health monitoring

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Worden, Keith; Todd, Michael D.

2014-03-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and mechanical infrastructure through analysis of structural response measurements. The supervised learning methodology for data-driven SHM involves computation of low-dimensional, damage-sensitive features from raw measurement data that are then used in conjunction with machine learning algorithms to detect, classify, and quantify damage states. However, these systems often suffer from performance degradation in real-world applications due to varying operational and environmental conditions. Probabilistic approaches to robust SHM system design suffer from incomplete knowledge of all conditions a system will experience over its lifetime. Info-gap decision theory enables nonprobabilistic evaluation of the robustness of competing models and systems in a variety of decision making applications. Previous work employed info-gap models to handle feature uncertainty when selecting various components of a supervised learning system, namely features from a pre-selected family and classifiers. In this work, the info-gap framework is extended to robust feature design and classifier selection for general time series classification through an efficient, interval arithmetic implementation of an info-gap data model. Experimental results are presented for a damage type classification problem on a ball bearing in a rotating machine. The info-gap framework in conjunction with an evolutionary feature design system allows for fully automated design of a time series classifier to meet performance requirements under maximum allowable uncertainty.

Use of structured decision-making to explicitly incorporate environmental process understanding in management of coastal restoration projects: Case study on barrier islands of the northern Gulf of Mexico.

PubMed

Dalyander, P Soupy; Meyers, Michelle; Mattsson, Brady; Steyer, Gregory; Godsey, Elizabeth; McDonald, Justin; Byrnes, Mark; Ford, Mark

2016-12-01

Coastal ecosystem management typically relies on subjective interpretation of scientific understanding, with limited methods for explicitly incorporating process knowledge into decisions that must meet multiple, potentially competing stakeholder objectives. Conversely, the scientific community lacks methods for identifying which advancements in system understanding would have the highest value to decision-makers. A case in point is barrier island restoration, where decision-makers lack tools to objectively use system understanding to determine how to optimally use limited contingency funds when project construction in this dynamic environment does not proceed as expected. In this study, collaborative structured decision-making (SDM) was evaluated as an approach to incorporate process understanding into mid-construction decisions and to identify priority gaps in knowledge from a management perspective. The focus was a barrier island restoration project at Ship Island, Mississippi, where sand will be used to close an extensive breach that currently divides the island. SDM was used to estimate damage that may occur during construction, and guide repair decisions within the confines of limited availability of sand and funding to minimize adverse impacts to project objectives. Sand was identified as more limiting than funds, and unrepaired major breaching would negatively impact objectives. Repairing minor damage immediately was determined to be generally more cost effective (depending on the longshore extent) than risking more damage to a weakened project. Key gaps in process-understanding relative to project management were identified as the relationship of island width to breach formation; the amounts of sand lost during breaching, lowering, or narrowing of the berm; the potential for minor breaches to self-heal versus developing into a major breach; and the relationship between upstream nourishment and resiliency of the berm to storms. This application is a prototype for using structured decision-making in support of engineering projects in dynamic environments where mid-construction decisions may arise; highlights uncertainty about barrier island physical processes that limit the ability to make robust decisions; and demonstrates the potential for direct incorporation of process-based models in a formal adaptive management decision framework. Published by Elsevier Ltd.

Use of structured decision-making to explicitly incorporate environmental process understanding in management of coastal restoration projects: Case study on barrier islands of the northern Gulf of Mexico

USGS Publications Warehouse

Dalyander, P. Soupy; Meyers, Michelle B.; Mattsson, Brady; Steyer, Gregory; Godsey, Elizabeth; McDonald, Justin; Byrnes, Mark R.; Ford, Mark

2016-01-01

Coastal ecosystem management typically relies on subjective interpretation of scientific understanding, with limited methods for explicitly incorporating process knowledge into decisions that must meet multiple, potentially competing stakeholder objectives. Conversely, the scientific community lacks methods for identifying which advancements in system understanding would have the highest value to decision-makers. A case in point is barrier island restoration, where decision-makers lack tools to objectively use system understanding to determine how to optimally use limited contingency funds when project construction in this dynamic environment does not proceed as expected. In this study, collaborative structured decision-making (SDM) was evaluated as an approach to incorporate process understanding into mid-construction decisions and to identify priority gaps in knowledge from a management perspective. The focus was a barrier island restoration project at Ship Island, Mississippi, where sand will be used to close an extensive breach that currently divides the island. SDM was used to estimate damage that may occur during construction, and guide repair decisions within the confines of limited availability of sand and funding to minimize adverse impacts to project objectives. Sand was identified as more limiting than funds, and unrepaired major breaching would negatively impact objectives. Repairing minor damage immediately was determined to be generally more cost effective (depending on the longshore extent) than risking more damage to a weakened project. Key gaps in process-understanding relative to project management were identified as the relationship of island width to breach formation; the amounts of sand lost during breaching, lowering, or narrowing of the berm; the potential for minor breaches to self-heal versus developing into a major breach; and the relationship between upstream nourishment and resiliency of the berm to storms. This application is a prototype for using structured decision-making in support of engineering projects in dynamic environments where mid-construction decisions may arise; highlights uncertainty about barrier island physical processes that limit the ability to make robust decisions; and demonstrates the potential for direct incorporation of process-based models in a formal adaptive management decision framework.

Assessing Generalisability in Model-Based Economic Evaluation Studies: A Structured Review in Osteoporosis

PubMed Central

Urdahl, Hege; Manca, Andrea; Sculpher, Mark J

2008-01-01

Background To support decision making many countries have now introduced some formal assessment process to evaluate whether health technologies represent good ‘value for money’. These often take the form of decision models which can be used to explore elements of importance to generalisability of study results across clinical settings and jurisdictions. The objectives of the present review were to assess: (i) whether the published studies clearly defined the decision-making audience for the model; (ii) the transparency of the reporting in terms of study question, structure and data inputs; (iii) the relevance of the data inputs used in the model to the stated decision-maker or jurisdiction; and (iv) how fully the robustness of the model's results to variation in data inputs between locations was assessed. Methods Articles reporting decision-analytic models in the area of osteoporosis were assessed to establish the extent to which the information provided enabled decision makers in different countries/jurisdictions to fully appreciate the variability of results according to location, and the relevance to their own. Results Of the 18 articles included in the review, only three explicitly stated the decision-making audience. It was not possible to infer a decision-making audience in eight studies. Target population was well reported, as was resource and cost data, and clinical data used for estimates of relative risk reduction. However, baseline risk was rarely adapted to the relevant jurisdiction, and when no decision-maker was explicit it was difficult to assess whether the reported cost and resource use data was in fact relevant. A few studies used sensitivity analysis to explore elements of generalisability, such as compliance rates and baseline fracture risk rates, although such analyses were generally restricted to evaluating parameter uncertainty. Conclusion This review found that variability in cost-effectiveness across locations is addressed to a varying extent in modelling studies in the field of osteoporosis, limiting their use for decision-makers across different locations. Transparency of reporting is expected to increase as methodology develops, and decision-makers publish “reference case” type guidance. PMID:17129074

The effectiveness of health impact assessment in influencing decision-making in Australia and New Zealand 2005–2009

PubMed Central

2013-01-01

Background Health Impact Assessment (HIA) involves assessing how proposals may alter the determinants of health prior to implementation and recommends changes to enhance positive and mitigate negative impacts. HIAs growing use needs to be supported by a strong evidence base, both to validate the value of its application and to make its application more robust. We have carried out the first systematic empirical study of the influence of HIA on decision-making and implementation of proposals in Australia and New Zealand. This paper focuses on identifying whether and how HIAs changed decision-making and implementation and impacts that participants report following involvement in HIAs. Methods We used a two-step process first surveying 55 HIAs followed by 11 in-depth case studies. Data gathering methods included questionnaires with follow-up interview, semi-structured interviews and document collation. We carried out deductive and inductive qualitative content analyses of interview transcripts and documents as well as simple descriptive statistics. Results We found that most HIAs are effective in some way. HIAs are often directly effective in changing, influencing, broadening areas considered and in some cases having immediate impact on decisions. Even when HIAs are reported to have no direct effect on a decision they are often still effective in influencing decision-making processes and the stakeholders involved in them. HIA participants identify changes in relationships, improved understanding of the determinants of health and positive working relationships as major and sustainable impacts of their involvement. Conclusions This study clearly demonstrates direct and indirect effectiveness of HIA influencing decision making in Australia and New Zealand. We recommend that public health leaders and policy makers should be confident in promoting the use of HIA and investing in building capacity to undertake high quality HIAs. New findings about the value HIA stakeholders put on indirect impacts such as learning and relationship building suggest HIA has a role both as a technical tool that makes predictions of potential impacts of a policy, program or project and as a mechanism for developing relationships with and influencing other sectors. Accordingly when evaluating the effectiveness of HIAs we need to look beyond the direct impacts on decisions. PMID:24341545

Capability and Development Risk Management in System-of-Systems Architectures: A Portfolio Approach to Decision-Making

DTIC Science & Technology

2012-04-30

tool that provides a means of balancing capability development against cost and interdependent risks through the use of modern portfolio theory ...Focardi, 2007; Tutuncu & Cornuejols, 2007) that are extensions of modern portfolio and control theory . The reformulation allows for possible changes...Acquisition: Wave Model context • An Investment Portfolio Approach – Mean Variance Approach – Mean - Variance : A Robust Version • Concept

Self-Stabilizing and Efficient Robust Uncertainty Management

DTIC Science & Technology

2011-10-01

Group decision making in honey bee swarms. American Scientist. 94:220-229. 71 Frisch, Karl von. (1967) The Dance Language and Orientation of... Bees . Cambridge, Mass.: The Belknap Press of Harvard University Press. 18 Thom et al. (21 August 2007) The Scent of the Waggle Dance . PLoS Biology...Orientation of Bees . Cambridge, Mass.: The Belknap Press of Harvard University Press. 02 Frisch, Karl von. (1967) The Dance Language and

Robust Sensitivity Analysis of Courses of Action Using an Additive Value Model

DTIC Science & Technology

2008-03-01

According to Clemen , sensitivity analysis answers, “What makes a difference in this decision?” (2001:175). Sensitivity analysis can also indicate...alternative to change. These models look for the new weighting that causes a specific alternative to rank above all others. 19 Barron and Schmidt first... Schmidt , 1988:123). A smaller objective function value indicates greater sensitivity. Wolters and Mareschal propose a similar approach using goal

Clinical reasoning in the real world is mediated by bounded rationality: implications for diagnostic clinical practice guidelines.

PubMed

Bonilauri Ferreira, Ana Paula Ribeiro; Ferreira, Rodrigo Fernando; Rajgor, Dimple; Shah, Jatin; Menezes, Andrea; Pietrobon, Ricardo

2010-04-20

Little is known about the reasoning mechanisms used by physicians in decision-making and how this compares to diagnostic clinical practice guidelines. We explored the clinical reasoning process in a real life environment. This is a qualitative study evaluating transcriptions of sixteen physicians' reasoning during appointments with patients, clinical discussions between specialists, and personal interviews with physicians affiliated to a hospital in Brazil. FOUR MAIN THEMES WERE IDENTIFIED: simple and robust heuristics, extensive use of social environment rationality, attempts to prove diagnostic and therapeutic hypothesis while refuting potential contradictions using positive test strategy, and reaching the saturation point. Physicians constantly attempted to prove their initial hypothesis while trying to refute any contradictions. While social environment rationality was the main factor in the determination of all steps of the clinical reasoning process, factors such as referral letters and number of contradictions associated with the initial hypothesis had influence on physicians' confidence and determination of the threshold to reach a final decision. Physicians rely on simple heuristics associated with environmental factors. This model allows for robustness, simplicity, and cognitive energy saving. Since this model does not fit into current diagnostic clinical practice guidelines, we make some propositions to help its integration.

Clinical Reasoning in the Real World Is Mediated by Bounded Rationality: Implications for Diagnostic Clinical Practice Guidelines

PubMed Central

Bonilauri Ferreira, Ana Paula Ribeiro; Ferreira, Rodrigo Fernando; Rajgor, Dimple; Shah, Jatin; Menezes, Andrea; Pietrobon, Ricardo

2010-01-01

Background Little is known about the reasoning mechanisms used by physicians in decision-making and how this compares to diagnostic clinical practice guidelines. We explored the clinical reasoning process in a real life environment. Method This is a qualitative study evaluating transcriptions of sixteen physicians' reasoning during appointments with patients, clinical discussions between specialists, and personal interviews with physicians affiliated to a hospital in Brazil. Results Four main themes were identified: simple and robust heuristics, extensive use of social environment rationality, attempts to prove diagnostic and therapeutic hypothesis while refuting potential contradictions using positive test strategy, and reaching the saturation point. Physicians constantly attempted to prove their initial hypothesis while trying to refute any contradictions. While social environment rationality was the main factor in the determination of all steps of the clinical reasoning process, factors such as referral letters and number of contradictions associated with the initial hypothesis had influence on physicians' confidence and determination of the threshold to reach a final decision. Discussion Physicians rely on simple heuristics associated with environmental factors. This model allows for robustness, simplicity, and cognitive energy saving. Since this model does not fit into current diagnostic clinical practice guidelines, we make some propositions to help its integration. PMID:20421920

Using expert judgments to explore robust alternatives for forest management under climate change.

PubMed

McDaniels, Timothy; Mills, Tamsin; Gregory, Robin; Ohlson, Dan

2012-12-01

We develop and apply a judgment-based approach to selecting robust alternatives, which are defined here as reasonably likely to achieve objectives, over a range of uncertainties. The intent is to develop an approach that is more practical in terms of data and analysis requirements than current approaches, informed by the literature and experience with probability elicitation and judgmental forecasting. The context involves decisions about managing forest lands that have been severely affected by mountain pine beetles in British Columbia, a pest infestation that is climate-exacerbated. A forest management decision was developed as the basis for the context, objectives, and alternatives for land management actions, to frame and condition the judgments. A wide range of climate forecasts, taken to represent the 10-90% levels on cumulative distributions for future climate, were developed to condition judgments. An elicitation instrument was developed, tested, and revised to serve as the basis for eliciting probabilistic three-point distributions regarding the performance of selected alternatives, over a set of relevant objectives, in the short and long term. The elicitations were conducted in a workshop comprising 14 regional forest management specialists. We employed the concept of stochastic dominance to help identify robust alternatives. We used extensive sensitivity analysis to explore the patterns in the judgments, and also considered the preferred alternatives for each individual expert. The results show that two alternatives that are more flexible than the current policies are judged more likely to perform better than the current alternatives on average in terms of stochastic dominance. The results suggest judgmental approaches to robust decision making deserve greater attention and testing. © 2012 Society for Risk Analysis.

Gaze behaviour during space perception and spatial decision making.

PubMed

Wiener, Jan M; Hölscher, Christoph; Büchner, Simon; Konieczny, Lars

2012-11-01

A series of four experiments investigating gaze behavior and decision making in the context of wayfinding is reported. Participants were presented with screenshots of choice points taken in large virtual environments. Each screenshot depicted alternative path options. In Experiment 1, participants had to decide between them to find an object hidden in the environment. In Experiment 2, participants were first informed about which path option to take as if following a guided route. Subsequently, they were presented with the same images in random order and had to indicate which path option they chose during initial exposure. In Experiment 1, we demonstrate (1) that participants have a tendency to choose the path option that featured the longer line of sight, and (2) a robust gaze bias towards the eventually chosen path option. In Experiment 2, systematic differences in gaze behavior towards the alternative path options between encoding and decoding were observed. Based on data from Experiments 1 and 2 and two control experiments ensuring that fixation patterns were specific to the spatial tasks, we develop a tentative model of gaze behavior during wayfinding decision making suggesting that particular attention was paid to image areas depicting changes in the local geometry of the environments such as corners, openings, and occlusions. Together, the results suggest that gaze during a wayfinding tasks is directed toward, and can be predicted by, a subset of environmental features and that gaze bias effects are a general phenomenon of visual decision making.

Justifying Clinical Nudges.

PubMed

Gorin, Moti; Joffe, Steven; Dickert, Neal; Halpern, Scott

2017-03-01

The shift away from paternalistic decision-making and toward patient-centered, shared decision-making has stemmed from the recognition that in order to practice medicine ethically, health care professionals must take seriously the values and preferences of their patients. At the same time, there is growing recognition that minor and seemingly irrelevant features of how choices are presented can substantially influence the decisions people make. Behavioral economists have identified striking ways in which trivial differences in the presentation of options can powerfully and predictably affect people's choices. Choice-affecting features of the decision environment that do not restrict the range of choices or significantly alter the incentives have come to be known as "nudges." Although some have criticized conscious efforts to influence choice, we believe that clinical nudges may often be morally justified. The most straightforward justification for nudge interventions is that they help people bypass their cognitive limitations-for example, the tendency to choose the first option presented even when that option is not the best for them-thereby allowing people to make choices that best align with their rational preferences or deeply held values. However, we argue that this justification is problematic. We argue that, if physicians wish to use nudges to shape their patients' choices, the justification for doing so must appeal to an ethical and professional standard, not to patients' preferences. We demonstrate how a standard with which clinicians and bioethicists already are quite familiar-the best-interest standard-offers a robust justification for the use of nudges. © 2017 The Hastings Center.

Adolescent neural response to reward is related to participant sex and task motivation

PubMed Central

Alarcón, Gabriela; Cservenka, Anita; Nagel, Bonnie J.

2017-01-01

Risky decision making is prominent during adolescence, perhaps contributed to by heightened sensation seeking and ongoing maturation of reward and dopamine systems in the brain, which are, in part, modulated by sex hormones. In this study, we examined sex differences in the neural substrates of reward sensitivity during a risky decision-making task and hypothesized that compared with girls, boys would show heightened brain activation in reward-relevant regions, particularly the nucleus accumbens, during reward receipt. Further, we hypothesized that testosterone and estradiol levels would mediate this sex difference. Moreover, we predicted boys would make more risky choices on the task. While boys showed increased nucleus accumbens blood oxygen level-dependent (BOLD) response relative to girls, sex hormones did not mediate this effect. As predicted, boys made a higher percentage of risky decisions during the task. Interestingly, boys also self-reported more motivation to perform well and earn money on the task, while girls self-reported higher state anxiety prior to the scan session. Motivation to earn money partially mediated the effect of sex on nucleus accumbens activity during reward. Previous research shows that increased motivation and salience of reinforcers is linked with more robust striatal BOLD response, therefore psychosocial factors, in addition to sex, may play an important role in reward sensitivity. Elucidating neurobiological mechanisms that support adolescent sex differences in risky decision making has important implications for understanding individual differences that lead to advantageous and adverse behaviors that affect health outcomes. PMID:27816780

The Behavioral Relevance of Cortical Neural Ensemble Responses Emerges Suddenly

PubMed Central

Sadacca, Brian F.; Mukherjee, Narendra; Vladusich, Tony; Li, Jennifer X.

2016-01-01

Whereas many laboratory-studied decisions involve a highly trained animal identifying an ambiguous stimulus, many naturalistic decisions do not. Consumption decisions, for instance, involve determining whether to eject or consume an already identified stimulus in the mouth and are decisions that can be made without training. By standard analyses, rodent cortical single-neuron taste responses come to predict such consumption decisions across the 500 ms preceding the consumption or rejection itself; decision-related firing emerges well after stimulus identification. Analyzing single-trial ensemble activity using hidden Markov models, we show these decision-related cortical responses to be part of a reliable sequence of states (each defined by the firing rates within the ensemble) separated by brief state-to-state transitions, the latencies of which vary widely between trials. When we aligned data to the onset of the (late-appearing) state that dominates during the time period in which single-neuron firing is correlated to taste palatability, the apparent ramp in stimulus-aligned choice-related firing was shown to be a much more precipitous coherent jump. This jump in choice-related firing resembled a step function more than it did the output of a standard (ramping) decision-making model, and provided a robust prediction of decision latency in single trials. Together, these results demonstrate that activity related to naturalistic consumption decisions emerges nearly instantaneously in cortical ensembles. SIGNIFICANCE STATEMENT This paper provides a description of how the brain makes evaluative decisions. The majority of work on the neurobiology of decision making deals with “what is it?” decisions; out of this work has emerged a model whereby neurons accumulate information about the stimulus in the form of slowly increasing firing rates and reach a decision when those firing rates reach a threshold. Here, we study a different kind of more naturalistic decision—a decision to evaluate “what shall I do with it?” after the identity of a taste in the mouth has been identified—and show that this decision is not made through the gradual increasing of stimulus-related firing, but rather that this decision appears to be made in a sudden moment of “insight.” PMID:26791199

Outcome based state budget allocation for diabetes prevention programs using multi-criteria optimization with robust weights.

PubMed

Mehrotra, Sanjay; Kim, Kibaek

2011-12-01

We consider the problem of outcomes based budget allocations to chronic disease prevention programs across the United States (US) to achieve greater geographical healthcare equity. We use Diabetes Prevention and Control Programs (DPCP) by the Center for Disease Control and Prevention (CDC) as an example. We present a multi-criteria robust weighted sum model for such multi-criteria decision making in a group decision setting. The principal component analysis and an inverse linear programming techniques are presented and used to study the actual 2009 budget allocation by CDC. Our results show that the CDC budget allocation process for the DPCPs is not likely model based. In our empirical study, the relative weights for different prevalence and comorbidity factors and the corresponding budgets obtained under different weight regions are discussed. Parametric analysis suggests that money should be allocated to states to promote diabetes education and to increase patient-healthcare provider interactions to reduce disparity across the US.

Planning for successful outcomes in the new millennium.

PubMed

Matthews, P

2000-02-01

The complexity of the health care environment will increase in the next millennium. Organizations must adopt an approach of selecting outcomes management solutions that are focused on data capture, analysis, and comparative reviews and reporting. They must decisively and creatively implement, in a phased approach, integrated solutions from existing robust systems, while considering future systems targeted for implementation. Outcomes management solutions must be integrated with the organization's information systems strategic plan. The successful organization must be able to turn business-critical data into information that supports both business and clinical decision-making activities. In short, health care organizations will have to become information-driven.

NASA Biomedical Informatics Capabilities and Needs

NASA Technical Reports Server (NTRS)

Johnson-Throop, Kathy A.

2009-01-01

To improve on-orbit clinical capabilities by developing and providing operational support for intelligent, robust, reliable, and secure, enterprise-wide and comprehensive health care and biomedical informatics systems with increasing levels of autonomy, for use on Earth, low Earth orbit & exploration class missions. Biomedical Informatics is an emerging discipline that has been defined as the study, invention, and implementation of structures and algorithms to improve communication, understanding and management of medical information. The end objective of biomedical informatics is the coalescing of data, knowledge, and the tools necessary to apply that data and knowledge in the decision-making process, at the time and place that a decision needs to be made.

Optimization of the decision-making process for the selection of therapeutics to undergo clinical testing for spinal cord injury in the North American Clinical Trials Network.

PubMed

Guest, James; Harrop, James S; Aarabi, Bizhan; Grossman, Robert G; Fawcett, James W; Fehlings, Michael G; Tator, Charles H

2012-09-01

The North American Clinical Trials Network (NACTN) includes 9 clinical centers funded by the US Department of Defense and the Christopher Reeve Paralysis Foundation. Its purpose is to accelerate clinical testing of promising therapeutics in spinal cord injury (SCI) through the development of a robust interactive infrastructure. This structure includes key committees that serve to provide longitudinal guidance to the Network. These committees include the Executive, Data Management, and Neurological Outcome Assessments Committees, and the Therapeutic Selection Committee (TSC), which is the subject of this manuscript. The NACTN brings unique elements to the SCI field. The Network's stability is not restricted to a single clinical trial. Network members have diverse expertise and include experts in clinical care, clinical trial design and methodology, pharmacology, preclinical and clinical research, and advanced rehabilitation techniques. Frequent systematic communication is assigned a high value, as is democratic process, fairness and efficiency of decision making, and resource allocation. This article focuses on how decision making occurs within the TSC to rank alternative therapeutics according to 2 main variables: quality of the preclinical data set, and fit with the Network's aims and capabilities. This selection process is important because if the Network's resources are committed to a therapeutic, alternatives cannot be pursued. A proposed methodology includes a multicriteria decision analysis that uses a Multi-Attribute Global Inference of Quality matrix to quantify the process. To rank therapeutics, the TSC uses a series of consensus steps designed to reduce individual and group bias and limit subjectivity. Given the difficulties encountered by industry in completing clinical trials in SCI, stable collaborative not-for-profit consortia, such as the NACTN, may be essential to clinical progress in SCI. The evolution of the NACTN also offers substantial opportunity to refine decision making and group dynamics. Making the best possible decisions concerning therapeutics selection for trial testing is a cornerstone of the Network's function.

Fast, cheap and in control: spectral imaging with handheld devices

NASA Astrophysics Data System (ADS)

Gooding, Edward A.; Deutsch, Erik R.; Huehnerhoff, Joseph; Hajian, Arsen R.

2017-05-01

Remote sensing has moved out of the laboratory and into the real world. Instruments using reflection or Raman imaging modalities become faster, cheaper and more powerful annually. Enabling technologies include virtual slit spectrometer design, high power multimode diode lasers, fast open-loop scanning systems, low-noise IR-sensitive array detectors and low-cost computers with touchscreen interfaces. High-volume manufacturing assembles these components into inexpensive portable or handheld devices that make possible sophisticated decision-making based on robust data analytics. Examples include threat, hazmat and narcotics detection; remote gas sensing; biophotonic screening; environmental remediation and a host of other applications.

Advanced Computational Framework for Environmental Management ZEM, Version 1.x

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vesselinov, Velimir V.; O'Malley, Daniel; Pandey, Sachin

2016-11-04

Typically environmental management problems require analysis of large and complex data sets originating from concurrent data streams with different data collection frequencies and pedigree. These big data sets require on-the-fly integration into a series of models with different complexity for various types of model analyses where the data are applied as soft and hard model constraints. This is needed to provide fast iterative model analyses based on the latest available data to guide decision-making. Furthermore, the data and model are associated with uncertainties. The uncertainties are probabilistic (e.g. measurement errors) and non-probabilistic (unknowns, e.g. alternative conceptual models characterizing site conditions).more » To address all of these issues, we have developed an integrated framework for real-time data and model analyses for environmental decision-making called ZEM. The framework allows for seamless and on-the-fly integration of data and modeling results for robust and scientifically-defensible decision-making applying advanced decision analyses tools such as Bayesian- Information-Gap Decision Theory (BIG-DT). The framework also includes advanced methods for optimization that are capable of dealing with a large number of unknown model parameters, and surrogate (reduced order) modeling capabilities based on support vector regression techniques. The framework is coded in Julia, a state-of-the-art high-performance programing language (http://julialang.org). The ZEM framework is open-source and can be applied to any environmental management site. The framework will be open-source and released under GPL V3 license.« less

Nonlatching positive feedback enables robust bimodality by decoupling expression noise from the mean

DOE Office of Scientific and Technical Information (OSTI.GOV)

Razooky, Brandon S.; Cao, Youfang; Hansen, Maike M. K.

Fundamental to biological decision-making is the ability to generate bimodal expression patterns where two alternate expression states simultaneously exist. Here in this study, we use a combination of single-cell analysis and mathematical modeling to examine the sources of bimodality in the transcriptional program controlling HIV’s fate decision between active replication and viral latency. We find that the HIV Tat protein manipulates the intrinsic toggling of HIV’s promoter, the LTR, to generate bimodal ON-OFF expression, and that transcriptional positive feedback from Tat shifts and expands the regime of LTR bimodality. This result holds for both minimal synthetic viral circuits and full-lengthmore » virus. Strikingly, computational analysis indicates that the Tat circuit’s non-cooperative ‘non-latching’ feedback architecture is optimized to slow the promoter’s toggling and generate bimodality by stochastic extinction of Tat. In contrast to the standard Poisson model, theory and experiment show that non-latching positive feedback substantially dampens the inverse noise-mean relationship to maintain stochastic bimodality despite increasing mean-expression levels. Given the rapid evolution of HIV, the presence of a circuit optimized to robustly generate bimodal expression appears consistent with the hypothesis that HIV’s decision between active replication and latency provides a viral fitness advantage. More broadly, the results suggest that positive-feedback circuits may have evolved not only for signal amplification but also for robustly generating bimodality by decoupling expression fluctuations (noise) from mean expression levels.« less

Intelligent failure-tolerant control

NASA Technical Reports Server (NTRS)

Stengel, Robert F.

1991-01-01

An overview of failure-tolerant control is presented, beginning with robust control, progressing through parallel and analytical redundancy, and ending with rule-based systems and artificial neural networks. By design or implementation, failure-tolerant control systems are 'intelligent' systems. All failure-tolerant systems require some degrees of robustness to protect against catastrophic failure; failure tolerance often can be improved by adaptivity in decision-making and control, as well as by redundancy in measurement and actuation. Reliability, maintainability, and survivability can be enhanced by failure tolerance, although each objective poses different goals for control system design. Artificial intelligence concepts are helpful for integrating and codifying failure-tolerant control systems, not as alternatives but as adjuncts to conventional design methods.

A recurrent network mechanism of time integration in perceptual decisions.

PubMed

Wong, Kong-Fatt; Wang, Xiao-Jing

2006-01-25

Recent physiological studies using behaving monkeys revealed that, in a two-alternative forced-choice visual motion discrimination task, reaction time was correlated with ramping of spike activity of lateral intraparietal cortical neurons. The ramping activity appears to reflect temporal accumulation, on a timescale of hundreds of milliseconds, of sensory evidence before a decision is reached. To elucidate the cellular and circuit basis of such integration times, we developed and investigated a simplified two-variable version of a biophysically realistic cortical network model of decision making. In this model, slow time integration can be achieved robustly if excitatory reverberation is primarily mediated by NMDA receptors; our model with only fast AMPA receptors at recurrent synapses produces decision times that are not comparable with experimental observations. Moreover, we found two distinct modes of network behavior, in which decision computation by winner-take-all competition is instantiated with or without attractor states for working memory. Decision process is closely linked to the local dynamics, in the "decision space" of the system, in the vicinity of an unstable saddle steady state that separates the basins of attraction for the two alternative choices. This picture provides a rigorous and quantitative explanation for the dependence of performance and response time on the degree of task difficulty, and the reason for which reaction times are longer in error trials than in correct trials as observed in the monkey experiment. Our reduced two-variable neural model offers a simple yet biophysically plausible framework for studying perceptual decision making in general.

Confronting dynamics and uncertainty in optimal decision making for conservation

NASA Astrophysics Data System (ADS)

Williams, Byron K.; Johnson, Fred A.

2013-06-01

The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a critically endangered population through captive breeding, control of invasive species, construction of biodiversity reserves, design of landscapes to increase habitat connectivity, and resource exploitation. Although these decision making problems and their solutions present significant challenges, we suggest that a systematic and effective approach to dynamic decision making in conservation need not be an onerous undertaking. The requirements are shared with any systematic approach to decision making—a careful consideration of values, actions, and outcomes.

Financial Forecasting and Stochastic Modeling: Predicting the Impact of Business Decisions.

PubMed

Rubin, Geoffrey D; Patel, Bhavik N

2017-05-01

In health care organizations, effective investment of precious resources is critical to assure that the organization delivers high-quality and sustainable patient care within a supportive environment for patients, their families, and the health care providers. This holds true for organizations independent of size, from small practices to large health systems. For radiologists whose role is to oversee the delivery of imaging services and the interpretation, communication, and curation of imaging-informed information, business decisions influence where and how they practice, the tools available for image acquisition and interpretation, and ultimately their professional satisfaction. With so much at stake, physicians must understand and embrace the methods necessary to develop and interpret robust financial analyses so they effectively participate in and better understand decision making. This review discusses the financial drivers upon which health care organizations base investment decisions and the central role that stochastic financial modeling should play in support of strategically aligned capital investments. Given a health care industry that has been slow to embrace advanced financial analytics, a fundamental message of this review is that the skills and analytical tools are readily attainable and well worth the effort to implement in the interest of informed decision making. © RSNA, 2017 Online supplemental material is available for this article.

Framing of Uncertainty in Scientific Publications: Towards Recommendations for Decision Support

NASA Astrophysics Data System (ADS)

Guillaume, J. H. A.; Helgeson, C.; Elsawah, S.; Jakeman, A. J.; Kummu, M.

2016-12-01

Uncertainty is recognised as an essential issue in environmental decision making and decision support. As modellers, we notably use a variety of tools and techniques within an analysis, for example related to uncertainty quantification and model validation. We also address uncertainty by how we present results. For example, experienced modellers are careful to distinguish robust conclusions from those that need further work, and the precision of quantitative results is tailored to their accuracy. In doing so, the modeller frames how uncertainty should be interpreted by their audience. This is an area which extends beyond modelling to fields such as philosophy of science, semantics, discourse analysis, intercultural communication and rhetoric. We propose that framing of uncertainty deserves greater attention in the context of decision support, and that there are opportunities in this area for fundamental research, synthesis and knowledge transfer, development of teaching curricula, and significant advances in managing uncertainty in decision making. This presentation reports preliminary results of a study of framing practices. Specifically, we analyse the framing of uncertainty that is visible in the abstracts from a corpus of scientific articles. We do this through textual analysis of the content and structure of those abstracts. Each finding that appears in an abstract is classified according to the uncertainty framing approach used, using a classification scheme that was iteratively revised based on reflection and comparison amongst three coders. This analysis indicates how frequently the different framing approaches are used, and provides initial insights into relationships between frames, how the frames relate to interpretation of uncertainty, and how rhetorical devices are used by modellers to communicate uncertainty in their work. We propose initial hypotheses for how the resulting insights might influence decision support, and help advance decision making to better address uncertainty.

Policy, practice and decision making for zoonotic disease management: water and Cryptosporidium.

PubMed

Austin, Zoë; Alcock, Ruth E; Christley, Robert M; Haygarth, Philip M; Heathwaite, A Louise; Latham, Sophia M; Mort, Maggie; Oliver, David M; Pickup, Roger; Wastling, Jonathan M; Wynne, Brian

2012-04-01

Decision making for zoonotic disease management should be based on many forms of appropriate data and sources of evidence. However, the criteria and timing for policy response and the resulting management decisions are often altered when a disease outbreak occurs and captures full media attention. In the case of waterborne disease, such as the robust protozoa, Cryptosporidium spp, exposure can cause significant human health risks and preventing exposure by maintaining high standards of biological and chemical water quality remains a priority for water companies in the UK. Little has been documented on how knowledge and information is translated between the many stakeholders involved in the management of Cryptosporidium, which is surprising given the different drivers that have shaped management decisions. Such information, coupled with the uncertainties that surround these data is essential for improving future management strategies that minimise disease outbreaks. Here, we examine the interplay between scientific information, the media, and emergent government and company policies to examine these issues using qualitative and quantitative data relating to Cryptosporidium management decisions by a water company in the North West of England. Our results show that political and media influences are powerful drivers of management decisions if fuelled by high profile outbreaks. Furthermore, the strength of the scientific evidence is often constrained by uncertainties in the data, and in the way knowledge is translated between policy levels during established risk management procedures. In particular, under or over-estimating risk during risk assessment procedures together with uncertainty regarding risk factors within the wider environment, was found to restrict the knowledge-base for decision-making in Cryptosporidium management. Our findings highlight some key current and future challenges facing the management of such diseases that are widely applicable to other risk management situations. Copyright © 2011 Elsevier Ltd. All rights reserved.

Risky decisions and their consequences: neural processing by boys with Antisocial Substance Disorder.

PubMed

Crowley, Thomas J; Dalwani, Manish S; Mikulich-Gilbertson, Susan K; Du, Yiping P; Lejuez, Carl W; Raymond, Kristen M; Banich, Marie T

2010-09-22

Adolescents with conduct and substance problems ("Antisocial Substance Disorder" (ASD)) repeatedly engage in risky antisocial and drug-using behaviors. We hypothesized that, during processing of risky decisions and resulting rewards and punishments, brain activation would differ between abstinent ASD boys and comparison boys. We compared 20 abstinent adolescent male patients in treatment for ASD with 20 community controls, examining rapid event-related blood-oxygen-level-dependent (BOLD) responses during functional magnetic resonance imaging. In 90 decision trials participants chose to make either a cautious response that earned one cent, or a risky response that would either gain 5 cents or lose 10 cents; odds of losing increased as the game progressed. We also examined those times when subjects experienced wins, or separately losses, from their risky choices. We contrasted decision trials against very similar comparison trials requiring no decisions, using whole-brain BOLD-response analyses of group differences, corrected for multiple comparisons. During decision-making ASD boys showed hypoactivation in numerous brain regions robustly activated by controls, including orbitofrontal and dorsolateral prefrontal cortices, anterior cingulate, basal ganglia, insula, amygdala, hippocampus, and cerebellum. While experiencing wins, ASD boys had significantly less activity than controls in anterior cingulate, temporal regions, and cerebellum, with more activity nowhere. During losses ASD boys had significantly more activity than controls in orbitofrontal cortex, dorsolateral prefrontal cortex, brain stem, and cerebellum, with less activity nowhere. Adolescent boys with ASD had extensive neural hypoactivity during risky decision-making, coupled with decreased activity during reward and increased activity during loss. These neural patterns may underlie the dangerous, excessive, sustained risk-taking of such boys. The findings suggest that the dysphoria, reward insensitivity, and suppressed neural activity observed among older addicted persons also characterize youths early in the development of substance use disorders.

Potential uses of Bayesian networks as tools for synthesis of systematic reviews of complex interventions.

PubMed

Stewart, G B; Mengersen, K; Meader, N

2014-03-01

Bayesian networks (BNs) are tools for representing expert knowledge or evidence. They are especially useful for synthesising evidence or belief concerning a complex intervention, assessing the sensitivity of outcomes to different situations or contextual frameworks and framing decision problems that involve alternative types of intervention. Bayesian networks are useful extensions to logic maps when initiating a review or to facilitate synthesis and bridge the gap between evidence acquisition and decision-making. Formal elicitation techniques allow development of BNs on the basis of expert opinion. Such applications are useful alternatives to 'empty' reviews, which identify knowledge gaps but fail to support decision-making. Where review evidence exists, it can inform the development of a BN. We illustrate the construction of a BN using a motivating example that demonstrates how BNs can ensure coherence, transparently structure the problem addressed by a complex intervention and assess sensitivity to context, all of which are critical components of robust reviews of complex interventions. We suggest that BNs should be utilised to routinely synthesise reviews of complex interventions or empty reviews where decisions must be made despite poor evidence. Copyright © 2013 John Wiley & Sons, Ltd.

A multicriteria-based methodology for site prioritisation in sediment management.

PubMed

Alvarez-Guerra, Manuel; Viguri, Javier R; Voulvoulis, Nikolaos

2009-08-01

Decision-making for sediment management is a complex task that incorporates the selections of areas for remediation and the assessment of options for any mitigation required. The application of Multicriteria Analysis (MCA) to rank different areas, according to their need for sediment management, provides a great opportunity for prioritisation, a first step in an integrated methodology that finally aims to assess and select suitable alternatives for managing the identified priority sites. This paper develops a methodology that starts with the delimitation of management units within areas of study, followed by the application of MCA methods that allows ranking of these management units, according to their need for remediation. This proposed process considers not only scientific evidence on sediment quality, but also other relevant aspects such as social and economic criteria associated with such decisions. This methodology is illustrated with its application to the case study area of the Bay of Santander, in northern Spain, highlighting some of the implications of utilising different MCA methods in the process. It also uses site-specific data to assess the subjectivity in the decision-making process, mainly reflected through the assignment of the criteria weights and uncertainties in the criteria scores. Analysis of the sensitivity of the results to these factors is used as a way to assess the stability and robustness of the ranking as a first step of the sediment management decision-making process.

Strategic Technology Investment Analysis: An Integrated System Approach

NASA Technical Reports Server (NTRS)

Adumitroaie, V.; Weisbin, C. R.

2010-01-01

Complex technology investment decisions within NASA are increasingly difficult to make such that the end results are satisfying the technical objectives and all the organizational constraints. Due to a restricted science budget environment and numerous required technology developments, the investment decisions need to take into account not only the functional impact on the program goals, but also development uncertainties and cost variations along with maintaining a healthy workforce. This paper describes an approach for optimizing and qualifying technology investment portfolios from the perspective of an integrated system model. The methodology encompasses multi-attribute decision theory elements and sensitivity analysis. The evaluation of the degree of robustness of the recommended portfolio provides the decision-maker with an array of viable selection alternatives, which take into account input uncertainties and possibly satisfy nontechnical constraints. The methodology is presented in the context of assessing capability development portfolios for NASA technology programs.

A Distributed Ensemble Approach for Mining Healthcare Data under Privacy Constraints

PubMed Central

Li, Yan; Bai, Changxin; Reddy, Chandan K.

2015-01-01

In recent years, electronic health records (EHRs) have been widely adapted at many healthcare facilities in an attempt to improve the quality of patient care and increase the productivity and efficiency of healthcare delivery. These EHRs can accurately diagnose diseases if utilized appropriately. While the EHRs can potentially resolve many of the existing problems associated with disease diagnosis, one of the main obstacles in effectively using them is the patient privacy and sensitivity of the medical information available in the EHR. Due to these concerns, even if the EHRs are available for storage and retrieval purposes, sharing of the patient records between different healthcare facilities has become a major concern and has hampered some of the effective advantages of using EHRs. Due to this lack of data sharing, most of the facilities aim at building clinical decision support systems using limited amount of patient data from their own EHR systems to provide important diagnosis related decisions. It becomes quite infeasible for a newly established healthcare facility to build a robust decision making system due to the lack of sufficient patient records. However, to make effective decisions from clinical data, it is indispensable to have large amounts of data to train the decision models. In this regard, there are conflicting objectives of preserving patient privacy and having sufficient data for modeling and decision making. To handle such disparate goals, we develop two adaptive distributed privacy-preserving algorithms based on a distributed ensemble strategy. The basic idea of our approach is to build an elegant model for each participating facility to accurately learn the data distribution, and then can transfer the useful healthcare knowledge acquired on their data from these participators in the form of their own decision models without revealing and sharing the patient-level sensitive data, thus protecting patient privacy. We demonstrate that our approach can successfully build accurate and robust prediction models, under privacy constraints, using the healthcare data collected from different geographical locations. We demonstrate the performance of our method using the Type-2 diabetes EHRs accumulated from multiple sources from all fifty states in the U.S. Our method was evaluated on diagnosing diabetes in the presence of insufficient number of patient records from certain regions without revealing the actual patient data from other regions. Using the proposed approach, we also discovered the important biomarkers, both universal and region-specific, and validated the selected biomarkers using the biomedical literature. PMID:26681811

A Distributed Ensemble Approach for Mining Healthcare Data under Privacy Constraints.

PubMed

Li, Yan; Bai, Changxin; Reddy, Chandan K

2016-02-10

In recent years, electronic health records (EHRs) have been widely adapted at many healthcare facilities in an attempt to improve the quality of patient care and increase the productivity and efficiency of healthcare delivery. These EHRs can accurately diagnose diseases if utilized appropriately. While the EHRs can potentially resolve many of the existing problems associated with disease diagnosis, one of the main obstacles in effectively using them is the patient privacy and sensitivity of the medical information available in the EHR. Due to these concerns, even if the EHRs are available for storage and retrieval purposes, sharing of the patient records between different healthcare facilities has become a major concern and has hampered some of the effective advantages of using EHRs. Due to this lack of data sharing, most of the facilities aim at building clinical decision support systems using limited amount of patient data from their own EHR systems to provide important diagnosis related decisions. It becomes quite infeasible for a newly established healthcare facility to build a robust decision making system due to the lack of sufficient patient records. However, to make effective decisions from clinical data, it is indispensable to have large amounts of data to train the decision models. In this regard, there are conflicting objectives of preserving patient privacy and having sufficient data for modeling and decision making. To handle such disparate goals, we develop two adaptive distributed privacy-preserving algorithms based on a distributed ensemble strategy. The basic idea of our approach is to build an elegant model for each participating facility to accurately learn the data distribution, and then can transfer the useful healthcare knowledge acquired on their data from these participators in the form of their own decision models without revealing and sharing the patient-level sensitive data, thus protecting patient privacy. We demonstrate that our approach can successfully build accurate and robust prediction models, under privacy constraints, using the healthcare data collected from different geographical locations. We demonstrate the performance of our method using the Type-2 diabetes EHRs accumulated from multiple sources from all fifty states in the U.S. Our method was evaluated on diagnosing diabetes in the presence of insufficient number of patient records from certain regions without revealing the actual patient data from other regions. Using the proposed approach, we also discovered the important biomarkers, both universal and region-specific, and validated the selected biomarkers using the biomedical literature.

A meta-analysis of decision-making and attention in adults with ADHD.

PubMed

Mowinckel, Athanasia M; Pedersen, Mads Lund; Eilertsen, Espen; Biele, Guido

2015-05-01

Deficient reward processing has gained attention as an important aspect of ADHD, but little is known about reward-based decision-making (DM) in adults with ADHD. This article summarizes research on DM in adult ADHD and contextualizes DM deficits by comparing them to attention deficits. Meta-analytic methods were used to calculate average effect sizes for different DM domains and continuous performance task (CPT) measures. None of the 59 included studies (DM: 12 studies; CPT: 43; both: 4) had indications of publication bias. DM and CPT measures showed robust, small to medium effects. Large effect sizes were found for a drift diffusion model analysis of the CPT. The results support the existence of DM deficits in adults with ADHD, which are of similar magnitude as attention deficits. These findings warrant further examination of DM in adults with ADHD to improve the understanding of underlying neurocognitive mechanisms. © 2014 SAGE Publications.

Defense Resource Planning Under Uncertainty: An Application of Robust Decision Making to Munitions Mix Planning

DTIC Science & Technology

2016-02-01

In addition , the parser updates some parameters based on uncertainties. For example, Analytica was very slow to update Pk values based on...moderate range. The additional security environments helped to fill gaps in lower severity. Weapons Effectiveness Pk values were modified to account for two...project is to help improve the value and character of defense resource planning in an era of growing uncertainty and complex strategic challenges

A Model of Human Teamwork for Agent-Assisted Search Operations

DTIC Science & Technology

2008-04-01

agent technology in providing effective team assistance , better understanding of robust human-agent teamwork is crucial. The goal of our research project...to various unexpected events. In order to fulfil the promise of agent technology in providing effective team assistance , better understanding of...distributed decision making. In Command and Control Research and Technology Symposium, 2004. A MODEL OF AGENT- ASSISTED SEARCH OPERATIONS 20 - 20 PUB REF NBR (e.g. RTO-MP-IST-999)

Expectations Do Not Alter Early Sensory Processing during Perceptual Decision-Making.

PubMed

Rungratsameetaweemana, Nuttida; Itthipuripat, Sirawaj; Salazar, Annalisa; Serences, John T

2018-06-13

Two factors play important roles in shaping perception: the allocation of selective attention to behaviorally relevant sensory features, and prior expectations about regularities in the environment. Signal detection theory proposes distinct roles of attention and expectation on decision-making such that attention modulates early sensory processing, whereas expectation influences the selection and execution of motor responses. Challenging this classic framework, recent studies suggest that expectations about sensory regularities enhance the encoding and accumulation of sensory evidence during decision-making. However, it is possible, that these findings reflect well documented attentional modulations in visual cortex. Here, we tested this framework in a group of male and female human participants by examining how expectations about stimulus features (orientation and color) and expectations about motor responses impacted electroencephalography (EEG) markers of early sensory processing and the accumulation of sensory evidence during decision-making (the early visual negative potential and the centro-parietal positive potential, respectively). We first demonstrate that these markers are sensitive to changes in the amount of sensory evidence in the display. Then we show, counter to recent findings, that neither marker is modulated by either feature or motor expectations, despite a robust effect of expectations on behavior. Instead, violating expectations about likely sensory features and motor responses impacts posterior alpha and frontal theta oscillations, signals thought to index overall processing time and cognitive conflict. These findings are inconsistent with recent theoretical accounts and suggest instead that expectations primarily influence decisions by modulating post-perceptual stages of information processing. SIGNIFICANCE STATEMENT Expectations about likely features or motor responses play an important role in shaping behavior. Classic theoretical frameworks posit that expectations modulate decision-making by biasing late stages of decision-making including the selection and execution of motor responses. In contrast, recent accounts suggest that expectations also modulate decisions by improving the quality of early sensory processing. However, these effects could instead reflect the influence of selective attention. Here we examine the effect of expectations about sensory features and motor responses on a set of electroencephalography (EEG) markers that index early sensory processing and later post-perceptual processing. Counter to recent empirical results, expectations have little effect on early sensory processing but instead modulate EEG markers of time-on-task and cognitive conflict. Copyright © 2018 the authors 0270-6474/18/385632-17$15.00/0.

Supporting Fisheries Management by Means of Complex Models: Can We Point out Isles of Robustness in a Sea of Uncertainty?

PubMed Central

Gasche, Loïc; Mahévas, Stéphanie; Marchal, Paul

2013-01-01

Ecosystems are usually complex, nonlinear and strongly influenced by poorly known environmental variables. Among these systems, marine ecosystems have high uncertainties: marine populations in general are known to exhibit large levels of natural variability and the intensity of fishing efforts can change rapidly. These uncertainties are a source of risks that threaten the sustainability of both fish populations and fishing fleets targeting them. Appropriate management measures have to be found in order to reduce these risks and decrease sensitivity to uncertainties. Methods have been developed within decision theory that aim at allowing decision making under severe uncertainty. One of these methods is the information-gap decision theory. The info-gap method has started to permeate ecological modelling, with recent applications to conservation. However, these practical applications have so far been restricted to simple models with analytical solutions. Here we implement a deterministic approach based on decision theory in a complex model of the Eastern English Channel. Using the ISIS-Fish modelling platform, we model populations of sole and plaice in this area. We test a wide range of values for ecosystem, fleet and management parameters. From these simulations, we identify management rules controlling fish harvesting that allow reaching management goals recommended by ICES (International Council for the Exploration of the Sea) working groups while providing the highest robustness to uncertainties on ecosystem parameters. PMID:24204873

Supporting fisheries management by means of complex models: can we point out isles of robustness in a sea of uncertainty?

PubMed

Gasche, Loïc; Mahévas, Stéphanie; Marchal, Paul

2013-01-01

Ecosystems are usually complex, nonlinear and strongly influenced by poorly known environmental variables. Among these systems, marine ecosystems have high uncertainties: marine populations in general are known to exhibit large levels of natural variability and the intensity of fishing efforts can change rapidly. These uncertainties are a source of risks that threaten the sustainability of both fish populations and fishing fleets targeting them. Appropriate management measures have to be found in order to reduce these risks and decrease sensitivity to uncertainties. Methods have been developed within decision theory that aim at allowing decision making under severe uncertainty. One of these methods is the information-gap decision theory. The info-gap method has started to permeate ecological modelling, with recent applications to conservation. However, these practical applications have so far been restricted to simple models with analytical solutions. Here we implement a deterministic approach based on decision theory in a complex model of the Eastern English Channel. Using the ISIS-Fish modelling platform, we model populations of sole and plaice in this area. We test a wide range of values for ecosystem, fleet and management parameters. From these simulations, we identify management rules controlling fish harvesting that allow reaching management goals recommended by ICES (International Council for the Exploration of the Sea) working groups while providing the highest robustness to uncertainties on ecosystem parameters.

Analyzing the quality robustness of chemotherapy plans with respect to model uncertainties.

PubMed

Hoffmann, Anna; Scherrer, Alexander; Küfer, Karl-Heinz

2015-01-01

Mathematical models of chemotherapy planning problems contain various biomedical parameters, whose values are difficult to quantify and thus subject to some uncertainty. This uncertainty propagates into the therapy plans computed on these models, which poses the question of robustness to the expected therapy quality. This work introduces a combined approach for analyzing the quality robustness of plans in terms of dosing levels with respect to model uncertainties in chemotherapy planning. It uses concepts from multi-criteria decision making for studying parameters related to the balancing between the different therapy goals, and concepts from sensitivity analysis for the examination of parameters describing the underlying biomedical processes and their interplay. This approach allows for a profound assessment of a therapy plan, how stable its quality is with respect to parametric changes in the used mathematical model. Copyright © 2014 Elsevier Inc. All rights reserved.

Decision support for evidence-based integration of disease control: A proof of concept for malaria and schistosomiasis

PubMed Central

Graeden, Ellie; Kerr, Justin; Sorrell, Erin M.; Katz, Rebecca

2018-01-01

Managing infectious disease requires rapid and effective response to support decision making. The decisions are complex and require understanding of the diseases, disease intervention and control measures, and the disease-relevant characteristics of the local community. Though disease modeling frameworks have been developed to address these questions, the complexity of current models presents a significant barrier to community-level decision makers in using the outputs of the most scientifically robust methods to support pragmatic decisions about implementing a public health response effort, even for endemic diseases with which they are already familiar. Here, we describe the development of an application available on the internet, including from mobile devices, with a simple user interface, to support on-the-ground decision-making for integrating disease control programs, given local conditions and practical constraints. The model upon which the tool is built provides predictive analysis for the effectiveness of integration of schistosomiasis and malaria control, two diseases with extensive geographical and epidemiological overlap, and which result in significant morbidity and mortality in affected regions. Working with data from countries across sub-Saharan Africa and the Middle East, we present a proof-of-principle method and corresponding prototype tool to provide guidance on how to optimize integration of vertical disease control programs. This method and tool demonstrate significant progress in effectively translating the best available scientific models to support practical decision making on the ground with the potential to significantly increase the efficacy and cost-effectiveness of disease control. Author summary Designing and implementing effective programs for infectious disease control requires complex decision-making, informed by an understanding of the diseases, the types of disease interventions and control measures available, and the disease-relevant characteristics of the local community. Though disease modeling frameworks have been developed to address these questions and support decision-making, the complexity of current models presents a significant barrier to on-the-ground end users. The picture is further complicated when considering approaches for integration of different disease control programs, where co-infection dynamics, treatment interactions, and other variables must also be taken into account. Here, we describe the development of an application available on the internet with a simple user interface, to support on-the-ground decision-making for integrating disease control, given local conditions and practical constraints. The model upon which the tool is built provides predictive analysis for the effectiveness of integration of schistosomiasis and malaria control, two diseases with extensive geographical and epidemiological overlap. This proof-of-concept method and tool demonstrate significant progress in effectively translating the best available scientific models to support pragmatic decision-making on the ground, with the potential to significantly increase the impact and cost-effectiveness of disease control. PMID:29649260

Uncertainty in Agricultural Impact Assessment

NASA Technical Reports Server (NTRS)

Wallach, Daniel; Mearns, Linda O.; Rivington, Michael; Antle, John M.; Ruane, Alexander C.

2014-01-01

This chapter considers issues concerning uncertainty associated with modeling and its use within agricultural impact assessments. Information about uncertainty is important for those who develop assessment methods, since that information indicates the need for, and the possibility of, improvement of the methods and databases. Such information also allows one to compare alternative methods. Information about the sources of uncertainties is an aid in prioritizing further work on the impact assessment method. Uncertainty information is also necessary for those who apply assessment methods, e.g., for projecting climate change impacts on agricultural production and for stakeholders who want to use the results as part of a decision-making process (e.g., for adaptation planning). For them, uncertainty information indicates the degree of confidence they can place in the simulated results. Quantification of uncertainty also provides stakeholders with an important guideline for making decisions that are robust across the known uncertainties. Thus, uncertainty information is important for any decision based on impact assessment. Ultimately, we are interested in knowledge about uncertainty so that information can be used to achieve positive outcomes from agricultural modeling and impact assessment.

Individual vision and peak distribution in collective actions

NASA Astrophysics Data System (ADS)

Lu, Peng

2017-06-01

People make decisions on whether they should participate as participants or not as free riders in collective actions with heterogeneous visions. Besides of the utility heterogeneity and cost heterogeneity, this work includes and investigates the effect of vision heterogeneity by constructing a decision model, i.e. the revised peak model of participants. In this model, potential participants make decisions under the joint influence of utility, cost, and vision heterogeneities. The outcomes of simulations indicate that vision heterogeneity reduces the values of peaks, and the relative variance of peaks is stable. Under normal distributions of vision heterogeneity and other factors, the peaks of participants are normally distributed as well. Therefore, it is necessary to predict distribution traits of peaks based on distribution traits of related factors such as vision heterogeneity and so on. We predict the distribution of peaks with parameters of both mean and standard deviation, which provides the confident intervals and robust predictions of peaks. Besides, we validate the peak model of via the Yuyuan Incident, a real case in China (2014), and the model works well in explaining the dynamics and predicting the peak of real case.

Applications of fuzzy logic to control and decision making

NASA Technical Reports Server (NTRS)

Lea, Robert N.; Jani, Yashvant

1991-01-01

Long range space missions will require high operational efficiency as well as autonomy to enhance the effectivity of performance. Fuzzy logic technology has been shown to be powerful and robust in interpreting imprecise measurements and generating appropriate control decisions for many space operations. Several applications are underway, studying the fuzzy logic approach to solving control and decision making problems. Fuzzy logic algorithms for relative motion and attitude control have been developed and demonstrated for proximity operations. Based on this experience, motion control algorithms that include obstacle avoidance were developed for a Mars Rover prototype for maneuvering during the sample collection process. A concept of an intelligent sensor system that can identify objects and track them continuously and learn from its environment is under development to support traffic management and proximity operations around the Space Station Freedom. For safe and reliable operation of Lunar/Mars based crew quarters, high speed controllers with ability to combine imprecise measurements from several sensors is required. A fuzzy logic approach that uses high speed fuzzy hardware chips is being studied.

Drugs, sex, money and power: an HPV vaccine case study.

PubMed

Haas, Marion; Ashton, Toni; Blum, Kerstin; Christiansen, Terkel; Conis, Elena; Crivelli, Luca; Lim, Meng Kin; Lisac, Melanie; Macadam, Margaret; Schlette, Sophia

2009-10-01

In this paper we compare the experiences of seven industrialized countries in considering approval and introduction of the world's first cervical cancer-preventing vaccine. Based on case studies, articles from public agencies, professional journals and newspapers we analyse the public debate about the vaccine, examine positions of stakeholder groups and their influence on the course and outcome of this policy process. The analysis shows that the countries considered here approved the vaccine and established related immunization programs exceptionally quickly even though there still exist many uncertainties as to the vaccine's long-term effectiveness, cost-effectiveness and safety. Some countries even bypassed established decision-making processes. The voice of special interest groups has been prominent in all countries, drawing on societal values and fears of the public. Even though positions differed among countries, all seven decided to publicly fund the vaccine, illustrating a widespread convergence of interests. It is important that decision-makers adhere to transparent and robust guidelines in making funding decisions in the future to avoid capture by vested interests and potentially negative effects on access and equity.

Risky business: rhesus monkeys exhibit persistent preferences for risky options.

PubMed

Xu, Eric R; Kralik, Jerald D

2014-01-01

Rhesus monkeys have been shown to prefer risky over safe options in experiential decision-making tasks. These findings might be due, however, to specific contextual factors, such as small amounts of fluid reward and minimal costs for risk-taking. To better understand the factors affecting decision-making under risk in rhesus monkeys, we tested multiple factors designed to increase the stakes including larger reward amounts, distinct food items rather than fluid reward, a smaller number of trials per session, and risky options with greater variation that also included non-rewarded outcomes. We found a consistent preference for risky options, except when the expected value of the safe option was greater than the risky option. Thus, with equivalent mean utilities between the safe and risky options, rhesus monkeys appear to have a robust preference for the risky options in a broad range of circumstances, akin to the preferences found in human children and some adults in similar tasks. One account for this result is that monkeys make their choices based on the salience of the largest payoff, without integrating likelihood and value across trials. A related idea is that they fail to override an impulsive tendency to select the option with the potential to obtain the highest possible outcome. Our results rule out strict versions of both accounts and contribute to an understanding of the diversity of risky decision-making among primates.

Robust learning for optimal treatment decision with NP-dimensionality

PubMed Central

Shi, Chengchun; Song, Rui; Lu, Wenbin

2016-01-01

In order to identify important variables that are involved in making optimal treatment decision, Lu, Zhang and Zeng (2013) proposed a penalized least squared regression framework for a fixed number of predictors, which is robust against the misspecification of the conditional mean model. Two problems arise: (i) in a world of explosively big data, effective methods are needed to handle ultra-high dimensional data set, for example, with the dimension of predictors is of the non-polynomial (NP) order of the sample size; (ii) both the propensity score and conditional mean models need to be estimated from data under NP dimensionality. In this paper, we propose a robust procedure for estimating the optimal treatment regime under NP dimensionality. In both steps, penalized regressions are employed with the non-concave penalty function, where the conditional mean model of the response given predictors may be misspecified. The asymptotic properties, such as weak oracle properties, selection consistency and oracle distributions, of the proposed estimators are investigated. In addition, we study the limiting distribution of the estimated value function for the obtained optimal treatment regime. The empirical performance of the proposed estimation method is evaluated by simulations and an application to a depression dataset from the STAR*D study. PMID:28781717

Adolescent neural response to reward is related to participant sex and task motivation.

PubMed

Alarcón, Gabriela; Cservenka, Anita; Nagel, Bonnie J

2017-02-01

Risky decision making is prominent during adolescence, perhaps contributed to by heightened sensation seeking and ongoing maturation of reward and dopamine systems in the brain, which are, in part, modulated by sex hormones. In this study, we examined sex differences in the neural substrates of reward sensitivity during a risky decision-making task and hypothesized that compared with girls, boys would show heightened brain activation in reward-relevant regions, particularly the nucleus accumbens, during reward receipt. Further, we hypothesized that testosterone and estradiol levels would mediate this sex difference. Moreover, we predicted boys would make more risky choices on the task. While boys showed increased nucleus accumbens blood oxygen level-dependent (BOLD) response relative to girls, sex hormones did not mediate this effect. As predicted, boys made a higher percentage of risky decisions during the task. Interestingly, boys also self-reported more motivation to perform well and earn money on the task, while girls self-reported higher state anxiety prior to the scan session. Motivation to earn money partially mediated the effect of sex on nucleus accumbens activity during reward. Previous research shows that increased motivation and salience of reinforcers is linked with more robust striatal BOLD response, therefore psychosocial factors, in addition to sex, may play an important role in reward sensitivity. Elucidating neurobiological mechanisms that support adolescent sex differences in risky decision making has important implications for understanding individual differences that lead to advantageous and adverse behaviors that affect health outcomes. Copyright © 2016 Elsevier Inc. All rights reserved.

Decision Analysis Methods Used to Make Appropriate Investments in Human Exploration Capabilities and Technologies

NASA Technical Reports Server (NTRS)

Williams-Byrd, Julie; Arney, Dale C.; Hay, Jason; Reeves, John D.; Craig, Douglas

2016-01-01

NASA is transforming human spaceflight. The Agency is shifting from an exploration-based program with human activities in low Earth orbit (LEO) and targeted robotic missions in deep space to a more sustainable and integrated pioneering approach. Through pioneering, NASA seeks to address national goals to develop the capacity for people to work, learn, operate, live, and thrive safely beyond Earth for extended periods of time. However, pioneering space involves daunting technical challenges of transportation, maintaining health, and enabling crew productivity for long durations in remote, hostile, and alien environments. Prudent investments in capability and technology developments, based on mission need, are critical for enabling a campaign of human exploration missions. There are a wide variety of capabilities and technologies that could enable these missions, so it is a major challenge for NASA's Human Exploration and Operations Mission Directorate (HEOMD) to make knowledgeable portfolio decisions. It is critical for this pioneering initiative that these investment decisions are informed with a prioritization process that is robust and defensible. It is NASA's role to invest in targeted technologies and capabilities that would enable exploration missions even though specific requirements have not been identified. To inform these investments decisions, NASA's HEOMD has supported a variety of analysis activities that prioritize capabilities and technologies. These activities are often based on input from subject matter experts within the NASA community who understand the technical challenges of enabling human exploration missions. This paper will review a variety of processes and methods that NASA has used to prioritize and rank capabilities and technologies applicable to human space exploration. The paper will show the similarities in the various processes and showcase instances were customer specified priorities force modifications to the process. Specifically, this paper will describe the processes that the NASA Langley Research Center (LaRC) Technology Assessment and Integration Team (TAIT) has used for several years and how those processes have been customized to meet customer needs while staying robust and defensible. This paper will show how HEOMD uses these analyses results to assist with making informed portfolio investment decisions. The paper will also highlight which human exploration capabilities and technologies typically rank high regardless of the specific design reference mission. The paper will conclude by describing future capability and technology ranking activities that will continue o leverage subject matter experts (SME) input while also incorporating more model-based analysis.

Suboptimal choice in rats: incentive salience attribution promotes maladaptive decision-making

PubMed Central

Chow, Jonathan J; Smith, Aaron P; Wilson, A George; Zentall, Thomas R; Beckmann, Joshua S

2016-01-01

Stimuli that are more predictive of subsequent reward also function as better conditioned reinforcers. Moreover, stimuli attributed with incentive salience function as more robust conditioned reinforcers. Some theories have suggested that conditioned reinforcement plays an important role in promoting suboptimal choice behavior, like gambling. The present experiments examined how different stimuli, those attributed with incentive salience versus those without, can function in tandem with stimulus-reward predictive utility to promote maladaptive decision-making in rats. One group of rats had lights associated with goal-tracking as the reward-predictive stimuli and another had levers associated with sign-tracking as the reward-predictive stimuli. All rats were first trained on a choice procedure in which the expected value across both alternatives was equivalent but differed in their stimulus-reward predictive utility. Next, the expected value across both alternatives was systematically changed so that the alternative with greater stimulus-reward predictive utility was suboptimal in regard to primary reinforcement. The results demonstrate that in order to obtain suboptimal choice behavior, incentive salience alongside strong stimulus-reward predictive utility may be necessary; thus, maladaptive decision-making can be driven more by the value attributed to stimuli imbued with incentive salience that reliably predict a reward rather than the reward itself. PMID:27993692

Suboptimal choice in rats: Incentive salience attribution promotes maladaptive decision-making.

PubMed

Chow, Jonathan J; Smith, Aaron P; Wilson, A George; Zentall, Thomas R; Beckmann, Joshua S

2017-03-01

Stimuli that are more predictive of subsequent reward also function as better conditioned reinforcers. Moreover, stimuli attributed with incentive salience function as more robust conditioned reinforcers. Some theories have suggested that conditioned reinforcement plays an important role in promoting suboptimal choice behavior, like gambling. The present experiments examined how different stimuli, those attributed with incentive salience versus those without, can function in tandem with stimulus-reward predictive utility to promote maladaptive decision-making in rats. One group of rats had lights associated with goal-tracking as the reward-predictive stimuli and another had levers associated with sign-tracking as the reward-predictive stimuli. All rats were first trained on a choice procedure in which the expected value across both alternatives was equivalent but differed in their stimulus-reward predictive utility. Next, the expected value across both alternatives was systematically changed so that the alternative with greater stimulus-reward predictive utility was suboptimal in regard to primary reinforcement. The results demonstrate that in order to obtain suboptimal choice behavior, incentive salience alongside strong stimulus-reward predictive utility may be necessary; thus, maladaptive decision-making can be driven more by the value attributed to stimuli imbued with incentive salience that reliably predict a reward rather than the reward itself. Copyright © 2016 Elsevier B.V. All rights reserved.

Designing and implementing transparency for real time inspection of autonomous robots

NASA Astrophysics Data System (ADS)

Theodorou, Andreas; Wortham, Robert H.; Bryson, Joanna J.

2017-07-01

The EPSRC's Principles of Robotics advises the implementation of transparency in robotic systems, however research related to AI transparency is in its infancy. This paper introduces the reader of the importance of having transparent inspection of intelligent agents and provides guidance for good practice when developing such agents. By considering and expanding upon other prominent definitions found in literature, we provide a robust definition of transparency as a mechanism to expose the decision-making of a robot. The paper continues by addressing potential design decisions developers need to consider when designing and developing transparent systems. Finally, we describe our new interactive intelligence editor, designed to visualise, develop and debug real-time intelligence.

Flood risk assessment and robust management under deep uncertainty: Application to Dhaka City

NASA Astrophysics Data System (ADS)

Mojtahed, Vahid; Gain, Animesh Kumar; Giupponi, Carlo

2014-05-01

The socio-economic changes as well as climatic changes have been the main drivers of uncertainty in environmental risk assessment and in particular flood. The level of future uncertainty that researchers face when dealing with problems in a future perspective with focus on climate change is known as Deep Uncertainty (also known as Knightian uncertainty), since nobody has already experienced and undergone those changes before and our knowledge is limited to the extent that we have no notion of probabilities, and therefore consolidated risk management approaches have limited potential.. Deep uncertainty is referred to circumstances that analysts and experts do not know or parties to decision making cannot agree on: i) the appropriate models describing the interaction among system variables, ii) probability distributions to represent uncertainty about key parameters in the model 3) how to value the desirability of alternative outcomes. The need thus emerges to assist policy-makers by providing them with not a single and optimal solution to the problem at hand, such as crisp estimates for the costs of damages of natural hazards considered, but instead ranges of possible future costs, based on the outcomes of ensembles of assessment models and sets of plausible scenarios. Accordingly, we need to substitute optimality as a decision criterion with robustness. Under conditions of deep uncertainty, the decision-makers do not have statistical and mathematical bases to identify optimal solutions, while instead they should prefer to implement "robust" decisions that perform relatively well over all conceivable outcomes out of all future unknown scenarios. Under deep uncertainty, analysts cannot employ probability theory or other statistics that usually can be derived from observed historical data and therefore, we turn to non-statistical measures such as scenario analysis. We construct several plausible scenarios with each scenario being a full description of what may happen in future and based on a meaningful synthesis of parameters' values with control of their correlations for maintaining internal consistencies. This paper aims at incorporating a set of data mining and sampling tools to assess uncertainty of model outputs under future climatic and socio-economic changes for Dhaka city and providing a decision support system for robust flood management and mitigation policies. After constructing an uncertainty matrix to identify the main sources of uncertainty for Dhaka City, we identify several hazard and vulnerability maps based on future climatic and socio-economic scenarios. The vulnerability of each flood management alternative under different set of scenarios is determined and finally the robustness of each plausible solution considered is defined based on the above assessment.

Making robust policy decisions using global biodiversity indicators.

PubMed

Nicholson, Emily; Collen, Ben; Barausse, Alberto; Blanchard, Julia L; Costelloe, Brendan T; Sullivan, Kathryn M E; Underwood, Fiona M; Burn, Robert W; Fritz, Steffen; Jones, Julia P G; McRae, Louise; Possingham, Hugh P; Milner-Gulland, E J

2012-01-01

In order to influence global policy effectively, conservation scientists need to be able to provide robust predictions of the impact of alternative policies on biodiversity and measure progress towards goals using reliable indicators. We present a framework for using biodiversity indicators predictively to inform policy choices at a global level. The approach is illustrated with two case studies in which we project forwards the impacts of feasible policies on trends in biodiversity and in relevant indicators. The policies are based on targets agreed at the Convention on Biological Diversity (CBD) meeting in Nagoya in October 2010. The first case study compares protected area policies for African mammals, assessed using the Red List Index; the second example uses the Living Planet Index to assess the impact of a complete halt, versus a reduction, in bottom trawling. In the protected areas example, we find that the indicator can aid in decision-making because it is able to differentiate between the impacts of the different policies. In the bottom trawling example, the indicator exhibits some counter-intuitive behaviour, due to over-representation of some taxonomic and functional groups in the indicator, and contrasting impacts of the policies on different groups caused by trophic interactions. Our results support the need for further research on how to use predictive models and indicators to credibly track trends and inform policy. To be useful and relevant, scientists must make testable predictions about the impact of global policy on biodiversity to ensure that targets such as those set at Nagoya catalyse effective and measurable change.

Making Robust Policy Decisions Using Global Biodiversity Indicators

PubMed Central

Nicholson, Emily; Collen, Ben; Barausse, Alberto; Blanchard, Julia L.; Costelloe, Brendan T.; Sullivan, Kathryn M. E.; Underwood, Fiona M.; Burn, Robert W.; Fritz, Steffen; Jones, Julia P. G.; McRae, Louise; Possingham, Hugh P.; Milner-Gulland, E. J.

2012-01-01

In order to influence global policy effectively, conservation scientists need to be able to provide robust predictions of the impact of alternative policies on biodiversity and measure progress towards goals using reliable indicators. We present a framework for using biodiversity indicators predictively to inform policy choices at a global level. The approach is illustrated with two case studies in which we project forwards the impacts of feasible policies on trends in biodiversity and in relevant indicators. The policies are based on targets agreed at the Convention on Biological Diversity (CBD) meeting in Nagoya in October 2010. The first case study compares protected area policies for African mammals, assessed using the Red List Index; the second example uses the Living Planet Index to assess the impact of a complete halt, versus a reduction, in bottom trawling. In the protected areas example, we find that the indicator can aid in decision-making because it is able to differentiate between the impacts of the different policies. In the bottom trawling example, the indicator exhibits some counter-intuitive behaviour, due to over-representation of some taxonomic and functional groups in the indicator, and contrasting impacts of the policies on different groups caused by trophic interactions. Our results support the need for further research on how to use predictive models and indicators to credibly track trends and inform policy. To be useful and relevant, scientists must make testable predictions about the impact of global policy on biodiversity to ensure that targets such as those set at Nagoya catalyse effective and measurable change. PMID:22815938

Democracy under uncertainty: the wisdom of crowds and the free-rider problem in group decision making.

PubMed

Kameda, Tatsuya; Tsukasaki, Takafumi; Hastie, Reid; Berg, Nathan

2011-01-01

We introduce a game theory model of individual decisions to cooperate by contributing personal resources to group decisions versus by free riding on the contributions of other members. In contrast to most public-goods games that assume group returns are linear in individual contributions, the present model assumes decreasing marginal group production as a function of aggregate individual contributions. This diminishing marginal returns assumption is more realistic and generates starkly different predictions compared to the linear model. One important implication is that, under most conditions, there exist equilibria where some, but not all, members of a group contribute, even with completely self-interested motives. An agent-based simulation confirmed the individual and group advantages of the equilibria in which behavioral asymmetry emerges from a game structure that is a priori perfectly symmetric for all agents (all agents have the same payoff function and action space but take different actions in equilibria). A behavioral experiment demonstrated that cooperators and free riders coexist in a stable manner in groups performing with the nonlinear production function. A collateral result demonstrated that, compared to a dictatorial decision scheme guided by the best member in a group, the majority/plurality decision rules can pool information effectively and produce greater individual net welfare at equilibrium, even if free riding is not sanctioned. This is an original proof that cooperation in ad hoc decision-making groups can be understood in terms of self-interested motivations and that, despite the free-rider problem, majority/plurality decision rules can function robustly as simple, efficient social decision heuristics.

Improving Empirical Approaches to Estimating Local Greenhouse Gas Emissions

NASA Astrophysics Data System (ADS)

Blackhurst, M.; Azevedo, I. L.; Lattanzi, A.

2016-12-01

Evidence increasingly indicates our changing climate will have significant global impacts on public health, economies, and ecosystems. As a result, local governments have become increasingly interested in climate change mitigation. In the U.S., cities and counties representing nearly 15% of the domestic population plan to reduce 300 million metric tons of greenhouse gases over the next 40 years (or approximately 1 ton per capita). Local governments estimate greenhouse gas emissions to establish greenhouse gas mitigation goals and select supporting mitigation measures. However, current practices produce greenhouse gas estimates - also known as a "greenhouse gas inventory " - of empirical quality often insufficient for robust mitigation decision making. Namely, current mitigation planning uses sporadic, annual, and deterministic estimates disaggregated by broad end use sector, obscuring sources of emissions uncertainty, variability, and exogeneity that influence mitigation opportunities. As part of AGU's Thriving Earth Exchange, Ari Lattanzi of City of Pittsburgh, PA recently partnered with Dr. Inez Lima Azevedo (Carnegie Mellon University) and Dr. Michael Blackhurst (University of Pittsburgh) to improve the empirical approach to characterizing Pittsburgh's greenhouse gas emissions. The project will produce first-order estimates of the underlying sources of uncertainty, variability, and exogeneity influencing Pittsburgh's greenhouse gases and discuss implications of mitigation decision making. The results of the project will enable local governments to collect more robust greenhouse gas inventories to better support their mitigation goals and improve measurement and verification efforts.

Improving Multi-Objective Management of Water Quality Tipping Points: Revisiting the Classical Shallow Lake Problem

NASA Astrophysics Data System (ADS)

Quinn, J. D.; Reed, P. M.; Keller, K.

2015-12-01

Recent multi-objective extensions of the classical shallow lake problem are useful for exploring the conceptual and computational challenges that emerge when managing irreversible water quality tipping points. Building on this work, we explore a four objective version of the lake problem where a hypothetical town derives economic benefits from polluting a nearby lake, but at the risk of irreversibly tipping the lake into a permanently polluted state. The trophic state of the lake exhibits non-linear threshold dynamics; below some critical phosphorus (P) threshold it is healthy and oligotrophic, but above this threshold it is irreversibly eutrophic. The town must decide how much P to discharge each year, a decision complicated by uncertainty in the natural P inflow to the lake. The shallow lake problem provides a conceptually rich set of dynamics, low computational demands, and a high level of mathematical difficulty. These properties maximize its value for benchmarking the relative merits and limitations of emerging decision support frameworks, such as Direct Policy Search (DPS). Here, we explore the use of DPS as a formal means of developing robust environmental pollution control rules that effectively account for deeply uncertain system states and conflicting objectives. The DPS reformulation of the shallow lake problem shows promise in formalizing pollution control triggers and signposts, while dramatically reducing the computational complexity of the multi-objective pollution control problem. More broadly, the insights from the DPS variant of the shallow lake problem formulated in this study bridge emerging work related to socio-ecological systems management, tipping points, robust decision making, and robust control.

How can sensitivity analysis improve the robustness of mathematical models utilized by the re/insurance industry?

NASA Astrophysics Data System (ADS)

Noacco, V.; Wagener, T.; Pianosi, F.; Philp, T.

2017-12-01

Insurance companies provide insurance against a wide range of threats, such as natural catastrophes, nuclear incidents and terrorism. To quantify risk and support investment decisions, mathematical models are used, for example to set the premiums charged to clients that protect from financial loss, should deleterious events occur. While these models are essential tools for adequately assessing the risk attached to an insurer's portfolio, their development is costly and their value for decision-making may be limited by an incomplete understanding of uncertainty and sensitivity. Aside from the business need to understand risk and uncertainty, the insurance sector also faces regulation which requires them to test their models in such a way that uncertainties are appropriately captured and that plans are in place to assess the risks and their mitigation. The building and testing of models constitutes a high cost for insurance companies, and it is a time intensive activity. This study uses an established global sensitivity analysis toolbox (SAFE) to more efficiently capture the uncertainties and sensitivities embedded in models used by a leading re/insurance firm, with structured approaches to validate these models and test the impact of assumptions on the model predictions. It is hoped that this in turn will lead to better-informed and more robust business decisions.

Sociohydrology of an Arid City: Development of a Coupled Model of Water Management in Las Vegas

NASA Astrophysics Data System (ADS)

Garcia, M. E.; Islam, S.; Portney, K. E.

2014-12-01

Rapidly growing cities in arid regions present a significant water management challenge. Key to tackling this challenge is understanding how and why some cities transition to more sustainable water management; acknowledging that urban water resources decisions are both responding to and precipitating hydrologic change, this question is best tackled through a sociohydrology approach. While coupling of natural and societal systems is in it's infancy in the field of hydrology, there is a strong tradition of studying coupled systems in the field of Socio-Ecological Systems. We build on Ostrom's Socio-Ecological Systems framework to develop a system dynamics model of water management for the Las Vegas metropolitan area using Vensim. A key objective our proposed modeling framework is to illuminate the dynamic interactions of the sociohydrologic system components and enable testing of various assumptions and strategies. The model of Las Vegas water management consists of five sub-modules: water supply, water demand, finances, public perception and policy making process. The development of the first three modules were based on clearly defined system structure. The public perception sub-module tracks the level public risk perception of a water supply shortage and represents the hypothesis that public risk perception is updated periodically when shortage events are experienced. The policy making process module uses an algorithm capturing the hypothesized decision making process to select policy actions (or in-action) from a set of feasible actions in response to the system states tracked by the model and observable to decision makers. The model was tested and parameterized using mix of quantitative data on water demands, supplies and costs and qualitative data from document analysis and interview data covering 1990 to 2010 period. Given that not only the parameters but also the structure of the public perception and the policy making process sub-systems is contested, a different approach must be taken to assess the robustness of these modules. Presented here is the development of the model, results of model testing against the historic reference modes using Las Vegas as an example, and future work planned to improve the robustness of the model.

Time evolving multi-city dependencies and robustness tradeoffs for risk-based portfolios of conservation, transfers, and cooperative water supply infrastructure development pathways

NASA Astrophysics Data System (ADS)

Trindade, B. C.; Reed, P. M.; Zeff, H. B.; Characklis, G. W.

2016-12-01

Water scarcity in historically water-rich regions such as the southeastern United States is becoming a more prevalent concern. It has been shown that cooperative short-term planning that relies on conservation and transfers of existing supplies amongst communities can be used by water utilities to mitigate the effects of water scarcity in the near future. However, in the longer term, infrastructure expansion is likely to be necessary to address imbalances between growing water demands and the available supply capacity. This study seeks to better diagnose and avoid candidate modes for system failure. Although it is becoming more common for water utilities to evaluate the robustness of their water supply, defined as the insensitivity of their systems to errors in deeply uncertain projections or assumptions, defining robustness is particularly challenging in multi-stakeholder regional contexts for decisions that encompass short management actions and long-term infrastructure planning. Planning and management decisions are highly interdependent and strongly shape how a region's infrastructure itself evolves. This research advances the concept of system robustness by making it evolve over time rather than static, so that it is applicable to an adaptive system and therefore more suited for use for combined short and long-term planning efforts. The test case for this research is the Research Triangle area of North Carolina, where the cities of Raleigh, Durham, Cary and Chapel Hill are experiencing rapid population growth and increasing concerns over drought. This study is facilitating their engagement in cooperative and robust regional water portfolio planning. The insights from this work have general merit for regions where adjacent municipalities can benefit from improving cooperative infrastructure investments and more efficient resource management strategies.

Towards Risk Based Design for NASA's Missions

NASA Technical Reports Server (NTRS)

Tumer, Irem Y.; Barrientos, Francesca; Meshkat, Leila

2004-01-01

This paper describes the concept of Risk Based Design in the context of NASA s low volume, high cost missions. The concept of accounting for risk in the design lifecycle has been discussed and proposed under several research topics, including reliability, risk analysis, optimization, uncertainty, decision-based design, and robust design. This work aims to identify and develop methods to enable and automate a means to characterize and optimize risk, and use risk as a tradeable resource to make robust and reliable decisions, in the context of the uncertain and ambiguous stage of early conceptual design. This paper first presents a survey of the related topics explored in the design research community as they relate to risk based design. Then, a summary of the topics from the NASA-led Risk Colloquium is presented, followed by current efforts within NASA to account for risk in early design. Finally, a list of "risk elements", identified for early-phase conceptual design at NASA, is presented. The purpose is to lay the foundation and develop a roadmap for future work and collaborations for research to eliminate and mitigate these risk elements in early phase design.

KNOW ESSENTIALS: a tool for informed decisions in the absence of formal HTA systems.

PubMed

Mathew, Joseph L

2011-04-01

Most developing countries and resource-limited settings lack robust health technology assessment (HTA) systems. Because the development of locally relevant HTA is not immediately viable, and the extrapolation of external HTA is inappropriate, a new model for evaluating health technologies is required. The aim of this study was to describe the development and application of KNOW ESSENTIALS, a tool facilitating evidence-based decisions on health technologies by stakeholders in settings lacking formal HTA systems. Current HTA methodology was examined through literature search. Additional issues relevant to resource-limited settings, but not adequately addressed in current methodology, were identified through further literature search, appraisal of contextually relevant issues, discussion with healthcare professionals familiar with the local context, and personal experience. A set of thirteen elements important for evidence-based decisions was identified, selected and combined into a tool with the mnemonic KNOW ESSENTIALS. Detailed definitions for each element, coding for the elements, and a system to evaluate a given health technology using the tool were developed. Developing countries and resource-limited settings face several challenges to informed decision making. Models that are relevant and applicable in high-income countries are unlikely in such settings. KNOW ESSENTIALS is an alternative that facilitates evidence-based decision making by stakeholders without formal expertise in HTA. The tool could be particularly useful, as an interim measure, in healthcare systems that are developing HTA capacity. It could also be useful anywhere when rapid evidence-based decisions on health technologies are required.

Development of a robust space power system decision model

NASA Astrophysics Data System (ADS)

Chew, Gilbert; Pelaccio, Dennis G.; Jacobs, Mark; Stancati, Michael; Cataldo, Robert

2001-02-01

NASA continues to evaluate power systems to support human exploration of the Moon and Mars. The system(s) would address all power needs of surface bases and on-board power for space transfer vehicles. Prior studies have examined both solar and nuclear-based alternatives with respect to individual issues such as sizing or cost. What has not been addressed is a comprehensive look at the risks and benefits of the options that could serve as the analytical framework to support a system choice that best serves the needs of the exploration program. This paper describes the SAIC developed Space Power System Decision Model, which uses a formal Two-step Analytical Hierarchy Process (TAHP) methodology that is used in the decision-making process to clearly distinguish candidate power systems in terms of benefits, safety, and risk. TAHP is a decision making process based on the Analytical Hierarchy Process, which employs a hierarchic approach of structuring decision factors by weights, and relatively ranks system design options on a consistent basis. This decision process also includes a level of data gathering and organization that produces a consistent, well-documented assessment, from which the capability of each power system option to meet top-level goals can be prioritized. The model defined on this effort focuses on the comparative assessment candidate power system options for Mars surface application(s). This paper describes the principles of this approach, the assessment criteria and weighting procedures, and the tools to capture and assess the expert knowledge associated with space power system evaluation. .

Watermark: An Application and Methodology and Application for Interactive and intelligent Decision Support for Groundwater Systems

NASA Astrophysics Data System (ADS)

Pierce, S. A.; Wagner, K.; Schwartz, S.; Gentle, J. N., Jr.

2016-12-01

Critical water resources face the effects of historic drought, increased demand, and potential contamination, the need has never been greater to develop resources to effectively communicate conservation and protection across a broad audience and geographical area. The Watermark application and macro-analysis methodology merges topical analysis of context rich corpus from policy texts with multi-attributed solution sets from integrated models of water resource and other subsystems, such as mineral, food, energy, or environmental systems to construct a scalable, robust, and reproducible approach for identifying links between policy and science knowledge bases. The Watermark application is an open-source, interactive workspace to support science-based visualization and decision making. Designed with generalization in mind, Watermark is a flexible platform that allows for data analysis and inclusion of large datasets with an interactive front-end capable of connecting with other applications as well as advanced computing resources. In addition, the Watermark analysis methodology offers functionality that streamlines communication with non-technical users for policy, education, or engagement with groups around scientific topics of societal relevance. The technology stack for Watermark was selected with the goal of creating a robust and dynamic modular codebase that can be adjusted to fit many use cases and scale to support usage loads that range between simple data display to complex scientific simulation-based modelling and analytics. The methodology uses to topical analysis and simulation-optimization to systematically analyze the policy and management realities of resource systems and explicitly connect the social and problem contexts with science-based and engineering knowledge from models. A case example demonstrates use in a complex groundwater resources management study highlighting multi-criteria spatial decision making and uncertainty comparisons.

Development of a chromatographic method with multi-criteria decision making design for simultaneous determination of nifedipine and atenolol in content uniformity testing.

PubMed

Ahmed, Sameh; Alqurshi, Abdulmalik; Mohamed, Abdel-Maaboud Ismail

2018-07-01

A new robust and reliable high-performance liquid chromatography (HPLC) method with multi-criteria decision making (MCDM) approach was developed to allow simultaneous quantification of atenolol (ATN) and nifedipine (NFD) in content uniformity testing. Felodipine (FLD) was used as an internal standard (I.S.) in this study. A novel marriage between a new interactive response optimizer and a HPLC method was suggested for multiple response optimizations of target responses. An interactive response optimizer was used as a decision and prediction tool for the optimal settings of target responses, according to specified criteria, based on Derringer's desirability. Four independent variables were considered in this study: Acetonitrile%, buffer pH and concentration along with column temperature. Eight responses were optimized: retention times of ATN, NFD, and FLD, resolutions between ATN/NFD and NFD/FLD, and plate numbers for ATN, NFD, and FLD. Multiple regression analysis was applied in order to scan the influences of the most significant variables for the regression models. The experimental design was set to give minimum retention times, maximum resolution and plate numbers. The interactive response optimizer allowed prediction of optimum conditions according to these criteria with a good composite desirability value of 0.98156. The developed method was validated according to the International Conference on Harmonization (ICH) guidelines with the aid of the experimental design. The developed MCDM-HPLC method showed superior robustness and resolution in short analysis time allowing successful simultaneous content uniformity testing of ATN and NFD in marketed capsules. The current work presents an interactive response optimizer as an efficient platform to optimize, predict responses, and validate HPLC methodology with tolerable design space for assay in quality control laboratories. Copyright © 2018 Elsevier B.V. All rights reserved.

Multi-objective Optimization for the Robust Performance of Drinking Water Treatment Plants under Climate Change and Climate Extremes

NASA Astrophysics Data System (ADS)

Raseman, W. J.; Kasprzyk, J. R.; Rosario-Ortiz, F.; Summers, R. S.; Stewart, J.; Livneh, B.

2016-12-01

To promote public health, the United States Environmental Protection Agency (US EPA), and similar entities around the world enact strict laws to regulate drinking water quality. These laws, such as the Stage 1 and 2 Disinfectants and Disinfection Byproducts (D/DBP) Rules, come at a cost to water treatment plants (WTPs) which must alter their operations and designs to meet more stringent standards and the regulation of new contaminants of concern. Moreover, external factors such as changing influent water quality due to climate extremes and climate change, may force WTPs to adapt their treatment methods. To grapple with these issues, decision support systems (DSSs) have been developed to aid WTP operation and planning. However, there is a critical need to better address long-term decision making for WTPs. In this poster, we propose a DSS framework for WTPs for long-term planning, which improves upon the current treatment of deep uncertainties within the overall potable water system including the impact of climate on influent water quality and uncertainties in treatment process efficiencies. We present preliminary results exploring how a multi-objective evolutionary algorithm (MOEA) search can be coupled with models of WTP processes to identify high-performing plans for their design and operation. This coupled simulation-optimization technique uses Borg MOEA, an auto-adaptive algorithm, and the Water Treatment Plant Model, a simulation model developed by the US EPA to assist in creating the D/DBP Rules. Additionally, Monte Carlo sampling methods were used to study the impact of uncertainty of influent water quality on WTP decision-making and generate plans for robust WTP performance.

Fuzzy robust credibility-constrained programming for environmental management and planning.

PubMed

Zhang, Yimei; Hang, Guohe

2010-06-01

In this study, a fuzzy robust credibility-constrained programming (FRCCP) is developed and applied to the planning for waste management systems. It incorporates the concepts of credibility-based chance-constrained programming and robust programming within an optimization framework. The developed method can reflect uncertainties presented as possibility-density by fuzzy-membership functions. Fuzzy credibility constraints are transformed to the crisp equivalents with different credibility levels, and ordinary fuzzy inclusion constraints are determined by their robust deterministic constraints by setting a-cut levels. The FRCCP method can provide different system costs under different credibility levels (lambda). From the results of sensitivity analyses, the operation cost of the landfill is a critical parameter. For the management, any factors that would induce cost fluctuation during landfilling operation would deserve serious observation and analysis. By FRCCP, useful solutions can be obtained to provide decision-making support for long-term planning of solid waste management systems. It could be further enhanced through incorporating methods of inexact analysis into its framework. It can also be applied to other environmental management problems.

Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems.

PubMed

Whitacre, James M; Bender, Axel

2010-06-15

A generic mechanism--networked buffering--is proposed for the generation of robust traits in complex systems. It requires two basic conditions to be satisfied: 1) agents are versatile enough to perform more than one single functional role within a system and 2) agents are degenerate, i.e. there exists partial overlap in the functional capabilities of agents. Given these prerequisites, degenerate systems can readily produce a distributed systemic response to local perturbations. Reciprocally, excess resources related to a single function can indirectly support multiple unrelated functions within a degenerate system. In models of genome:proteome mappings for which localized decision-making and modularity of genetic functions are assumed, we verify that such distributed compensatory effects cause enhanced robustness of system traits. The conditions needed for networked buffering to occur are neither demanding nor rare, supporting the conjecture that degeneracy may fundamentally underpin distributed robustness within several biotic and abiotic systems. For instance, networked buffering offers new insights into systems engineering and planning activities that occur under high uncertainty. It may also help explain recent developments in understanding the origins of resilience within complex ecosystems.

Robustness of non-interdependent and interdependent networks against dependent and adaptive attacks

NASA Astrophysics Data System (ADS)

Tyra, Adam; Li, Jingtao; Shang, Yilun; Jiang, Shuo; Zhao, Yanjun; Xu, Shouhuai

2017-09-01

Robustness of complex networks has been extensively studied via the notion of site percolation, which typically models independent and non-adaptive attacks (or disruptions). However, real-life attacks are often dependent and/or adaptive. This motivates us to characterize the robustness of complex networks, including non-interdependent and interdependent ones, against dependent and adaptive attacks. For this purpose, dependent attacks are accommodated by L-hop percolation where the nodes within some L-hop (L ≥ 0) distance of a chosen node are all deleted during one attack (with L = 0 degenerating to site percolation). Whereas, adaptive attacks are launched by attackers who can make node-selection decisions based on the network state in the beginning of each attack. The resulting characterization enriches the body of knowledge with new insights, such as: (i) the Achilles' Heel phenomenon is only valid for independent attacks, but not for dependent attacks; (ii) powerful attack strategies (e.g., targeted attacks and dependent attacks, dependent attacks and adaptive attacks) are not compatible and cannot help the attacker when used collectively. Our results shed some light on the design of robust complex networks.

Planning water supply under uncertainty - benefits and limitations of RDM, Info-Gap, economic optimization and many-objective optimization

NASA Astrophysics Data System (ADS)

Matrosov, E.; Padula, S.; Huskova, I.; Harou, J. J.

2012-12-01

Population growth and the threat of drier or changed climates are likely to increase water scarcity world-wide. A combination of demand management (water conservation) and new supply infrastructure is often needed to meet future projected demands. In this case system planners must decide what to implement, when and at what capacity. Choices can range from infrastructure to policies or a mix of the two, culminating in a complex planning problem. Decision making under uncertainty frameworks can be used to help planners with this planning problem. This presentation introduces, applies and compares four decision making under uncertainty frameworks. The application is to the Thames basin water resource system which includes the city of London. The approaches covered here include least-economic cost capacity expansion optimization (EO), Robust Decision Making (RDM), Info-Gap Decision Theory (Info-gap) and many-objective evolutionary optimization (MOEO). EO searches for the least-economic cost program, i.e. the timing, sizing, and choice of supply-demand management actions/upgrades which meet projected water demands. Instead of striving for optimality, the RDM and Info-gap approaches help build plans that are robust to 'deep' uncertainty in future conditions. The MOEO framework considers multiple performance criteria and uses water systems simulators as a function evaluator for the evolutionary algorithm. Visualizations show Pareto approximate tradeoffs between multiple objectives. In this presentation we detail the application of each framework to the Thames basin (including London) water resource planning problem. Supply and demand options are proposed by the major water companies in the basin. We apply the EO method using a 29 year time horizon and an annual time step considering capital, operating (fixed and variable), social and environmental costs. The method considers all plausible combinations of supply and conservation schemes and capacities proposed by water companies and generates the least-economic cost annual plan. The RDM application uses stochastic simulation under a weekly time-step and regret analysis to choose a candidate strategy. We then use a statistical cluster algorithm to identify future states of the world under which the strategy is vulnerable. The method explicitly considers the effects of uncertainty in supply, demands and energy price on multiple performance criteria. The Info-gap approach produces robustness and opportuneness plots that show the performance of different plans under the most dire and favorable sets of future conditions. The same simulator, supply and demand options and uncertainties are considered as in the RDM application. The MOEO application considers many more combinations of supply and demand options while still employing a simulator that enables a more realistic representation of the physical system and operating rules. A computer cluster is employed to ease the computational burden. Visualization software allows decision makers to interactively view tradeoffs in many dimensions. Benefits and limitations of each framework are discussed and recommendations for future planning in the basin are provided.

Robust Team-Optimal and Leader-Follower Policies for Decision Making in C3 (Command, Control and Communications) Systems.

DTIC Science & Technology

1984-07-01

edition , Moscow: Nooka Publishing Co.. 1977; toelith translation of the Ilt edition : t ine with a specific cost structure, we have obtained Functional...651-666, September 1981. [131 L. W. Kantorovich and G. P. Akilov, Functional Analysis, 2nd edition , Moscow: Nauka Publishing Co., 1977; English...translation of the ist edition : Functional Analysis in Normed Spaces, New York: MacMillan, 1964. [14] J. Marschak and R. Radner, Economic Theory of Teams

The Construction of a Vague Fuzzy Measure Through L1 Parameter Optimization

DTIC Science & Technology

2012-08-26

Programming v. 1.21, http://cvxr.com/cvx, (2011) 11 [3] E.J. Candes, J. Romberg and T. Tao. Robust Uncertainty Principles: Exact Signal Reconstruction From...Annales de I’institut Fourer, 5 (1954), pp. 131-295 [9] D. Diakoulaki, C. Antunes and A. Martins. MCDA in Energy Planning, Int. Series in Operations...formance and Tests , Fuzzy Sets and Systems, Vol. 65, Issues 2-3 (1994), pp.255-271 [15] M. Grabisch. Fuzzy Integral in Multicriteria Decision Making, Fuzzy

A robust algorithm for optimisation and customisation of fractal dimensions of time series modified by nonlinearly scaling their time derivatives: mathematical theory and practical applications.

PubMed

Fuss, Franz Konstantin

2013-01-01

Standard methods for computing the fractal dimensions of time series are usually tested with continuous nowhere differentiable functions, but not benchmarked with actual signals. Therefore they can produce opposite results in extreme signals. These methods also use different scaling methods, that is, different amplitude multipliers, which makes it difficult to compare fractal dimensions obtained from different methods. The purpose of this research was to develop an optimisation method that computes the fractal dimension of a normalised (dimensionless) and modified time series signal with a robust algorithm and a running average method, and that maximises the difference between two fractal dimensions, for example, a minimum and a maximum one. The signal is modified by transforming its amplitude by a multiplier, which has a non-linear effect on the signal's time derivative. The optimisation method identifies the optimal multiplier of the normalised amplitude for targeted decision making based on fractal dimensions. The optimisation method provides an additional filter effect and makes the fractal dimensions less noisy. The method is exemplified by, and explained with, different signals, such as human movement, EEG, and acoustic signals.

A Robust Algorithm for Optimisation and Customisation of Fractal Dimensions of Time Series Modified by Nonlinearly Scaling Their Time Derivatives: Mathematical Theory and Practical Applications

PubMed Central

2013-01-01

Standard methods for computing the fractal dimensions of time series are usually tested with continuous nowhere differentiable functions, but not benchmarked with actual signals. Therefore they can produce opposite results in extreme signals. These methods also use different scaling methods, that is, different amplitude multipliers, which makes it difficult to compare fractal dimensions obtained from different methods. The purpose of this research was to develop an optimisation method that computes the fractal dimension of a normalised (dimensionless) and modified time series signal with a robust algorithm and a running average method, and that maximises the difference between two fractal dimensions, for example, a minimum and a maximum one. The signal is modified by transforming its amplitude by a multiplier, which has a non-linear effect on the signal's time derivative. The optimisation method identifies the optimal multiplier of the normalised amplitude for targeted decision making based on fractal dimensions. The optimisation method provides an additional filter effect and makes the fractal dimensions less noisy. The method is exemplified by, and explained with, different signals, such as human movement, EEG, and acoustic signals. PMID:24151522

A climate robust integrated modelling framework for regional impact assessment of climate change

NASA Astrophysics Data System (ADS)

Janssen, Gijs; Bakker, Alexander; van Ek, Remco; Groot, Annemarie; Kroes, Joop; Kuiper, Marijn; Schipper, Peter; van Walsum, Paul; Wamelink, Wieger; Mol, Janet

2013-04-01

Decision making towards climate proofing the water management of regional catchments can benefit greatly from the availability of a climate robust integrated modelling framework, capable of a consistent assessment of climate change impacts on the various interests present in the catchments. In the Netherlands, much effort has been devoted to developing state-of-the-art regional dynamic groundwater models with a very high spatial resolution (25x25 m2). Still, these models are not completely satisfactory to decision makers because the modelling concepts do not take into account feedbacks between meteorology, vegetation/crop growth, and hydrology. This introduces uncertainties in forecasting the effects of climate change on groundwater, surface water, agricultural yields, and development of groundwater dependent terrestrial ecosystems. These uncertainties add to the uncertainties about the predictions on climate change itself. In order to create an integrated, climate robust modelling framework, we coupled existing model codes on hydrology, agriculture and nature that are currently in use at the different research institutes in the Netherlands. The modelling framework consists of the model codes MODFLOW (groundwater flow), MetaSWAP (vadose zone), WOFOST (crop growth), SMART2-SUMO2 (soil-vegetation) and NTM3 (nature valuation). MODFLOW, MetaSWAP and WOFOST are coupled online (i.e. exchange information on time step basis). Thus, changes in meteorology and CO2-concentrations affect crop growth and feedbacks between crop growth, vadose zone water movement and groundwater recharge are accounted for. The model chain WOFOST-MetaSWAP-MODFLOW generates hydrological input for the ecological prediction model combination SMART2-SUMO2-NTM3. The modelling framework was used to support the regional water management decision making process in the 267 km2 Baakse Beek-Veengoot catchment in the east of the Netherlands. Computations were performed for regionalized 30-year climate change scenarios developed by KNMI for precipitation and reference evapotranspiration according to Penman-Monteith. Special focus in the project was on the role of uncertainty. How valid is the information that is generated by this modelling framework? What are the most important uncertainties of the input data, how do they affect the results of the model chain and how can the uncertainties of the data, results, and model concepts be quantified and communicated? Besides these technical issues, an important part of the study was devoted to the perception of stakeholders. Stakeholder analysis and additional working sessions yielded insight into how the models, their results and the uncertainties are perceived, how the modelling framework and results connect to the stakeholders' information demands and what kind of additional information is needed for adequate support on decision making.

Impaired Expected Value Computations Coupled With Overreliance on Stimulus-Response Learning in Schizophrenia.

PubMed

Hernaus, Dennis; Gold, James M; Waltz, James A; Frank, Michael J

2018-04-03

While many have emphasized impaired reward prediction error signaling in schizophrenia, multiple studies suggest that some decision-making deficits may arise from overreliance on stimulus-response systems together with a compromised ability to represent expected value. Guided by computational frameworks, we formulated and tested two scenarios in which maladaptive representations of expected value should be most evident, thereby delineating conditions that may evoke decision-making impairments in schizophrenia. In a modified reinforcement learning paradigm, 42 medicated people with schizophrenia and 36 healthy volunteers learned to select the most frequently rewarded option in a 75-25 pair: once when presented with a more deterministic (90-10) pair and once when presented with a more probabilistic (60-40) pair. Novel and old combinations of choice options were presented in a subsequent transfer phase. Computational modeling was employed to elucidate contributions from stimulus-response systems (actor-critic) and expected value (Q-learning). People with schizophrenia showed robust performance impairments with increasing value difference between two competing options, which strongly correlated with decreased contributions from expected value-based learning (Q-learning). Moreover, a subtle yet consistent contextual choice bias for the probabilistic 75 option was present in people with schizophrenia, which could be accounted for by a context-dependent reward prediction error in the actor-critic. We provide evidence that decision-making impairments in schizophrenia increase monotonically with demands placed on expected value computations. A contextual choice bias is consistent with overreliance on stimulus-response learning, which may signify a deficit secondary to the maladaptive representation of expected value. These results shed new light on conditions under which decision-making impairments may arise. Copyright © 2018 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Uncertainty quantification and optimal decisions

PubMed Central

2017-01-01

A mathematical model can be analysed to construct policies for action that are close to optimal for the model. If the model is accurate, such policies will be close to optimal when implemented in the real world. In this paper, the different aspects of an ideal workflow are reviewed: modelling, forecasting, evaluating forecasts, data assimilation and constructing control policies for decision-making. The example of the oil industry is used to motivate the discussion, and other examples, such as weather forecasting and precision agriculture, are used to argue that the same mathematical ideas apply in different contexts. Particular emphasis is placed on (i) uncertainty quantification in forecasting and (ii) how decisions are optimized and made robust to uncertainty in models and judgements. This necessitates full use of the relevant data and by balancing costs and benefits into the long term may suggest policies quite different from those relevant to the short term. PMID:28484343

Exploring critical pathways for urban water management to identify robust strategies under deep uncertainties.

PubMed

Urich, Christian; Rauch, Wolfgang

2014-12-01

Long-term projections for key drivers needed in urban water infrastructure planning such as climate change, population growth, and socio-economic changes are deeply uncertain. Traditional planning approaches heavily rely on these projections, which, if a projection stays unfulfilled, can lead to problematic infrastructure decisions causing high operational costs and/or lock-in effects. New approaches based on exploratory modelling take a fundamentally different view. Aim of these is, to identify an adaptation strategy that performs well under many future scenarios, instead of optimising a strategy for a handful. However, a modelling tool to support strategic planning to test the implication of adaptation strategies under deeply uncertain conditions for urban water management does not exist yet. This paper presents a first step towards a new generation of such strategic planning tools, by combing innovative modelling tools, which coevolve the urban environment and urban water infrastructure under many different future scenarios, with robust decision making. The developed approach is applied to the city of Innsbruck, Austria, which is spatially explicitly evolved 20 years into the future under 1000 scenarios to test the robustness of different adaptation strategies. Key findings of this paper show that: (1) Such an approach can be used to successfully identify parameter ranges of key drivers in which a desired performance criterion is not fulfilled, which is an important indicator for the robustness of an adaptation strategy; and (2) Analysis of the rich dataset gives new insights into the adaptive responses of agents to key drivers in the urban system by modifying a strategy. Copyright © 2014 Elsevier Ltd. All rights reserved.

Innovation and nested preferential growth in chess playing behavior

NASA Astrophysics Data System (ADS)

Perotti, J. I.; Jo, H.-H.; Schaigorodsky, A. L.; Billoni, O. V.

2013-11-01

Complexity develops via the incorporation of innovative properties. Chess is one of the most complex strategy games, where expert contenders exercise decision making by imitating old games or introducing innovations. In this work, we study innovation in chess by analyzing how different move sequences are played at the population level. It is found that the probability of exploring a new or innovative move decreases as a power law with the frequency of the preceding move sequence. Chess players also exploit already known move sequences according to their frequencies, following a preferential growth mechanism. Furthermore, innovation in chess exhibits Heaps' law suggesting similarities with the process of vocabulary growth. We propose a robust generative mechanism based on nested Yule-Simon preferential growth processes that reproduces the empirical observations. These results, supporting the self-similar nature of innovations in chess are important in the context of decision making in a competitive scenario, and extend the scope of relevant findings recently discovered regarding the emergence of Zipf's law in chess.

mTOR Inhibition: From Aging to Autism and Beyond.

PubMed

Kaeberlein, Matt

2013-01-01

The mechanistic target of rapamycin (mTOR) is a highly conserved protein that regulates growth and proliferation in response to environmental and hormonal cues. Broadly speaking, organisms are constantly faced with the challenge of interpreting their environment and making a decision between "grow or do not grow." mTOR is a major component of the network that makes this decision at the cellular level and, to some extent, the tissue and organismal level as well. Although overly simplistic, this framework can be useful when considering the myriad functions ascribed to mTOR and the pleiotropic phenotypes associated with genetic or pharmacological modulation of mTOR signaling. In this review, I will consider mTOR function in this context and attempt to summarize and interpret the growing body of literature demonstrating interesting and varied effects of mTOR inhibitors. These include robust effects on a multitude of age-related parameters and pathologies, as well as several other processes not obviously linked to aging or age-related disease.

Multi-criteria decision making development of ion chromatographic method for determination of inorganic anions in oilfield waters based on artificial neural networks retention model.

PubMed

Stefanović, Stefica Cerjan; Bolanča, Tomislav; Luša, Melita; Ukić, Sime; Rogošić, Marko

2012-02-24

This paper describes the development of ad hoc methodology for determination of inorganic anions in oilfield water, since their composition often significantly differs from the average (concentration of components and/or matrix). Therefore, fast and reliable method development has to be performed in order to ensure the monitoring of desired properties under new conditions. The method development was based on computer assisted multi-criteria decision making strategy. The used criteria were: maximal value of objective functions used, maximal robustness of the separation method, minimal analysis time, and maximal retention distance between two nearest components. Artificial neural networks were used for modeling of anion retention. The reliability of developed method was extensively tested by the validation of performance characteristics. Based on validation results, the developed method shows satisfactory performance characteristics, proving the successful application of computer assisted methodology in the described case study. Copyright © 2011 Elsevier B.V. All rights reserved.

Is Israel ready for disease management?

PubMed

Linden, Ariel

2006-10-01

Approximately 60% of all worldwide deaths are caused by chronic disease resulting from modifiable health behaviors. In the United States, structured programs tailored to identify and modify health behaviors of patients with chronic illness have grown into a robust industry called disease management. DM is premised upon the basic assumption that health services utilization and morbidity can be reduced for those with chronic illness by augmenting traditional episodic medical care services and support between physician visits. Given that Israel and the U.S. have similar demographics in their chronically ill populations, it would make intuitive sense for Israel to replicate efforts made in the U.S. to incorporate DM strategies. This paper provides a conceptual framework of how DM could be integrated within the current organizational structure of the Israeli healthcare system, which is uniquely conducive to the implementation of DM on a population-wide basis. While ultimately the decision to invest in DM lies with stakeholders at various institutional levels in Israel, this paper is intended to provide direction and support for that decision-making process.

X-33/RLV System Health Management/ Vehicle Health Management

NASA Technical Reports Server (NTRS)

Garbos, Raymond J.; Mouyos, William

1998-01-01

To reduce operations cost, the RLV must include the following elements: highly reliable, robust subsystems designed for simple repair access with a simplified servicing infrastructure and incorporating expedited decision making about faults and anomalies. A key component for the Single Stage to Orbit (SSTO) RLV System used to meet these objectives is System Health Management (SHM). SHM deals with the vehicle component- Vehicle Health Management (VHM), the ground processing associated with the fleet (GVHM) and the Ground Infrastructure Health Management (GIHM). The objective is to provide an automated collection and paperless health decision, maintenance and logistics system. Many critical technologies are necessary to make the SHM (and more specifically VHM) practical, reliable and cost effective. Sanders is leading the design, development and integration of the SHM system for RLV and X-33 SHM (a sub-scale, sub-orbit Advanced Technology Demonstrator). This paper will present the X-33 SHM design which forms the baseline for RLV SHM. This paper will also discuss other applications of these technologies.

Science, precaution, and the politics of technological risk: converging implications in evolutionary and social scientific perspectives.

PubMed

Stirling, Andy

2008-04-01

This paper examines apparent tensions between "science-based," "precautionary," and "participatory" approaches to decision making on risk. Partly by reference to insights currently emerging in evolutionary studies, the present paper looks for ways to reconcile some of the contradictions. First, I argue that technological evolution is a much more plural and open-ended process than is conventionally supposed. Risk politics is thus implicitly as much about social choice of technological pathways as narrow issues of safety. Second, it is shown how conventional "science-based" risk assessment techniques address only limited aspects of incomplete knowledge in complex, dynamic, evolutionary processes. Together, these understandings open the door to more sophisticated, comprehensive, rational, and robust decision-making processes. Despite their own limitations, it is found that precautionary and participatory approaches help to address these needs. A concrete framework is outlined through which the synergies can be more effectively harnessed. By this means, we can hope simultaneously to improve scientific rigor and democratic legitimacy in risk governance.

Alcohol Use Disorders in Pregnancy

PubMed Central

DeVido, Jeffrey; Bogunovic, Olivera; Weiss, Roger D.

2015-01-01

Alcohol use disorders (AUD) during pregnancy are less prevalent than in non-pregnant women, but they can create a host of clinical challenges when encountered. Unfortunately, there is little research information available to guide clinical decision-making in this population. Drinking alcohol during pregnancy can have negative consequences on both fetus and mother, but there is controversy regarding the volume of alcohol consumption that correlates with these consequences. There is little evidence to support the use of pharmacologic interventions for AUD during pregnancy. Similarly, there are few data to guide management of alcohol detoxification in pregnant women, and the use of benzodiazepines (the mainstay of most alcohol detoxification protocols) in pregnant women is controversial. Despite a lack of robust data to guide management of AUDs in pregnancy, clinicians must nonetheless make management decisions when confronted with these challenging situations. Therefore, this paper reviews the epidemiology of AUDs in pregnancy, and the pharmacologic management of both AUDs and alcohol withdrawal in pregnant women, to better inform clinicians about what is known about managing these co-occurring conditions. PMID:25747924

Advanced driver assistance system: Road sign identification using VIAPIX system and a correlation technique

NASA Astrophysics Data System (ADS)

Ouerhani, Y.; Alfalou, A.; Desthieux, M.; Brosseau, C.

2017-02-01

We present a three-step approach based on the commercial VIAPIX® module for road traffic sign recognition and identification. Firstly, detection in a scene of all objects having characteristics of traffic signs is performed. This is followed by a first-level recognition based on correlation which consists in making a comparison between each detected object with a set of reference images of a database. Finally, a second level of identification allows us to confirm or correct the previous identification. In this study, we perform a correlation-based analysis by combining and adapting the Vander Lugt correlator with the nonlinear joint transformation correlator (JTC). Of particular significance, this approach permits to make a reliable decision on road traffic sign identification. We further discuss a robust scheme allowing us to track a detected road traffic sign in a video sequence for the purpose of increasing the decision performance of our system. This approach can have broad practical applications in the maintenance and rehabilitation of transportation infrastructure, or for drive assistance.

Family conflict is associated with longitudinal changes in insular-striatal functional connectivity during adolescent risk taking under maternal influence.

PubMed

Guassi Moreira, João F; Telzer, Eva H

2017-12-11

Maternal presence has marked effects on adolescent neurocognition during risk taking, influencing teenagers to make safer decisions. However, it is currently unknown whether maternal buffering changes over the course of adolescence itself, and whether its effects are robust to individual differences in family relationship quality. In the current longitudinal study, 23 adolescents completed a risk-taking task under maternal presence during an fMRI scan before and after the transition to high school. Behavioral results reveal that adolescent risk taking increased under maternal presence across a one-year period. At the neural level, we found that adolescents reporting higher family conflict showed longitudinal increases in functional coupling between the anterior insula (AI) and ventral striatum (VS) when making safe decisions in the presence of their mother, which was associated with increased real-world risk taking. These findings show that individual differences in family relationship quality undermine effective development of AI-VS connectivity resulting in increased risk taking. © 2017 John Wiley & Sons Ltd.

Decision making tools for selecting sustainable wastewater treatment technologies in Thailand

NASA Astrophysics Data System (ADS)

Wongburi, Praewa; Park, Jae K.

2018-05-01

Wastewater consists of valuable resources that could be recovered or reused. Still it is under threat because of ineffective wastewater management and systems. In Thailand, less than 25% of wastewater generated may be treated while then rest is inadequately treated and sent back directly into waterbodies or the environment. Furthermore, the technologies that have been applied may be inefficient and unsustainable. Efficiency, sustainability, and simplicity are important concepts when designing an appropriate wastewater treatment system in developing countries. The objectives of this study were to review and evaluate wastewater treatment technologies and propose a method to improve or select an appropriate technology. An expert system in Excel® program was developed to determine the best solution. Sensitivity analysis was applied to compare and assess uncertainty factors. Due to the different conditions of each area, the key factor of interest was varied. Furthermore, Robust Decision Making tool was applied to determine the best way to improve existing wastewater treatment facility and to choose the most appropriate wastewater treatment technology.

Science-based decision making in a high-risk energy production environment

NASA Astrophysics Data System (ADS)

Weiser, D. A.

2016-12-01

Energy production practices that may induce earthquakes require decisions about acceptable risk before projects begin. How much ground shaking, structural damage, infrastructure damage, or delay of geothermal power and other operations is tolerable? I review a few mitigation strategies as well as existing protocol in several U.S. states. Timely and accurate scientific information can assist in determining the costs and benefits of altering production parameters. These issues can also be addressed with probability estimates of adverse effects ("costs"), frequency of earthquakes of different sizes, and associated impacts of different magnitude earthquakes. When risk management decisions based on robust science are well-communicated to stakeholders, mitigation efforts benefit. Effective communications elements include a) the risks and benefits of different actions (e.g. using a traffic light protocol); b) the factors to consider when determining acceptable risk; and c) the probability of different magnitude events. I present a case example for The Geysers geothermal field in California, to discuss locally "acceptable" and "unacceptable" earthquakes and share nearby communities' responses to smaller and larger magnitude earthquakes. I use the USGS's "Did You Feel It?" data archive to sample how often felt events occur, and how many of those are above acceptable magnitudes (to both local residents and operators). Using this information, I develop a science-based decision-making framework, in the case of potentially risky earthquakes, for lessening seismic risk and other negative consequences. This includes assessing future earthquake probabilities based on past earthquake records. One of my goals is to help characterize uncertainties in a way that they can be managed; to this end, I present simple and accessible approaches that can be used in the decision making process.

Testing the robustness of management decisions to uncertainty: Everglades restoration scenarios.

PubMed

Fuller, Michael M; Gross, Louis J; Duke-Sylvester, Scott M; Palmer, Mark

2008-04-01

To effectively manage large natural reserves, resource managers must prepare for future contingencies while balancing the often conflicting priorities of different stakeholders. To deal with these issues, managers routinely employ models to project the response of ecosystems to different scenarios that represent alternative management plans or environmental forecasts. Scenario analysis is often used to rank such alternatives to aid the decision making process. However, model projections are subject to uncertainty in assumptions about model structure, parameter values, environmental inputs, and subcomponent interactions. We introduce an approach for testing the robustness of model-based management decisions to the uncertainty inherent in complex ecological models and their inputs. We use relative assessment to quantify the relative impacts of uncertainty on scenario ranking. To illustrate our approach we consider uncertainty in parameter values and uncertainty in input data, with specific examples drawn from the Florida Everglades restoration project. Our examples focus on two alternative 30-year hydrologic management plans that were ranked according to their overall impacts on wildlife habitat potential. We tested the assumption that varying the parameter settings and inputs of habitat index models does not change the rank order of the hydrologic plans. We compared the average projected index of habitat potential for four endemic species and two wading-bird guilds to rank the plans, accounting for variations in parameter settings and water level inputs associated with hypothetical future climates. Indices of habitat potential were based on projections from spatially explicit models that are closely tied to hydrology. For the American alligator, the rank order of the hydrologic plans was unaffected by substantial variation in model parameters. By contrast, simulated major shifts in water levels led to reversals in the ranks of the hydrologic plans in 24.1-30.6% of the projections for the wading bird guilds and several individual species. By exposing the differential effects of uncertainty, relative assessment can help resource managers assess the robustness of scenario choice in model-based policy decisions.

In-vivo analysis of ankle joint movement for patient-specific kinematic characterization.

PubMed

Ferraresi, Carlo; De Benedictis, Carlo; Franco, Walter; Maffiodo, Daniela; Leardini, Alberto

2017-09-01

In this article, a method for the experimental in-vivo characterization of the ankle kinematics is proposed. The method is meant to improve personalization of various ankle joint treatments, such as surgical decision-making or design and application of an orthosis, possibly to increase their effectiveness. This characterization in fact would make the treatments more compatible with the specific patient's joint physiological conditions. This article describes the experimental procedure and the analytical method adopted, based on the instantaneous and mean helical axis theories. The results obtained in this experimental analysis reveal that more accurate techniques are necessary for a robust in-vivo assessment of the tibio-talar axis of rotation.

Compromise-based Robust Prioritization of Climate Change Adaptation Strategies for Watershed Management

NASA Astrophysics Data System (ADS)

Kim, Y.; Chung, E. S.

2014-12-01

This study suggests a robust prioritization framework for climate change adaptation strategies under multiple climate change scenarios with a case study of selecting sites for reusing treated wastewater (TWW) in a Korean urban watershed. The framework utilizes various multi-criteria decision making techniques, including the VIKOR method and the Shannon entropy-based weights. In this case study, the sustainability of TWW use is quantified with indicator-based approaches with the DPSIR framework, which considers both hydro-environmental and socio-economic aspects of the watershed management. Under the various climate change scenarios, the hydro-environmental responses to reusing TWW in potential alternative sub-watersheds are determined using the Hydrologic Simulation Program in Fortran (HSPF). The socio-economic indicators are obtained from the statistical databases. Sustainability scores for multiple scenarios are estimated individually and then integrated with the proposed approach. At last, the suggested framework allows us to prioritize adaptation strategies in a robust manner with varying levels of compromise between utility-based and regret-based strategies.

A platform for population-based weight management: description of a health plan-based integrated systems approach.

PubMed

Pronk, Nicolaas P; Boucher, Jackie L; Gehling, Eve; Boyle, Raymond G; Jeffery, Robert W

2002-10-01

To describe an integrated, operational platform from which mail- and telephone-based health promotion programs are implemented and to specifically relate this approach to weight management programming in a managed care setting. In-depth description of essential systems structures, including people, computer technology, and decision-support protocols. The roles of support staff, counselors, a librarian, and a manager in delivering a weight management program are described. Information availability using computer technology is a critical component in making this system effective and is presented according to its architectural layout and design. Protocols support counselors and administrative support staff in decision making, and a detailed flowchart presents the layout of this part of the system. This platform is described in the context of a weight management program, and we present baseline characteristics of 1801 participants, their behaviors, self-reported medical conditions, and initial pattern of enrollment in the various treatment options. Considering the prevalence and upward trend of overweight and obesity in the United States, a need exists for robust intervention platforms that can systematically support multiple types of programs. Weight management interventions implemented using this platform are scalable to the population level and are sustainable over time despite the limits of defined resources and budgets. The present article describes an innovative approach to reaching a large population with effective programs in an integrated, coordinated, and systematic manner. This comprehensive, robust platform represents an example of how obesity prevention and treatment research may be translated into the applied setting.

Climate Risk Informed Decision Analysis (CRIDA): A novel practical guidance for Climate Resilient Investments and Planning

NASA Astrophysics Data System (ADS)

Jeuken, Ad; Mendoza, Guillermo; Matthews, John; Ray, Patrick; Haasnoot, Marjolijn; Gilroy, Kristin; Olsen, Rolf; Kucharski, John; Stakhiv, Gene; Cushing, Janet; Brown, Casey

2016-04-01

Engineers and water managers have always incorporated uncertainty in water resources operations, design and planning. In recent years, concern has been growing concern that many of the fundamental principles to address uncertainty in planning and design are insufficient for coping with unprecedented shifts in climate, especially given the long lifetimes of water investments - spanning decades, even centuries. Can we design and operate new flood risk management, energy, water supply and sanitation, and agricultural projects that are robust to shifts over 20, 50, or more years? Since about 2009, better approaches to planning and designing under climate uncertainty have been gaining ground worldwide. The main challenge is to operationalize these approaches and bring them from science to practice, embed them within the existing decision-making processes of particular institutions, and shift from highly specialized "boutique" applications to methods that result in consistent, replicable outcomes accessible to water managers worldwide. With CRIDA a serious step is taken to achieve these goals. CRIDA is built on two innovative but complementary approaches that have developed in isolation across the Atlantic over the past seven years: diagnosing and assessing risk (decision scaling), and developing sequential decision steps to compensate for uncertainty within regulatory / performance standards (adaptation pathways). First, the decision scaling or "bottom up" framework to climate change adaptation was first conceptualized during the US/Canada Great Lakes regulation study and has recently been placed in a decision-making context for water-related investments published by the World Bank Second, the adaptation pathways approach was developed in the Netherlands to cope with the level of climate uncertainty we now face. Adaptation pathways is a tool for maintaining options and flexibility while meeting operational goals by envisioning how sequences of decisions can be navigated over time. They are part of the Dutch adaptive planning approach Adaptive Delta Management, executed and develop by the Dutch Delta program. Both decision scaling and adaptation pathways have been piloted in studies worldwide. The objective of CRIDA is to mainstream effective climate adaptation for professional water managers. The CRIDA publication, due in april 2016, follows the generic water design planning design cycle. At each step, CRIDA describes stepwise guidance for incorporating climate robustness: problem definition, stress test, alternatives formulation and recommendation, evaluation and selection. In the presentation the origin, goal, steps and practical tools available at each step of CRIDA will be explained. In two other abstracts ("Climate Risk Informed Decision Analysis: A Hypothetical Application to the Waas Region" by Gilroy et al., "The Application of Climate Risk Informed Decision Analysis to the Ioland Water Treatment Plant in Lusaka, Zambia, by Kucharski et al.), the application of CRIDA to cases is explained

Rawlsian maximin rule operates as a common cognitive anchor in distributive justice and risky decisions

PubMed Central

Kameda, Tatsuya; Inukai, Keigo; Higuchi, Satomi; Ogawa, Akitoshi; Kim, Hackjin; Matsuda, Tetsuya; Sakagami, Masamichi

2016-01-01

Distributive justice concerns the moral principles by which we seek to allocate resources fairly among diverse members of a society. Although the concept of fair allocation is one of the fundamental building blocks for societies, there is no clear consensus on how to achieve “socially just” allocations. Here, we examine neurocognitive commonalities of distributive judgments and risky decisions. We explore the hypothesis that people’s allocation decisions for others are closely related to economic decisions for oneself at behavioral, cognitive, and neural levels, via a concern about the minimum, worst-off position. In a series of experiments using attention-monitoring and brain-imaging techniques, we investigated this “maximin” concern (maximizing the minimum possible payoff) via responses in two seemingly disparate tasks: third-party distribution of rewards for others, and choosing gambles for self. The experiments revealed three robust results: (i) participants’ distributive choices closely matched their risk preferences—“Rawlsians,” who maximized the worst-off position in distributions for others, avoided riskier gambles for themselves, whereas “utilitarians,” who favored the largest-total distributions, preferred riskier but more profitable gambles; (ii) across such individual choice preferences, however, participants generally showed the greatest spontaneous attention to information about the worst possible outcomes in both tasks; and (iii) this robust concern about the minimum outcomes was correlated with activation of the right temporoparietal junction (RTPJ), the region associated with perspective taking. The results provide convergent evidence that social distribution for others is psychologically linked to risky decision making for self, drawing on common cognitive–neural processes with spontaneous perspective taking of the worst-off position. PMID:27688764

Multiattribute selection of acute stroke imaging software platform for Extending the Time for Thrombolysis in Emergency Neurological Deficits (EXTEND) clinical trial.

PubMed

Churilov, Leonid; Liu, Daniel; Ma, Henry; Christensen, Soren; Nagakane, Yoshinari; Campbell, Bruce; Parsons, Mark W; Levi, Christopher R; Davis, Stephen M; Donnan, Geoffrey A

2013-04-01

The appropriateness of a software platform for rapid MRI assessment of the amount of salvageable brain tissue after stroke is critical for both the validity of the Extending the Time for Thrombolysis in Emergency Neurological Deficits (EXTEND) Clinical Trial of stroke thrombolysis beyond 4.5 hours and for stroke patient care outcomes. The objective of this research is to develop and implement a methodology for selecting the acute stroke imaging software platform most appropriate for the setting of a multi-centre clinical trial. A multi-disciplinary decision making panel formulated the set of preferentially independent evaluation attributes. Alternative Multi-Attribute Value Measurement methods were used to identify the best imaging software platform followed by sensitivity analysis to ensure the validity and robustness of the proposed solution. Four alternative imaging software platforms were identified. RApid processing of PerfusIon and Diffusion (RAPID) software was selected as the most appropriate for the needs of the EXTEND trial. A theoretically grounded generic multi-attribute selection methodology for imaging software was developed and implemented. The developed methodology assured both a high quality decision outcome and a rational and transparent decision process. This development contributes to stroke literature in the area of comprehensive evaluation of MRI clinical software. At the time of evaluation, RAPID software presented the most appropriate imaging software platform for use in the EXTEND clinical trial. The proposed multi-attribute imaging software evaluation methodology is based on sound theoretical foundations of multiple criteria decision analysis and can be successfully used for choosing the most appropriate imaging software while ensuring both robust decision process and outcomes. © 2012 The Authors. International Journal of Stroke © 2012 World Stroke Organization.

Value-based decision making via sequential sampling with hierarchical competition and attentional modulation

PubMed Central

2017-01-01

In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems. Connectionist models are proposed herein at an intermediate level of analysis that bridges mental phenomena and underlying neurophysiological mechanisms. Several such models drawing elements from the established race, drift-diffusion, feedforward-inhibition, divisive-normalization, and competing-accumulator models were tested with respect to fitting empirical data from human participants making choices between foods on the basis of hedonic value rather than a traditional perceptual attribute. Even when considering performance at emulating behavior alone, more neurally plausible models were set apart from more normative race or drift-diffusion models both quantitatively and qualitatively despite remaining parsimonious. To best capture the paradigm, a novel six-parameter computational model was formulated with features including hierarchical levels of competition via mutual inhibition as well as a static approximation of attentional modulation, which promotes “winner-take-all” processing. Moreover, a meta-analysis encompassing several related experiments validated the robustness of model-predicted trends in humans’ value-based choices and concomitant reaction times. These findings have yet further implications for analysis of neurophysiological data in accordance with computational modeling, which is also discussed in this new light. PMID:29077746

Value-based decision making via sequential sampling with hierarchical competition and attentional modulation.

PubMed

Colas, Jaron T

2017-01-01

In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems. Connectionist models are proposed herein at an intermediate level of analysis that bridges mental phenomena and underlying neurophysiological mechanisms. Several such models drawing elements from the established race, drift-diffusion, feedforward-inhibition, divisive-normalization, and competing-accumulator models were tested with respect to fitting empirical data from human participants making choices between foods on the basis of hedonic value rather than a traditional perceptual attribute. Even when considering performance at emulating behavior alone, more neurally plausible models were set apart from more normative race or drift-diffusion models both quantitatively and qualitatively despite remaining parsimonious. To best capture the paradigm, a novel six-parameter computational model was formulated with features including hierarchical levels of competition via mutual inhibition as well as a static approximation of attentional modulation, which promotes "winner-take-all" processing. Moreover, a meta-analysis encompassing several related experiments validated the robustness of model-predicted trends in humans' value-based choices and concomitant reaction times. These findings have yet further implications for analysis of neurophysiological data in accordance with computational modeling, which is also discussed in this new light.

The effect of response mode on lateralized lexical decision performance.

PubMed

Weems, Scott A; Zaidel, Eran

2005-01-01

We examined the effect of manipulations of response programming, i.e. post-lexical decision making requirements, on lateralized lexical decision. Although response hand manipulations tend to elicit weaker laterality effects than those involving visual field of presentation, the implementation of different lateralized response strategies remains relatively unexplored. Four different response conditions were compared in a between-subjects design: (1) unimanual, (2) bimanual, (3) congruent visual field/response hand, and (4) confounded response hand/target lexicality response. It was observed that hemispheric specialization and interaction effects during the lexical decision task remained unchanged despite the very different response requirements. However, a priori examination of each condition revealed that some manipulations yielded a reduced power to detect laterality effects. The consistent observation of left hemisphere specialization, and both left and right hemisphere lexicality priming effects (interhemispheric transfer), indicate that these effects are relatively robust and unaffected by late occurring processes in the lexical decision task. It appears that the lateralized response mode neither determines nor reflects the laterality of decision processes. In contrast, the target visual half-field is critical for determining the deciding hemisphere and is a sensitive index of hemispheric specialization, as well as of directional interhemispheric transfer.

Decision-Making in Critical Limb Ischemia: A Markov Simulation.

PubMed

Deutsch, Aaron J; Jain, C Charles; Blumenthal, Kimberly G; Dickinson, Mark W; Neilan, Anne M

2017-11-01

Critical limb ischemia (CLI) is a feared complication of peripheral vascular disease that often requires surgical management and may require amputation of the affected limb. We developed a decision model to inform clinical management for a 63-year-old woman with CLI and multiple medical comorbidities, including advanced heart failure and diabetes. We developed a Markov decision model to evaluate 4 strategies: amputation, surgical bypass, endovascular therapy (e.g. stent or revascularization), and medical management. We measured the impact of parameter uncertainty using 1-way, 2-way, and multiway sensitivity analyses. In the base case, endovascular therapy yielded similar discounted quality-adjusted life months (26.50 QALMs) compared with surgical bypass (26.34 QALMs). Both endovascular and surgical therapies were superior to amputation (18.83 QALMs) and medical management (11.08 QALMs). This finding was robust to a wide range of periprocedural mortality weights and was most sensitive to long-term mortality associated with endovascular and surgical therapies. Utility weights were not stratified by patient comorbidities; nonetheless, our conclusion was robust to a range of utility weight values. For a patient with CLI, endovascular therapy and surgical bypass provided comparable clinical outcomes. However, this finding was sensitive to long-term mortality rates associated with each procedure. Both endovascular and surgical therapies were superior to amputation or medical management in a range of scenarios. Copyright © 2017 Elsevier Inc. All rights reserved.

Overview of the Smart Network Element Architecture and Recent Innovations

NASA Technical Reports Server (NTRS)

Perotti, Jose M.; Mata, Carlos T.; Oostdyk, Rebecca L.

2008-01-01

In industrial environments, system operators rely on the availability and accuracy of sensors to monitor processes and detect failures of components and/or processes. The sensors must be networked in such a way that their data is reported to a central human interface, where operators are tasked with making real-time decisions based on the state of the sensors and the components that are being monitored. Incorporating health management functions at this central location aids the operator by automating the decision-making process to suggest, and sometimes perform, the action required by current operating conditions. Integrated Systems Health Management (ISHM) aims to incorporate data from many sources, including real-time and historical data and user input, and extract information and knowledge from that data to diagnose failures and predict future failures of the system. By distributing health management processing to lower levels of the architecture, there is less bandwidth required for ISHM, enhanced data fusion, make systems and processes more robust, and improved resolution for the detection and isolation of failures in a system, subsystem, component, or process. The Smart Network Element (SNE) has been developed at NASA Kennedy Space Center to perform intelligent functions at sensors and actuators' level in support of ISHM.

Robustness and Uncertainty: Applications for Policy in Climate and Hydrological Modeling

NASA Astrophysics Data System (ADS)

Fields, A. L., III

2015-12-01

Policymakers must often decide how to proceed when presented with conflicting simulation data from hydrological, climatological, and geological models. While laboratory sciences often appeal to the reproducibility of results to argue for the validity of their conclusions, simulations cannot use this strategy for a number of pragmatic and methodological reasons. However, robustness of predictions and causal structures can serve the same function for simulations as reproducibility does for laboratory experiments and field observations in either adjudicating between conflicting results or showing that there is insufficient justification to externally validate the results. Additionally, an interpretation of the argument from robustness is presented that involves appealing to the convergence of many well-built and diverse models rather than the more common version which involves appealing to the probability that one of a set of models is likely to be true. This interpretation strengthens the case for taking robustness as an additional requirement for the validation of simulation results and ultimately supports the idea that computer simulations can provide information about the world that is just as trustworthy as data from more traditional laboratory studies and field observations. Understanding the importance of robust results for the validation of simulation data is especially important for policymakers making decisions on the basis of potentially conflicting models. Applications will span climate, hydrological, and hydroclimatological models.

Working Memory and Decision-Making in a Frontoparietal Circuit Model

PubMed Central

2017-01-01

Working memory (WM) and decision-making (DM) are fundamental cognitive functions involving a distributed interacting network of brain areas, with the posterior parietal cortex (PPC) and prefrontal cortex (PFC) at the core. However, the shared and distinct roles of these areas and the nature of their coordination in cognitive function remain poorly understood. Biophysically based computational models of cortical circuits have provided insights into the mechanisms supporting these functions, yet they have primarily focused on the local microcircuit level, raising questions about the principles for distributed cognitive computation in multiregional networks. To examine these issues, we developed a distributed circuit model of two reciprocally interacting modules representing PPC and PFC circuits. The circuit architecture includes hierarchical differences in local recurrent structure and implements reciprocal long-range projections. This parsimonious model captures a range of behavioral and neuronal features of frontoparietal circuits across multiple WM and DM paradigms. In the context of WM, both areas exhibit persistent activity, but, in response to intervening distractors, PPC transiently encodes distractors while PFC filters distractors and supports WM robustness. With regard to DM, the PPC module generates graded representations of accumulated evidence supporting target selection, while the PFC module generates more categorical responses related to action or choice. These findings suggest computational principles for distributed, hierarchical processing in cortex during cognitive function and provide a framework for extension to multiregional models. SIGNIFICANCE STATEMENT Working memory and decision-making are fundamental “building blocks” of cognition, and deficits in these functions are associated with neuropsychiatric disorders such as schizophrenia. These cognitive functions engage distributed networks with prefrontal cortex (PFC) and posterior parietal cortex (PPC) at the core. It is not clear, however, what the contributions of PPC and PFC are in light of the computations that subserve working memory and decision-making. We constructed a biophysical model of a reciprocally connected frontoparietal circuit that revealed shared and distinct functions for the PFC and PPC across working memory and decision-making tasks. Our parsimonious model connects circuit-level properties to cognitive functions and suggests novel design principles beyond those of local circuits for cognitive processing in multiregional brain networks. PMID:29114071

Working Memory and Decision-Making in a Frontoparietal Circuit Model.

PubMed

Murray, John D; Jaramillo, Jorge; Wang, Xiao-Jing

2017-12-13

Working memory (WM) and decision-making (DM) are fundamental cognitive functions involving a distributed interacting network of brain areas, with the posterior parietal cortex (PPC) and prefrontal cortex (PFC) at the core. However, the shared and distinct roles of these areas and the nature of their coordination in cognitive function remain poorly understood. Biophysically based computational models of cortical circuits have provided insights into the mechanisms supporting these functions, yet they have primarily focused on the local microcircuit level, raising questions about the principles for distributed cognitive computation in multiregional networks. To examine these issues, we developed a distributed circuit model of two reciprocally interacting modules representing PPC and PFC circuits. The circuit architecture includes hierarchical differences in local recurrent structure and implements reciprocal long-range projections. This parsimonious model captures a range of behavioral and neuronal features of frontoparietal circuits across multiple WM and DM paradigms. In the context of WM, both areas exhibit persistent activity, but, in response to intervening distractors, PPC transiently encodes distractors while PFC filters distractors and supports WM robustness. With regard to DM, the PPC module generates graded representations of accumulated evidence supporting target selection, while the PFC module generates more categorical responses related to action or choice. These findings suggest computational principles for distributed, hierarchical processing in cortex during cognitive function and provide a framework for extension to multiregional models. SIGNIFICANCE STATEMENT Working memory and decision-making are fundamental "building blocks" of cognition, and deficits in these functions are associated with neuropsychiatric disorders such as schizophrenia. These cognitive functions engage distributed networks with prefrontal cortex (PFC) and posterior parietal cortex (PPC) at the core. It is not clear, however, what the contributions of PPC and PFC are in light of the computations that subserve working memory and decision-making. We constructed a biophysical model of a reciprocally connected frontoparietal circuit that revealed shared and distinct functions for the PFC and PPC across working memory and decision-making tasks. Our parsimonious model connects circuit-level properties to cognitive functions and suggests novel design principles beyond those of local circuits for cognitive processing in multiregional brain networks. Copyright © 2017 the authors 0270-6474/17/3712167-20$15.00/0.

Reducing regional vulnerabilities and multi-city robustness conflicts using many-objective optimization under deep uncertainty

NASA Astrophysics Data System (ADS)

Reed, Patrick; Trindade, Bernardo; Jonathan, Herman; Harrison, Zeff; Gregory, Characklis

2016-04-01

Emerging water scarcity concerns in southeastern US are associated with several deeply uncertain factors, including rapid population growth, limited coordination across adjacent municipalities and the increasing risks for sustained regional droughts. Managing these uncertainties will require that regional water utilities identify regionally coordinated, scarcity-mitigating strategies that trigger the appropriate actions needed to avoid water shortages and financial instabilities. This research focuses on the Research Triangle area of North Carolina, seeking to engage the water utilities within Raleigh, Durham, Cary and Chapel Hill in cooperative and robust regional water portfolio planning. Prior analysis of this region through the year 2025 has identified significant regional vulnerabilities to volumetric shortfalls and financial losses. Moreover, efforts to maximize the individual robustness of any of the mentioned utilities also have the potential to strongly degrade the robustness of the others. This research advances a multi-stakeholder Many-Objective Robust Decision Making (MORDM) framework to better account for deeply uncertain factors when identifying cooperative management strategies. Results show that the sampling of deeply uncertain factors in the computational search phase of MORDM can aid in the discovery of management actions that substantially improve the robustness of individual utilities as well as the overall region to water scarcity. Cooperative water transfers, financial risk mitigation tools, and coordinated regional demand management must be explored jointly to decrease robustness conflicts between the utilities. The insights from this work have general merit for regions where adjacent municipalities can benefit from cooperative regional water portfolio planning.

Reducing regional vulnerabilities and multi-city robustness conflicts using many-objective optimization under deep uncertainty

NASA Astrophysics Data System (ADS)

Trindade, B. C.; Reed, P. M.; Herman, J. D.; Zeff, H. B.; Characklis, G. W.

2015-12-01

Emerging water scarcity concerns in southeastern US are associated with several deeply uncertain factors, including rapid population growth, limited coordination across adjacent municipalities and the increasing risks for sustained regional droughts. Managing these uncertainties will require that regional water utilities identify regionally coordinated, scarcity-mitigating strategies that trigger the appropriate actions needed to avoid water shortages and financial instabilities. This research focuses on the Research Triangle area of North Carolina, seeking to engage the water utilities within Raleigh, Durham, Cary and Chapel Hill in cooperative and robust regional water portfolio planning. Prior analysis of this region through the year 2025 has identified significant regional vulnerabilities to volumetric shortfalls and financial losses. Moreover, efforts to maximize the individual robustness of any of the mentioned utilities also have the potential to strongly degrade the robustness of the others. This research advances a multi-stakeholder Many-Objective Robust Decision Making (MORDM) framework to better account for deeply uncertain factors when identifying cooperative management strategies. Results show that the sampling of deeply uncertain factors in the computational search phase of MORDM can aid in the discovery of management actions that substantially improve the robustness of individual utilities as well as of the overall region to water scarcity. Cooperative water transfers, financial risk mitigation tools, and coordinated regional demand management should be explored jointly to decrease robustness conflicts between the utilities. The insights from this work have general merit for regions where adjacent municipalities can benefit from cooperative regional water portfolio planning.

Extending the surrogacy analogy: applying the advance directive model to biobanks.

PubMed

Solomon, Stephanie; Mongoven, Ann

2015-01-01

Biobank donors and biobank governance face a conceptual challenge akin to clinical patients and their designated surrogate decision-makers, the necessity of making decisions and policies now that must be implemented under future unknown circumstances. We propose that biobanks take advantage of this parallel to learn lessons from the historical trajectory of advance directives and develop models analogous to current 'best practice' advance directives such as Values Histories and TheFive Wishes. We suggest how such models could improve biobanks' engagement both with communities and with individual donors by being more honest about the limits of current disclosure and eliciting information to ensure the protection of donor interests more robustly through time than current 'informed consent' processes in biobanking. © 2014 S. Karger AG, Basel.

Simulation reduction using the Taguchi method

NASA Technical Reports Server (NTRS)

Mistree, Farrokh; Lautenschlager, Ume; Erikstad, Stein Owe; Allen, Janet K.

1993-01-01

A large amount of engineering effort is consumed in conducting experiments to obtain information needed for making design decisions. Efficiency in generating such information is the key to meeting market windows, keeping development and manufacturing costs low, and having high-quality products. The principal focus of this project is to develop and implement applications of Taguchi's quality engineering techniques. In particular, we show how these techniques are applied to reduce the number of experiments for trajectory simulation of the LifeSat space vehicle. Orthogonal arrays are used to study many parameters simultaneously with a minimum of time and resources. Taguchi's signal to noise ratio is being employed to measure quality. A compromise Decision Support Problem and Robust Design are applied to demonstrate how quality is designed into a product in the early stages of designing.

The cost analysis of cemented versus cementless total hip replacement operations on the NHS.

PubMed

Kallala, R; Anderson, P; Morris, S; Haddad, F S

2013-07-01

In a time of limited resources, the debate continues over which types of hip prosthesis are clinically superior and more cost-effective. Orthopaedic surgeons increasingly need robust economic evidence to understand the full value of the operation, and to aid decision making on the 'package' of procedures that are available and to justify their practice beyond traditional clinical preference. In this paper we explore the current economic debate about the merits of cemented and cementless total hip replacement, an issue that continues to divide the orthopaedic community.

Workload-Adaptive Human Interface to Aid Robust Decision Making in Human-System Interface. Year 1 Report

DTIC Science & Technology

2014-04-30

performance is to create a computational system to mimic human game-play patterns. The objective of this study is to see to what extent we can...estimates as a function of task load. We conducted a pair of studies towards’ this end. In a first study , described in detail in Appendix D...could inform a system as to the relative workload of a user. In a second study , described in detail in Appendix E, participants were exposed to a 40

Info-gap management of public health Policy for TB with HIV-prevalence and epidemiological uncertainty

PubMed Central

2012-01-01

Background Formulation and evaluation of public health policy commonly employs science-based mathematical models. For instance, epidemiological dynamics of TB is dominated, in general, by flow between actively and latently infected populations. Thus modelling is central in planning public health intervention. However, models are highly uncertain because they are based on observations that are geographically and temporally distinct from the population to which they are applied. Aims We aim to demonstrate the advantages of info-gap theory, a non-probabilistic approach to severe uncertainty when worst cases cannot be reliably identified and probability distributions are unreliable or unavailable. Info-gap is applied here to mathematical modelling of epidemics and analysis of public health decision-making. Methods Applying info-gap robustness analysis to tuberculosis/HIV (TB/HIV) epidemics, we illustrate the critical role of incorporating uncertainty in formulating recommendations for interventions. Robustness is assessed as the magnitude of uncertainty that can be tolerated by a given intervention. We illustrate the methodology by exploring interventions that alter the rates of diagnosis, cure, relapse and HIV infection. Results We demonstrate several policy implications. Equivalence among alternative rates of diagnosis and relapse are identified. The impact of initial TB and HIV prevalence on the robustness to uncertainty is quantified. In some configurations, increased aggressiveness of intervention improves the predicted outcome but also reduces the robustness to uncertainty. Similarly, predicted outcomes may be better at larger target times, but may also be more vulnerable to model error. Conclusions The info-gap framework is useful for managing model uncertainty and is attractive when uncertainties on model parameters are extreme. When a public health model underlies guidelines, info-gap decision theory provides valuable insight into the confidence of achieving agreed-upon goals. PMID:23249291

Info-gap management of public health Policy for TB with HIV-prevalence and epidemiological uncertainty.

PubMed

Ben-Haim, Yakov; Dacso, Clifford C; Zetola, Nicola M

2012-12-19

Formulation and evaluation of public health policy commonly employs science-based mathematical models. For instance, epidemiological dynamics of TB is dominated, in general, by flow between actively and latently infected populations. Thus modelling is central in planning public health intervention. However, models are highly uncertain because they are based on observations that are geographically and temporally distinct from the population to which they are applied. We aim to demonstrate the advantages of info-gap theory, a non-probabilistic approach to severe uncertainty when worst cases cannot be reliably identified and probability distributions are unreliable or unavailable. Info-gap is applied here to mathematical modelling of epidemics and analysis of public health decision-making. Applying info-gap robustness analysis to tuberculosis/HIV (TB/HIV) epidemics, we illustrate the critical role of incorporating uncertainty in formulating recommendations for interventions. Robustness is assessed as the magnitude of uncertainty that can be tolerated by a given intervention. We illustrate the methodology by exploring interventions that alter the rates of diagnosis, cure, relapse and HIV infection. We demonstrate several policy implications. Equivalence among alternative rates of diagnosis and relapse are identified. The impact of initial TB and HIV prevalence on the robustness to uncertainty is quantified. In some configurations, increased aggressiveness of intervention improves the predicted outcome but also reduces the robustness to uncertainty. Similarly, predicted outcomes may be better at larger target times, but may also be more vulnerable to model error. The info-gap framework is useful for managing model uncertainty and is attractive when uncertainties on model parameters are extreme. When a public health model underlies guidelines, info-gap decision theory provides valuable insight into the confidence of achieving agreed-upon goals.

Water resources planning under climate change: Assessing the robustness of real options for the Blue Nile

NASA Astrophysics Data System (ADS)

Jeuland, Marc; Whittington, Dale

2014-03-01

This article presents a methodology for planning new water resources infrastructure investments and operating strategies in a world of climate change uncertainty. It combines a real options (e.g., options to defer, expand, contract, abandon, switch use, or otherwise alter a capital investment) approach with principles drawn from robust decision-making (RDM). RDM comprises a class of methods that are used to identify investment strategies that perform relatively well, compared to the alternatives, across a wide range of plausible future scenarios. Our proposed framework relies on a simulation model that includes linkages between climate change and system hydrology, combined with sensitivity analyses that explore how economic outcomes of investments in new dams vary with forecasts of changing runoff and other uncertainties. To demonstrate the framework, we consider the case of new multipurpose dams along the Blue Nile in Ethiopia. We model flexibility in design and operating decisions—the selection, sizing, and sequencing of new dams, and reservoir operating rules. Results show that there is no single investment plan that performs best across a range of plausible future runoff conditions. The decision-analytic framework is then used to identify dam configurations that are both robust to poor outcomes and sufficiently flexible to capture high upside benefits if favorable future climate and hydrological conditions should arise. The approach could be extended to explore design and operating features of development and adaptation projects other than dams.

Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data.

PubMed

Hager, Rebecca; Tsiatis, Anastasios A; Davidian, Marie

2018-05-18

Clinicians often make multiple treatment decisions at key points over the course of a patient's disease. A dynamic treatment regime is a sequence of decision rules, each mapping a patient's observed history to the set of available, feasible treatment options at each decision point, and thus formalizes this process. An optimal regime is one leading to the most beneficial outcome on average if used to select treatment for the patient population. We propose a method for estimation of an optimal regime involving two decision points when the outcome of interest is a censored survival time, which is based on maximizing a locally efficient, doubly robust, augmented inverse probability weighted estimator for average outcome over a class of regimes. By casting this optimization as a classification problem, we exploit well-studied classification techniques such as support vector machines to characterize the class of regimes and facilitate implementation via a backward iterative algorithm. Simulation studies of performance and application of the method to data from a sequential, multiple assignment randomized clinical trial in acute leukemia are presented. © 2018, The International Biometric Society.

Data-driven decision support for radiologists: re-using the National Lung Screening Trial dataset for pulmonary nodule management.

PubMed

Morrison, James J; Hostetter, Jason; Wang, Kenneth; Siegel, Eliot L

2015-02-01

Real-time mining of large research trial datasets enables development of case-based clinical decision support tools. Several applicable research datasets exist including the National Lung Screening Trial (NLST), a dataset unparalleled in size and scope for studying population-based lung cancer screening. Using these data, a clinical decision support tool was developed which matches patient demographics and lung nodule characteristics to a cohort of similar patients. The NLST dataset was converted into Structured Query Language (SQL) tables hosted on a web server, and a web-based JavaScript application was developed which performs real-time queries. JavaScript is used for both the server-side and client-side language, allowing for rapid development of a robust client interface and server-side data layer. Real-time data mining of user-specified patient cohorts achieved a rapid return of cohort cancer statistics and lung nodule distribution information. This system demonstrates the potential of individualized real-time data mining using large high-quality clinical trial datasets to drive evidence-based clinical decision-making.

Future perspectives toward the early definition of a multivariate decision-support scheme employed in clinical decision making for senior citizens.

PubMed

Frantzidis, Christos A; Gilou, Sotiria; Billis, Antonis; Karagianni, Maria; Bratsas, Charalampos D; Bamidis, Panagiotis

2016-03-01

Recent neuroscientific studies focused on the identification of pathological neurophysiological patterns (emotions, geriatric depression, memory impairment and sleep disturbances) through computerised clinical decision-support systems. Almost all these research attempts employed either resting-state condition (e.g. eyes-closed) or event-related potentials extracted during a cognitive task known to be affected by the disease under consideration. This Letter reviews existing data mining techniques and aims to enhance their robustness by proposing a holistic decision framework dealing with comorbidities and early symptoms' identification, while it could be applied in realistic occasions. Multivariate features are elicited and fused in order to be compared with average activities characteristic of each neuropathology group. A proposed model of the specific cognitive function which may be based on previous findings (a priori information) and/or validated by current experimental data should be then formed. So, the proposed scheme facilitates the early identification and prevention of neurodegenerative phenomena. Neurophysiological semantic annotation is hypothesised to enhance the importance of the proposed framework in facilitating the personalised healthcare of the information society and medical informatics research community.

The Neural Basis of Aversive Pavlovian Guidance during Planning

PubMed Central

Faulkner, Paul

2017-01-01

Important real-world decisions are often arduous as they frequently involve sequences of choices, with initial selections affecting future options. Evaluating every possible combination of choices is computationally intractable, particularly for longer multistep decisions. Therefore, humans frequently use heuristics to reduce the complexity of decisions. We recently used a goal-directed planning task to demonstrate the profound behavioral influence and ubiquity of one such shortcut, namely aversive pruning, a reflexive Pavlovian process that involves neglecting parts of the decision space residing beyond salient negative outcomes. However, how the brain implements this important decision heuristic and what underlies individual differences have hitherto remained unanswered. Therefore, we administered an adapted version of the same planning task to healthy male and female volunteers undergoing functional magnetic resonance imaging (fMRI) to determine the neural basis of aversive pruning. Through both computational and standard categorical fMRI analyses, we show that when planning was influenced by aversive pruning, the subgenual cingulate cortex was robustly recruited. This neural signature was distinct from those associated with general planning and valuation, two fundamental cognitive components elicited by our task but which are complementary to aversive pruning. Furthermore, we found that individual variation in levels of aversive pruning was associated with the responses of insula and dorsolateral prefrontal cortices to the receipt of large monetary losses, and also with subclinical levels of anxiety. In summary, our data reveal the neural signatures of an important reflexive Pavlovian process that shapes goal-directed evaluations and thereby determines the outcome of high-level sequential cognitive processes. SIGNIFICANCE STATEMENT Multistep decisions are complex because initial choices constrain future options. Evaluating every path for long decision sequences is often impractical; thus, cognitive shortcuts are often essential. One pervasive and powerful heuristic is aversive pruning, in which potential decision-making avenues are curtailed at immediate negative outcomes. We used neuroimaging to examine how humans implement such pruning. We found it to be associated with activity in the subgenual cingulate cortex, with neural signatures that were distinguishable from those covarying with planning and valuation. Individual variations in aversive pruning levels related to subclinical anxiety levels and insular cortex activation. These findings reveal the neural mechanisms by which basic negative Pavlovian influences guide decision-making during planning, with implications for disrupted decision-making in psychiatric disorders. PMID:28924006

The Neural Basis of Aversive Pavlovian Guidance during Planning.

PubMed

Lally, Níall; Huys, Quentin J M; Eshel, Neir; Faulkner, Paul; Dayan, Peter; Roiser, Jonathan P

2017-10-18

Important real-world decisions are often arduous as they frequently involve sequences of choices, with initial selections affecting future options. Evaluating every possible combination of choices is computationally intractable, particularly for longer multistep decisions. Therefore, humans frequently use heuristics to reduce the complexity of decisions. We recently used a goal-directed planning task to demonstrate the profound behavioral influence and ubiquity of one such shortcut, namely aversive pruning, a reflexive Pavlovian process that involves neglecting parts of the decision space residing beyond salient negative outcomes. However, how the brain implements this important decision heuristic and what underlies individual differences have hitherto remained unanswered. Therefore, we administered an adapted version of the same planning task to healthy male and female volunteers undergoing functional magnetic resonance imaging (fMRI) to determine the neural basis of aversive pruning. Through both computational and standard categorical fMRI analyses, we show that when planning was influenced by aversive pruning, the subgenual cingulate cortex was robustly recruited. This neural signature was distinct from those associated with general planning and valuation, two fundamental cognitive components elicited by our task but which are complementary to aversive pruning. Furthermore, we found that individual variation in levels of aversive pruning was associated with the responses of insula and dorsolateral prefrontal cortices to the receipt of large monetary losses, and also with subclinical levels of anxiety. In summary, our data reveal the neural signatures of an important reflexive Pavlovian process that shapes goal-directed evaluations and thereby determines the outcome of high-level sequential cognitive processes. SIGNIFICANCE STATEMENT Multistep decisions are complex because initial choices constrain future options. Evaluating every path for long decision sequences is often impractical; thus, cognitive shortcuts are often essential. One pervasive and powerful heuristic is aversive pruning, in which potential decision-making avenues are curtailed at immediate negative outcomes. We used neuroimaging to examine how humans implement such pruning. We found it to be associated with activity in the subgenual cingulate cortex, with neural signatures that were distinguishable from those covarying with planning and valuation. Individual variations in aversive pruning levels related to subclinical anxiety levels and insular cortex activation. These findings reveal the neural mechanisms by which basic negative Pavlovian influences guide decision-making during planning, with implications for disrupted decision-making in psychiatric disorders. Copyright © 2017 the authors 0270-6474/17/3710216-15$15.00/0.

Expanded envelope concepts for aircraft control-element failure detection and identification

NASA Technical Reports Server (NTRS)

Weiss, Jerold L.; Hsu, John Y.

1988-01-01

The purpose of this effort was to develop and demonstrate concepts for expanding the envelope of failure detection and isolation (FDI) algorithms for aircraft-path failures. An algorithm which uses analytic-redundancy in the form of aerodynamic force and moment balance equations was used. Because aircraft-path FDI uses analytical models, there is a tradeoff between accuracy and the ability to detect and isolate failures. For single flight condition operation, design and analysis methods are developed to deal with this robustness problem. When the departure from the single flight condition is significant, algorithm adaptation is necessary. Adaptation requirements for the residual generation portion of the FDI algorithm are interpreted as the need for accurate, large-motion aero-models, over a broad range of velocity and altitude conditions. For the decision-making part of the algorithm, adaptation may require modifications to filtering operations, thresholds, and projection vectors that define the various hypothesis tests performed in the decision mechanism. Methods of obtaining and evaluating adequate residual generation and decision-making designs have been developed. The application of the residual generation ideas to a high-performance fighter is demonstrated by developing adaptive residuals for the AFTI-F-16 and simulating their behavior under a variety of maneuvers using the results of a NASA F-16 simulation.

Deliberation favours social efficiency by making people disregard their relative shares: evidence from USA and India

PubMed Central

Corgnet, Brice; Espín, Antonio M.; Hernán-González, Roberto

2017-01-01

Groups make decisions on both the production and the distribution of resources. These decisions typically involve a tension between increasing the total level of group resources (i.e. social efficiency) and distributing these resources among group members (i.e. individuals' relative shares). This is the case because the redistribution process may destroy part of the resources, thus resulting in socially inefficient allocations. Here we apply a dual-process approach to understand the cognitive underpinnings of this fundamental tension. We conducted a set of experiments to examine the extent to which different allocation decisions respond to intuition or deliberation. In a newly developed approach, we assess intuition and deliberation at both the trait level (using the Cognitive Reflection Test, henceforth CRT) and the state level (through the experimental manipulation of response times). To test for robustness, experiments were conducted in two countries: the USA and India. Despite absolute-level differences across countries, in both locations we show that: (i) time pressure and low CRT scores are associated with individuals' concerns for their relative shares and (ii) time delay and high CRT scores are associated with individuals' concerns for social efficiency. These findings demonstrate that deliberation favours social efficiency by overriding individuals' intuitive tendency to focus on relative shares. PMID:28386421

Optimizing the response to surveillance alerts in automated surveillance systems.

PubMed

Izadi, Masoumeh; Buckeridge, David L

2011-02-28

Although much research effort has been directed toward refining algorithms for disease outbreak alerting, considerably less attention has been given to the response to alerts generated from statistical detection algorithms. Given the inherent inaccuracy in alerting, it is imperative to develop methods that help public health personnel identify optimal policies in response to alerts. This study evaluates the application of dynamic decision making models to the problem of responding to outbreak detection methods, using anthrax surveillance as an example. Adaptive optimization through approximate dynamic programming is used to generate a policy for decision making following outbreak detection. We investigate the degree to which the model can tolerate noise theoretically, in order to keep near optimal behavior. We also evaluate the policy from our model empirically and compare it with current approaches in routine public health practice for investigating alerts. Timeliness of outbreak confirmation and total costs associated with the decisions made are used as performance measures. Using our approach, on average, 80 per cent of outbreaks were confirmed prior to the fifth day of post-attack with considerably less cost compared to response strategies currently in use. Experimental results are also provided to illustrate the robustness of the adaptive optimization approach and to show the realization of the derived error bounds in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Integrative Governance of Environmental Water in Australia's Murray-Darling Basin: Evolving Challenges and Emerging Pathways.

PubMed

Bischoff-Mattson, Zachary; Lynch, Amanda H

2017-07-01

Integration, a widely promoted response to the multi-scale complexities of social-environmental sustainability, is diversely and sometimes poorly conceptualized. In this paper we explore integrative governance, which we define as an iterative and contextual process for negotiating and advancing the common interest. We ground this definition in a discussion of institutional factors conditioning integrative governance of environmental water in Australia's Murray-Darling Basin. The Murray-Darling Basin is an iconic system of social-ecological complexity, evocative of large-scale conservation challenges in other developed arid river basins. Our critical assessment of integrative governance practices in that context emerges through analysis of interviews with policy participants and documents pertaining to environmental water management in the tri-state area of southwestern New South Wales, northwestern Victoria, and the South Australian Riverland. We identify four linked challenges: (i) decision support for developing socially robust environmental water management goals, (ii) resource constraints on adaptive practice, (iii) inter-state differences in participatory decision-making and devolution of authority, and (iv) representative inclusion in decision-making. Our appraisal demonstrates these as pivotal challenges for integrative governance in the common interest. We conclude by offering a perspective on the potential for supporting integrative governance through the bridging capacity of Australia's Commonwealth Environmental Water Holder.

Climate change induced transformations of agricultural systems: insights from a global model

NASA Astrophysics Data System (ADS)

Leclère, D.; Havlík, P.; Fuss, S.; Schmid, E.; Mosnier, A.; Walsh, B.; Valin, H.; Herrero, M.; Khabarov, N.; Obersteiner, M.

2014-12-01

Climate change might impact crop yields considerably and anticipated transformations of agricultural systems are needed in the coming decades to sustain affordable food provision. However, decision-making on transformational shifts in agricultural systems is plagued by uncertainties concerning the nature and geography of climate change, its impacts, and adequate responses. Locking agricultural systems into inadequate transformations costly to adjust is a significant risk and this acts as an incentive to delay action. It is crucial to gain insight into how much transformation is required from agricultural systems, how robust such strategies are, and how we can defuse the associated challenge for decision-making. While implementing a definition related to large changes in resource use into a global impact assessment modelling framework, we find transformational adaptations to be required of agricultural systems in most regions by 2050s in order to cope with climate change. However, these transformations widely differ across climate change scenarios: uncertainties in large-scale development of irrigation span in all continents from 2030s on, and affect two-thirds of regions by 2050s. Meanwhile, significant but uncertain reduction of major agricultural areas affects the Northern Hemisphere’s temperate latitudes, while increases to non-agricultural zones could be large but uncertain in one-third of regions. To help reducing the associated challenge for decision-making, we propose a methodology exploring which, when, where and why transformations could be required and uncertain, by means of scenario analysis.

Application of ANP and DEMATEL to evaluate the decision-making of municipal solid waste management in Metro Manila.

PubMed

Tseng, Ming-Lang

2009-09-01

A municipal solid waste management (MSW) expert group was consulted in order to mirror how government officials might reach an effective solution regarding municipal solid waste management in Metro Manila. A critical issue regarding this is how the expert group can better evaluate and select a favorable MSW management solution using a series of criteria. MSW management solution selection is a multiple criteria decision-making (MCDM) problem, which requires the consideration of a large number of complex criteria. A robust MCDM method should consider the interactions among these criteria. The analytic network process (ANP) is a relatively new MCDM method which can deal with all kinds of interactions systematically. The Decision Making Trial and Evaluation Laboratory (DEMATEL) not only can convert the relations between cause and effect of criteria into a structural model, but also can be used as a way to handle the inner dependences within a set of criteria. Hence, this paper applies an effective solution based on a combined ANP and DEMATEL method to assist the expert group evaluating different MSW management solutions. According to the results, the best solution is for each city to have its own type of thermal process technology and resource recovery facility before landfill rather than entering a joint venture with enterprises or going into build-operate-transfer projects in order to be able to construct thermal process technologies and resource recovery facilities.

Patient-reported outcomes in randomised controlled trials of prostate cancer: methodological quality and impact on clinical decision making.

PubMed

Efficace, Fabio; Feuerstein, Michael; Fayers, Peter; Cafaro, Valentina; Eastham, James; Pusic, Andrea; Blazeby, Jane

2014-09-01

Patient-reported outcomes (PRO) data from randomised controlled trials (RCTs) are increasingly used to inform patient-centred care as well as clinical and health policy decisions. The main objective of this study was to investigate the methodological quality of PRO assessment in RCTs of prostate cancer (PCa) and to estimate the likely impact of these studies on clinical decision making. A systematic literature search of studies was undertaken on main electronic databases to retrieve articles published between January 2004 and March 2012. RCTs were evaluated on a predetermined extraction form, including (1) basic trial demographics and clinical and PRO characteristics; (2) level of PRO reporting based on the recently published recommendations by the International Society for Quality of Life Research; and (3) bias, assessed using the Cochrane Risk of Bias tool. Studies were systematically analysed to evaluate their relevance for supporting clinical decision making. Sixty-five RCTs enrolling a total of 22 071 patients were evaluated, with 31 (48%) in patients with nonmetastatic disease. When a PRO difference between treatments was found, it related in most cases to symptoms only (n=29, 58%). Although the extent of missing data was generally documented (72% of RCTs), few reported details on statistical handling of this data (18%) and reasons for dropout (35%). Improvements in key methodological aspects over time were found. Thirteen (20%) RCTs were judged as likely to be robust in informing clinical decision making. Higher-quality PRO studies were generally associated with those RCTs that had higher internal validity. Including PRO in RCTs of PCa patients is critical for better evaluating the treatment effectiveness of new therapeutic approaches. Marked improvements in PRO quality reporting over time were found, and it is estimated that at least one-fifth of PRO RCTs have provided sufficient details to allow health policy makers and physicians to make critical appraisals of results. In this report, we have investigated the methodological quality of PCa trials that have included a PRO assessment. We conclude that including PRO is critical to better evaluating the treatment effectiveness of new therapeutic approaches from the patient's perspective. Also, at least one-fifth of PRO RCTs in PCa have provided sufficient details to allow health policy makers and physicians to make a critical appraisal of results. Copyright © 2013. Published by Elsevier B.V.

Exposure levels for chemical threat compounds: information to facilitate chemical incident response.

PubMed

Hauschild, Veronique D; Watson, Annetta

2013-01-01

Although not widely known, a robust set of peer-reviewed public health and occupational exposure levels presently exist for key chemical warfare agents (CWAs) and certain acutely toxic industrial chemicals (TICs) identified as terrorist attack threats. Familiarity with these CWA and TIC exposure levels and their historic applications has facilitated emergency management decision-making by public and environmental health decision-makers. Specifically, multiple air, soil, and water exposure levels for CWAs and TICs summarized here have been extensively peer-reviewed and published; many have been recognized and are in use by federal and state health agencies as criteria for hazard zone prediction and assessment, occupational safety, and "how clean is clean enough" decisions. The key, however, is to know which criteria are most appropriate for specific decisions. While public safety is critical, high levels of concern often associated with perceived or actual proximity to extremely toxic chemical agents could result in overly cautious decisions that generate excessive delays, expenditure of scarce resources, and technological difficulties. Rapid selection of the most appropriate chemical exposure criteria is recommended to avoid such problems and expedite all phases of chemical incident response and recovery.

[Application of evidence based medicine to the individual patient: the role of decision analysis].

PubMed

Housset, B; Junod, A F

2003-11-01

The objective of evidence based medicine (EBM) is to contribute to medical decision making by providing the best possible information in terms of validity and relevance. This allows evaluation in a specific manner of the benefits and risks of a decision. The limitations and hazards of this approach are discussed in relation to a clinical case where the diagnosis of pulmonary embolism was under consideration. The individual details and the limited availability of some technical procedures illustrate the need to adapt the data of EBM to the circumstances. The choice between two diagnostic tests (d-dimers and ultrasound of the legs) and their optimal timing is analysed with integration of the consequences for the patient of the treatments proposed. This allows discussion of the concept of utility and the use of sensitivity analysis. If EBM is the cornerstone of rational and explicit practise it should also allow for the constraints of real life. Decision analysis, which depends on the same critical demands as EBM but can also take account of the individual features of each patient and test the robustness of a decision, gives a unique opportunity reconcile rigorous reasoning with individualisation of management.

Human Decision Processes: Implications for SSA Support Tools

NASA Astrophysics Data System (ADS)

Picciano, P.

2013-09-01

Despite significant advances in computing power and artificial intelligence (AI), few critical decisions are made without a human decision maker in the loop. Space Situational Awareness (SSA) missions are both critical and complex, typically adhering to the human-in-the-loop (HITL) model. The collection of human operators injects a needed diversity of expert knowledge, experience, and authority required to successfully fulfill SSA tasking. A wealth of literature on human decision making exists citing myriad empirical studies and offering a varied set of prescriptive and descriptive models of judgment and decision making (Hastie & Dawes, 2001; Baron, 2000). Many findings have been proven sufficiently robust to allow information architects or system/interface designers to take action to improve decision processes. For the purpose of discussion, these concepts are bifurcated in two groups: 1) vulnerabilities to mitigate, and 2) capabilities to augment. These vulnerabilities and capabilities refer specifically to the decision process and should not be confused with a shortcoming or skill of a specific human operator. Thus the framing of questions and orders, the automated tools with which to collaborate, priming and contextual data, and the delivery of information all play a critical role in human judgment and choice. Evaluating the merits of any decision can be elusive; in order to constrain this discussion, ‘rational choice' will tend toward the economic model characteristics such as maximizing utility and selection consistency (e.g., if A preferred to B, and B preferred to C, than A should be preferred to C). Simple decision models often encourage one to list the pros and cons of a decision, perhaps use a weighting schema, but one way or another weigh the future benefit (or harm) of making a selection. The result (sought by the rationalist models) should drive toward higher utility. Despite notable differences in researchers' theses (to be discussed in the full paper), one opinion shared is that the rational, economic, deliberate listing/evaluation of all options is NOT representative of how many decision are made. A framework gaining interest lately describes two systems predominantly at work: intuition and reasoning (Kahneman, 2003). Intuition is fast, automatic, and parallel contrasted with the more effortful, deliberative, and sequential reasoning. One of the issues of contention is that considerable research is stacked supporting both sides claiming that intuition is: • A hallmark of expertise responsible for rapid, optimal decisions in the face of adversity • A vulnerability where biases serve as decision traps leading to wrong choices Using seminal studies from a range of domains and tasking, potential solutions for SSA decision support will be offered. Important issues such as managing uncertainty, framing inquiries, and information architecture, and contextual cues will be discussed. The purpose is to provide awareness of the human limitations and capabilities in complex decision making so engineers and designers can consider such factors in their development of SSA tools.

Making Optic Flow Robust to Dynamic Lighting Conditions for Real-Time Operation

DTIC Science & Technology

2016-03-17

ARL-TR-7629 ● MAR 2016 US Army Research Laboratory Making Optic Flow Robust to Dynamic Lighting Conditions for Real-Time...ARL-TR-7629 ● MAR 2016 US Army Research Laboratory Making Optic Flow Robust to Dynamic Lighting Conditions for Real-Time Operation...SUBTITLE Making Optic Flow Robust to Dynamic Lighting Conditions for Real-Time Operation 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT

Using Discrete Choice Experiments to Inform the Benefit-Risk Assessment of Medicines: Are We Ready Yet?

PubMed

Vass, Caroline M; Payne, Katherine

2017-09-01

There is emerging interest in the use of discrete choice experiments as a means of quantifying the perceived balance between benefits and risks (quantitative benefit-risk assessment) of new healthcare interventions, such as medicines, under assessment by regulatory agencies. For stated preference data on benefit-risk assessment to be used in regulatory decision making, the methods to generate these data must be valid, reliable and capable of producing meaningful estimates understood by decision makers. Some reporting guidelines exist for discrete choice experiments, and for related methods such as conjoint analysis. However, existing guidelines focus on reporting standards, are general in focus and do not consider the requirements for using discrete choice experiments specifically for quantifying benefit-risk assessments in the context of regulatory decision making. This opinion piece outlines the current state of play in using discrete choice experiments for benefit-risk assessment and proposes key areas needing to be addressed to demonstrate that discrete choice experiments are an appropriate and valid stated preference elicitation method in this context. Methodological research is required to establish: how robust the results of discrete choice experiments are to formats and methods of risk communication; how information in the discrete choice experiment can be presented effectually to respondents; whose preferences should be elicited; the correct underlying utility function and analytical model; the impact of heterogeneity in preferences; and the generalisability of the results. We believe these methodological issues should be addressed, alongside developing a 'reference case', before agencies can safely and confidently use discrete choice experiments for quantitative benefit-risk assessment in the context of regulatory decision making for new medicines and healthcare products.

Communication During Pediatric Intensive Care Unit Family Conferences: A Pilot Study of Content, Communication, and Parent Perceptions.

PubMed

Michelson, Kelly; Clayman, Marla L; Ryan, Claire; Emanuel, Linda; Frader, Joel

2017-10-01

While there is a robust literature describing family conferences (FCs) in adult intensive care units (ICUs), less information exists about FCs in pediatric ICUs (PICUs). We conducted a pilot study to describe the focus of discussion, communication patterns of health care team members (HTMs) and parents, and parents' perspectives about clinician communication during PICU FCs. We analyzed data from 22 video- or audiorecorded PICU FCs and post-FC questionnaire responses from 27 parents involved in 18 FCs. We used the Roter Interaction Analysis System (RIAS) to describe FC dialogue content. Our questionnaire included the validated Communication Assessment Tool (CAT). FCs were focused on care planning (n = 5), decision making (n = 6), and updates (n = 11). Most speech came from HTMs (mean 85%; range, 65-94%). Most HTM utterances involved medical information. Most parent utterances involved asking for explanations. The mean overall CAT score was 4.62 (using a 1-5 scale where 5 represents excellent and 1 poor) with a mean of 73.02% "excellent" responses. Update and care-planning FCs had lower CAT scores compared to decision-making FCs. The lowest scoring CAT items were "Involved me in decisions as much as I wanted," "Talked in terms I could understand," and "Gave me as much information as I wanted." These findings suggest that while health care providers spend most of their time during FCs relaying medical information, more attention should be directed at providing information in an understandable manner. More work is needed to improve communication when decision making is not the main focus of the FC.

A nonlinear bi-level programming approach for product portfolio management.

PubMed

Ma, Shuang

2016-01-01

Product portfolio management (PPM) is a critical decision-making for companies across various industries in today's competitive environment. Traditional studies on PPM problem have been motivated toward engineering feasibilities and marketing which relatively pay less attention to other competitors' actions and the competitive relations, especially in mathematical optimization domain. The key challenge lies in that how to construct a mathematical optimization model to describe this Stackelberg game-based leader-follower PPM problem and the competitive relations between them. The primary work of this paper is the representation of a decision framework and the optimization model to leverage the PPM problem of leader and follower. A nonlinear, integer bi-level programming model is developed based on the decision framework. Furthermore, a bi-level nested genetic algorithm is put forward to solve this nonlinear bi-level programming model for leader-follower PPM problem. A case study of notebook computer product portfolio optimization is reported. Results and analyses reveal that the leader-follower bi-level optimization model is robust and can empower product portfolio optimization.

Generalized railway tank car safety design optimization for hazardous materials transport: addressing the trade-off between transportation efficiency and safety.

PubMed

Saat, Mohd Rapik; Barkan, Christopher P L

2011-05-15

North America railways offer safe and generally the most economical means of long distance transport of hazardous materials. Nevertheless, in the event of a train accident releases of these materials can pose substantial risk to human health, property or the environment. The majority of railway shipments of hazardous materials are in tank cars. Improving the safety design of these cars to make them more robust in accidents generally increases their weight thereby reducing their capacity and consequent transportation efficiency. This paper presents a generalized tank car safety design optimization model that addresses this tradeoff. The optimization model enables evaluation of each element of tank car safety design, independently and in combination with one another. We present the optimization model by identifying a set of Pareto-optimal solutions for a baseline tank car design in a bicriteria decision problem. This model provides a quantitative framework for a rational decision-making process involving tank car safety design enhancements to reduce the risk of transporting hazardous materials. Copyright © 2011 Elsevier B.V. All rights reserved.

Alcohol use disorders in pregnancy.

PubMed

DeVido, Jeffrey; Bogunovic, Olivera; Weiss, Roger D

2015-01-01

Alcohol use disorders (AUDs) are less prevalent in pregnant women than in nonpregnant women, but these disorders can create a host of clinical challenges when encountered. Unfortunately, little evidence is available to guide clinical decision making in this population. Drinking alcohol during pregnancy can have negative consequences on both fetus and mother, but it remains controversial as to the volume of alcohol consumption that correlates with these consequences. Likewise, little evidence is available to support the use of particular pharmacologic interventions for AUDs during pregnancy or to guide the management of alcohol detoxification in pregnant women. The use of benzodiazepines (the mainstay of most alcohol detoxification protocols) in pregnant women is controversial. Nevertheless, despite the lack of robust data to guide management of AUDs in pregnancy, clinicians need to make management decisions when confronted with these challenging situations. In that context, this article reviews the epidemiology of AUDs in pregnancy and the pharmacologic management of both AUDs and alcohol withdrawal in pregnant women, with the goal of informing clinicians about what is known about managing these co-occurring conditions.

A group decision-making tool for the application of membrane technologies in different water reuse scenarios.

PubMed

Sadr, S M K; Saroj, D P; Kouchaki, S; Ilemobade, A A; Ouki, S K

2015-06-01

A global challenge of increasing concern is diminishing fresh water resources. A growing practice in many communities to supplement diminishing fresh water availability has been the reuse of water. Novel methods of treating polluted waters, such as membrane assisted technologies, have recently been developed and successfully implemented in many places. Given the diversity of membrane assisted technologies available, the current challenge is how to select a reliable alternative among numerous technologies for appropriate water reuse. In this research, a fuzzy logic based multi-criteria, group decision making tool has been developed. This tool has been employed in the selection of appropriate membrane treatment technologies for several non-potable and potable reuse scenarios. Robust criteria, covering technical, environmental, economic and socio-cultural aspects, were selected, while 10 different membrane assisted technologies were assessed in the tool. The results show this approach capable of facilitating systematic and rigorous analysis in the comparison and selection of membrane assisted technologies for advanced wastewater treatment and reuse. Copyright © 2015 Elsevier Ltd. All rights reserved.

From the microscope to the macroscopic: changing from the bench to portfolio management.

PubMed

Sachs, Michael

2017-11-01

A role in portfolio management is ideal for individuals who enjoy tackling challenges that have both technical and business components. Portfolio management provides objective insights and analytics to support research and development decision making and planning. Successful practitioners usually have strong analytical abilities developed from a background in either science or business. Portfolio managers often advise key decision makers at both the team and senior management level and thus require robust oral, written, and interpersonal communication skills. Day-to-day tasks are rarely the same, and comfort with change and the unknown is essential. Here I will discuss my experience as a portfolio manager in a larger biopharmaceutical company and the skills from academic research I leveraged to make the transition. © 2017 Sachs. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Fusion Analytics: A Data Integration System for Public Health and Medical Disaster Response Decision Support

PubMed Central

Passman, Dina B.

2013-01-01

Objective The objective of this demonstration is to show conference attendees how they can integrate, analyze, and visualize diverse data type data from across a variety of systems by leveraging an off-the-shelf enterprise business intelligence (EBI) solution to support decision-making in disasters. Introduction Fusion Analytics is the data integration system developed by the Fusion Cell at the U.S. Department of Health and Human Services (HHS), Office of the Assistant Secretary for Preparedness and Response (ASPR). Fusion Analytics meaningfully augments traditional public and population health surveillance reporting by providing web-based data analysis and visualization tools. Methods Fusion Analytics serves as a one-stop-shop for the web-based data visualizations of multiple real-time data sources within ASPR. The 24-7 web availability makes it an ideal analytic tool for situational awareness and response allowing stakeholders to access the portal from any internet-enabled device without installing any software. The Fusion Analytics data integration system was built using off-the-shelf EBI software. Fusion Analytics leverages the full power of statistical analysis software and delivers reports to users in a secure web-based environment. Fusion Analytics provides an example of how public health staff can develop and deploy a robust public health informatics solution using an off-the shelf product and with limited development funding. It also provides the unique example of a public health information system that combines patient data for traditional disease surveillance with manpower and resource data to provide overall decision support for federal public health and medical disaster response operations. Conclusions We are currently in a unique position within public health. One the one hand, we have been gaining greater and greater access to electronic data of all kinds over the last few years. On the other, we are working in a time of reduced government spending to support leveraging this data for decision support with robust analytics and visualizations. Fusion Analytics provides an opportunity for attendees to see how various types of data are integrated into a single application for population health decision support. It also can provide them with ideas of how they can use their own staff to create analyses and reports that support their public health activities.

Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse.

PubMed

Gowin, Joshua L; Ball, Tali M; Wittmann, Marc; Tapert, Susan F; Paulus, Martin P

2015-07-01

Nearly half of individuals with substance use disorders relapse in the year after treatment. A diagnostic tool to help clinicians make decisions regarding treatment does not exist for psychiatric conditions. Identifying individuals with high risk for relapse to substance use following abstinence has profound clinical consequences. This study aimed to develop neuroimaging as a robust tool to predict relapse. 68 methamphetamine-dependent adults (15 female) were recruited from 28-day inpatient treatment. During treatment, participants completed a functional MRI scan that examined brain activation during reward processing. Patients were followed 1 year later to assess abstinence. We examined brain activation during reward processing between relapsing and abstaining individuals and employed three random forest prediction models (clinical and personality measures, neuroimaging measures, a combined model) to generate predictions for each participant regarding their relapse likelihood. 18 individuals relapsed. There were significant group by reward-size interactions for neural activation in the left insula and right striatum for rewards. Abstaining individuals showed increased activation for large, risky relative to small, safe rewards, whereas relapsing individuals failed to show differential activation between reward types. All three random forest models yielded good test characteristics such that a positive test for relapse yielded a likelihood ratio 2.63, whereas a negative test had a likelihood ratio of 0.48. These findings suggest that neuroimaging can be developed in combination with other measures as an instrument to predict relapse, advancing tools providers can use to make decisions about individualized treatment of substance use disorders. Published by Elsevier Ireland Ltd.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cardenas, Ibsen C., E-mail: c.cardenas@utwente.nl; Halman, Johannes I.M., E-mail: J.I.M.Halman@utwente.nl

Uncertainty is virtually unavoidable in environmental impact assessments (EIAs). From the literature related to treating and managing uncertainty, we have identified specific techniques for coping with uncertainty in EIAs. Here, we have focused on basic steps in the decision-making process that take place within an EIA setting. More specifically, we have identified uncertainties involved in each decision-making step and discussed the extent to which these can be treated and managed in the context of an activity or project that may have environmental impacts. To further demonstrate the relevance of the techniques identified, we have examined the extent to which themore » EIA guidelines currently used in Colombia consider and provide guidance on managing the uncertainty involved in these assessments. Some points that should be considered in order to provide greater robustness in impact assessments in Colombia have been identified. These include the management of stakeholder values, the systematic generation of project options, and their associated impacts as well as the associated management actions, and the evaluation of uncertainties and assumptions. We believe that the relevant and specific techniques reported here can be a reference for future evaluations of other EIA guidelines in different countries. - Highlights: • uncertainty is unavoidable in environmental impact assessments, EIAs; • we have identified some open techniques to EIAs for treating and managing uncertainty in these assessments; • points for improvement that should be considered in order to provide greater robustness in EIAs in Colombia have been identified; • the paper provides substantiated a reference for possible examinations of EIAs guidelines in other countries.« less

Toward accurate and precise estimates of lion density.

PubMed

Elliot, Nicholas B; Gopalaswamy, Arjun M

2017-08-01

Reliable estimates of animal density are fundamental to understanding ecological processes and population dynamics. Furthermore, their accuracy is vital to conservation because wildlife authorities rely on estimates to make decisions. However, it is notoriously difficult to accurately estimate density for wide-ranging carnivores that occur at low densities. In recent years, significant progress has been made in density estimation of Asian carnivores, but the methods have not been widely adapted to African carnivores, such as lions (Panthera leo). Although abundance indices for lions may produce poor inferences, they continue to be used to estimate density and inform management and policy. We used sighting data from a 3-month survey and adapted a Bayesian spatially explicit capture-recapture (SECR) model to estimate spatial lion density in the Maasai Mara National Reserve and surrounding conservancies in Kenya. Our unstructured spatial capture-recapture sampling design incorporated search effort to explicitly estimate detection probability and density on a fine spatial scale, making our approach robust in the context of varying detection probabilities. Overall posterior mean lion density was estimated to be 17.08 (posterior SD 1.310) lions >1 year old/100 km 2 , and the sex ratio was estimated at 2.2 females to 1 male. Our modeling framework and narrow posterior SD demonstrate that SECR methods can produce statistically rigorous and precise estimates of population parameters, and we argue that they should be favored over less reliable abundance indices. Furthermore, our approach is flexible enough to incorporate different data types, which enables robust population estimates over relatively short survey periods in a variety of systems. Trend analyses are essential to guide conservation decisions but are frequently based on surveys of differing reliability. We therefore call for a unified framework to assess lion numbers in key populations to improve management and policy decisions. © 2016 Society for Conservation Biology.

Dynamics of intracellular information decoding.

PubMed

Kobayashi, Tetsuya J; Kamimura, Atsushi

2011-10-01

A variety of cellular functions are robust even to substantial intrinsic and extrinsic noise in intracellular reactions and the environment that could be strong enough to impair or limit them. In particular, of substantial importance is cellular decision-making in which a cell chooses a fate or behavior on the basis of information conveyed in noisy external signals. For robust decoding, the crucial step is filtering out the noise inevitably added during information transmission. As a minimal and optimal implementation of such an information decoding process, the autocatalytic phosphorylation and autocatalytic dephosphorylation (aPadP) cycle was recently proposed. Here, we analyze the dynamical properties of the aPadP cycle in detail. We describe the dynamical roles of the stationary and short-term responses in determining the efficiency of information decoding and clarify the optimality of the threshold value of the stationary response and its information-theoretical meaning. Furthermore, we investigate the robustness of the aPadP cycle against the receptor inactivation time and intrinsic noise. Finally, we discuss the relationship among information decoding with information-dependent actions, bet-hedging and network modularity.

Robust Face Recognition via Multi-Scale Patch-Based Matrix Regression.

PubMed

Gao, Guangwei; Yang, Jian; Jing, Xiaoyuan; Huang, Pu; Hua, Juliang; Yue, Dong

2016-01-01

In many real-world applications such as smart card solutions, law enforcement, surveillance and access control, the limited training sample size is the most fundamental problem. By making use of the low-rank structural information of the reconstructed error image, the so-called nuclear norm-based matrix regression has been demonstrated to be effective for robust face recognition with continuous occlusions. However, the recognition performance of nuclear norm-based matrix regression degrades greatly in the face of the small sample size problem. An alternative solution to tackle this problem is performing matrix regression on each patch and then integrating the outputs from all patches. However, it is difficult to set an optimal patch size across different databases. To fully utilize the complementary information from different patch scales for the final decision, we propose a multi-scale patch-based matrix regression scheme based on which the ensemble of multi-scale outputs can be achieved optimally. Extensive experiments on benchmark face databases validate the effectiveness and robustness of our method, which outperforms several state-of-the-art patch-based face recognition algorithms.

Assessing the performance of a motion tracking system based on optical joint transform correlation

NASA Astrophysics Data System (ADS)

Elbouz, M.; Alfalou, A.; Brosseau, C.; Ben Haj Yahia, N.; Alam, M. S.

2015-08-01

We present an optimized system specially designed for the tracking and recognition of moving subjects in a confined environment (such as an elderly remaining at home). In the first step of our study, we use a VanderLugt correlator (VLC) with an adapted pre-processing treatment of the input plane and a postprocessing of the correlation plane via a nonlinear function allowing us to make a robust decision. The second step is based on an optical joint transform correlation (JTC)-based system (NZ-NL-correlation JTC) for achieving improved detection and tracking of moving persons in a confined space. The proposed system has been found to have significantly superior discrimination and robustness capabilities allowing to detect an unknown target in an input scene and to determine the target's trajectory when this target is in motion. This system offers robust tracking performance of a moving target in several scenarios, such as rotational variation of input faces. Test results obtained using various real life video sequences show that the proposed system is particularly suitable for real-time detection and tracking of moving objects.

Info-Gap Decision Theory for Assessing the Management of Catchments for Timber Production and Urban Water Supply

NASA Astrophysics Data System (ADS)

McCarthy, Michael A.; Lindenmayer, David B.

2007-04-01

While previous studies have examined how forest management is influenced by the risk of fire, they rely on probabilistic estimates of the occurrence and impacts of fire. However, nonprobabilistic approaches are required for assessing the importance of fire risk when data are poor but risks are appreciable. We explore impacts of fire risk on forest management using as a case study a water catchment in the Australian Capital Territory (ACT) (southeastern Australia). In this forested area, urban water supply and timber yields from exotic plantations are potential joint but also competing land uses. Our analyses were stimulated by extensive wildfires in early 2003 that burned much of the existing exotic pine plantation estate in the water catchment and the resulting need to explore the relative economic benefits of revegetating the catchment with exotic plantations or native vegetation. The current mean fire interval in the ACT is approximately 40 years, making the establishment of a pine plantation economically marginal at a 4% discount rate. However, the relative impact on water yield of revegetation with native species and pines is very uncertain, as is the risk of fire under climate change. We use info-gap decision theory to account for these nonprobabilistic sources of uncertainty, demonstrating that the decision that is most robust to uncertainty is highly sensitive to the cost of native revegetation. If costs of native revegetation are sufficiently small, this option is more robust to uncertainty than revegetation with a commercial pine plantation.

Info-gap decision theory for assessing the management of catchments for timber production and urban water supply.

PubMed

McCarthy, Michael A; Lindenmayer, David B

2007-04-01

While previous studies have examined how forest management is influenced by the risk of fire, they rely on probabilistic estimates of the occurrence and impacts of fire. However, nonprobabilistic approaches are required for assessing the importance of fire risk when data are poor but risks are appreciable. We explore impacts of fire risk on forest management using as a case study a water catchment in the Australian Capital Territory (ACT) (southeastern Australia). In this forested area, urban water supply and timber yields from exotic plantations are potential joint but also competing land uses. Our analyses were stimulated by extensive wildfires in early 2003 that burned much of the existing exotic pine plantation estate in the water catchment and the resulting need to explore the relative economic benefits of revegetating the catchment with exotic plantations or native vegetation. The current mean fire interval in the ACT is approximately 40 years, making the establishment of a pine plantation economically marginal at a 4% discount rate. However, the relative impact on water yield of revegetation with native species and pines is very uncertain, as is the risk of fire under climate change. We use info-gap decision theory to account for these nonprobabilistic sources of uncertainty, demonstrating that the decision that is most robust to uncertainty is highly sensitive to the cost of native revegetation. If costs of native revegetation are sufficiently small, this option is more robust to uncertainty than revegetation with a commercial pine plantation.

Positioning Model-Supported, Participatory, Water Management Decision Making under Uncertainty within the Western Philosphical Discourse on Knowledge and Governance

NASA Astrophysics Data System (ADS)

Purkey, D. R.; Escobar, M.; Mehta, V. K.; Forni, L.

2016-12-01

Two important trends currently shape the manner in which water resources planning and decision making occurs. The first relates to the increasing reliance on participatory stakeholder processes as a forum for evaluating water management options and selecting the appropriate course of action. The second relates to the growing recognition that earlier deterministic approaches to this evaluation of options may no longer be appropriate, nor required. The convergence of these two trends poses questions as to the proper role of data, information, analysis and expertise in the inherently social and political process of negotiating water resources management agreements and implementing water resources management interventions. The question of how to discover the best or optimal option in the face of deep uncertainty related to climate change, demography, economic development, and regulatory reform is compelling. More fundamentally the question of whether the "perfect" option even exits to be discovered is perhaps more critical. While this existential question may be new to the water resource management community, it is not new to western political theory. This paper explores early classical philosophical writing related to issues of knowledge and governance as captured in the work of Plato and Aristotle; and then attempts to place a new approach to analysis-supported, stakeholder-driven water resources planning and decision making within this philosophical discourse. Using examples from river systems in California and the Andes, where the theory of Robust Decision Making has been used as an organizing construct for stakeholder processes, it is argued that the expectation that analysis will lead to the discovery of the perfect option is not warranted when stakeholders are engaged in the process of discovering a consensus option. This argument will touch upon issue of the diversity of values, model uncertainty and creditability, and the visualization of model output required to explore the implications of various management options across a range of inherently unknowable future conditions.

Consensus-Based Cooperative Spectrum Sensing with Improved Robustness Against SSDF Attacks

NASA Astrophysics Data System (ADS)

Liu, Quan; Gao, Jun; Guo, Yunwei; Liu, Siyang

2011-05-01

Based on the consensus algorithm, an attack-proof cooperative spectrum sensing (CSS) scheme is presented for decentralized cognitive radio networks (CRNs), where a common fusion center is not available and some malicious users may launch attacks with spectrum sensing data falsification (SSDF). Local energy detection is firstly performed by each secondary user (SU), and then, utilizing the consensus notions, each SU can make its own decision individually only by local information exchange with its neighbors rather than any centralized fusion used in most existing schemes. With the help of some anti-attack tricks, each authentic SU can generally identify and exclude those malicious reports during the interactions within the neighborhood. Compared with the existing solutions, the proposed scheme is proved to have much better robustness against three categories of SSDF attack, without requiring any a priori knowledge of the whole network.

Robust Approach for Nonuniformity Correction in Infrared Focal Plane Array.

PubMed

Boutemedjet, Ayoub; Deng, Chenwei; Zhao, Baojun

2016-11-10

In this paper, we propose a new scene-based nonuniformity correction technique for infrared focal plane arrays. Our work is based on the use of two well-known scene-based methods, namely, adaptive and interframe registration-based exploiting pure translation motion model between frames. The two approaches have their benefits and drawbacks, which make them extremely effective in certain conditions and not adapted for others. Following on that, we developed a method robust to various conditions, which may slow or affect the correction process by elaborating a decision criterion that adapts the process to the most effective technique to ensure fast and reliable correction. In addition to that, problems such as bad pixels and ghosting artifacts are also dealt with to enhance the overall quality of the correction. The performance of the proposed technique is investigated and compared to the two state-of-the-art techniques cited above.

Robust Approach for Nonuniformity Correction in Infrared Focal Plane Array

PubMed Central

Boutemedjet, Ayoub; Deng, Chenwei; Zhao, Baojun

2016-01-01

In this paper, we propose a new scene-based nonuniformity correction technique for infrared focal plane arrays. Our work is based on the use of two well-known scene-based methods, namely, adaptive and interframe registration-based exploiting pure translation motion model between frames. The two approaches have their benefits and drawbacks, which make them extremely effective in certain conditions and not adapted for others. Following on that, we developed a method robust to various conditions, which may slow or affect the correction process by elaborating a decision criterion that adapts the process to the most effective technique to ensure fast and reliable correction. In addition to that, problems such as bad pixels and ghosting artifacts are also dealt with to enhance the overall quality of the correction. The performance of the proposed technique is investigated and compared to the two state-of-the-art techniques cited above. PMID:27834893

Echo

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harvey, Dustin Yewell

This document is a white paper marketing proposal for Echo™ is a data analysis platform designed for efficient, robust, and scalable creation and execution of complex workflows. Echo’s analysis management system refers to the ability to track, understand, and reproduce workflows used for arriving at results and decisions. Echo improves on traditional scripted data analysis in MATLAB, Python, R, and other languages to allow analysts to make better use of their time. Additionally, the Echo platform provides a powerful data management and curation solution allowing analysts to quickly find, access, and consume datasets. After two years of development and amore » first release in early 2016, Echo is now available for use with many data types in a wide range of application domains. Echo provides tools that allow users to focus on data analysis and decisions with confidence that results are reported accurately.« less

If We Know So Much, Why Do We Make Such Crummy Decisions?

NASA Astrophysics Data System (ADS)

Davis, M.

2017-12-01

If We Know So Much, Why Do We Make Such Crummy Decisions?Mark Davis Senior Research Fellow Director, Tulane Institute on Water Resources Law and Policy Director, ByWater Institute at Tulane University A Presentation to American Geophysical Union Translational Hydrology: Moving from Science to Decisions Panel December 2017 For much of human history, water management and science have not been on a first name basis. Waters have been dammed, diverted, pumped and filled for many reasons, occasionally even in ways that reflected a deep respect for hydrology and even less occasionally for ecology. But the simple fact is that water management decisions have long been rooted in utilitarian relations to power and property and not in science-and for the most part still are. Over the past 50 years or state and federal laws have grafted science driven concerns into a wide range of governmental decisions through acts like the National Environmental Policy Act, the Endangered Species Act, the Clean Water Act and others. As a result, science influences decisions but does not compel them; for the most part, there is no actual pathway from science to decisions. Science is not its own advocate and even the best information does not compel action without help from factors rooted in law, policy, property, and power. Understanding this basic fact and the architecture of decision-making for a given action are key to ensuring that the connections between science and decisions are robust. An example of this can be seen in the plans to counter sea level rise and coastal land loss in Louisiana. Extensive plans with a strong science and engineering foundation have been drawn that call for a complex system of public works projects that would essentially reconnect the coast with the rivers that built and nurtured it. What those plans do not do is say how those projects would be authorized or financed. They do not say what rights they have to redirect waters or the even how what rights the State has to those waters. And they do not say how competing right to use and access water will be reconciled. Without those things even the best plan is more a prayer than a prelude to action.

Beyond optimality: Multistakeholder robustness tradeoffs for regional water portfolio planning under deep uncertainty

NASA Astrophysics Data System (ADS)

Herman, Jonathan D.; Zeff, Harrison B.; Reed, Patrick M.; Characklis, Gregory W.

2014-10-01

While optimality is a foundational mathematical concept in water resources planning and management, "optimal" solutions may be vulnerable to failure if deeply uncertain future conditions deviate from those assumed during optimization. These vulnerabilities may produce severely asymmetric impacts across a region, making it vital to evaluate the robustness of management strategies as well as their impacts for regional stakeholders. In this study, we contribute a multistakeholder many-objective robust decision making (MORDM) framework that blends many-objective search and uncertainty analysis tools to discover key tradeoffs between water supply alternatives and their robustness to deep uncertainties (e.g., population pressures, climate change, and financial risks). The proposed framework is demonstrated for four interconnected water utilities representing major stakeholders in the "Research Triangle" region of North Carolina, U.S. The utilities supply well over one million customers and have the ability to collectively manage drought via transfer agreements and shared infrastructure. We show that water portfolios for this region that compose optimal tradeoffs (i.e., Pareto-approximate solutions) under expected future conditions may suffer significantly degraded performance with only modest changes in deeply uncertain hydrologic and economic factors. We then use the Patient Rule Induction Method (PRIM) to identify which uncertain factors drive the individual and collective vulnerabilities for the four cooperating utilities. Our framework identifies key stakeholder dependencies and robustness tradeoffs associated with cooperative regional planning, which are critical to understanding the tensions between individual versus regional water supply goals. Cooperative demand management was found to be the key factor controlling the robustness of regional water supply planning, dominating other hydroclimatic and economic uncertainties through the 2025 planning horizon. Results suggest that a modest reduction in the projected rate of demand growth (from approximately 3% per year to 2.4%) will substantially improve the utilities' robustness to future uncertainty and reduce the potential for regional tensions. The proposed multistakeholder MORDM framework offers critical insights into the risks and challenges posed by rising water demands and hydrological uncertainties, providing a planning template for regions now forced to confront rapidly evolving water scarcity risks.

The physics of bacterial decision making.

PubMed

Ben-Jacob, Eshel; Lu, Mingyang; Schultz, Daniel; Onuchic, Jose' N

2014-01-01

The choice that bacteria make between sporulation and competence when subjected to stress provides a prototypical example of collective cell fate determination that is stochastic on the individual cell level, yet predictable (deterministic) on the population level. This collective decision is performed by an elaborated gene network. Considerable effort has been devoted to simplify its complexity by taking physics approaches to untangle the basic functional modules that are integrated to form the complete network: (1) A stochastic switch whose transition probability is controlled by two order parameters-population density and internal/external stress. (2) An adaptable timer whose clock rate is normalized by the same two previous order parameters. (3) Sensing units which measure population density and external stress. (4) A communication module that exchanges information about the cells' internal stress levels. (5) An oscillating gate of the stochastic switch which is regulated by the timer. The unique circuit architecture of the gate allows special dynamics and noise management features. The gate opens a window of opportunity in time for competence transitions, during which the circuit generates oscillations that are translated into a chain of short intervals with high transition probability. In addition, the unique architecture of the gate allows filtering of external noise and robustness against variations in circuit parameters and internal noise. We illustrate that a physics approach can be very valuable in investigating the decision process and in identifying its general principles. We also show that both cell-cell variability and noise have important functional roles in the collectively controlled individual decisions.

The physics of bacterial decision making

PubMed Central

Ben-Jacob, Eshel; Lu, Mingyang; Schultz, Daniel; Onuchic, Jose' N.

2014-01-01

The choice that bacteria make between sporulation and competence when subjected to stress provides a prototypical example of collective cell fate determination that is stochastic on the individual cell level, yet predictable (deterministic) on the population level. This collective decision is performed by an elaborated gene network. Considerable effort has been devoted to simplify its complexity by taking physics approaches to untangle the basic functional modules that are integrated to form the complete network: (1) A stochastic switch whose transition probability is controlled by two order parameters—population density and internal/external stress. (2) An adaptable timer whose clock rate is normalized by the same two previous order parameters. (3) Sensing units which measure population density and external stress. (4) A communication module that exchanges information about the cells' internal stress levels. (5) An oscillating gate of the stochastic switch which is regulated by the timer. The unique circuit architecture of the gate allows special dynamics and noise management features. The gate opens a window of opportunity in time for competence transitions, during which the circuit generates oscillations that are translated into a chain of short intervals with high transition probability. In addition, the unique architecture of the gate allows filtering of external noise and robustness against variations in circuit parameters and internal noise. We illustrate that a physics approach can be very valuable in investigating the decision process and in identifying its general principles. We also show that both cell-cell variability and noise have important functional roles in the collectively controlled individual decisions. PMID:25401094

Hormonal Signal Amplification Mediates Environmental Conditions during Development and Controls an Irreversible Commitment to Adulthood

PubMed Central

Schaedel, Oren N.; Gerisch, Birgit; Antebi, Adam; Sternberg, Paul W.

2012-01-01

Many animals can choose between different developmental fates to maximize fitness. Despite the complexity of environmental cues and life history, different developmental fates are executed in a robust fashion. The nematode Caenorhabditis elegans serves as a powerful model to examine this phenomenon because it can adopt one of two developmental fates (adulthood or diapause) depending on environmental conditions. The steroid hormone dafachronic acid (DA) directs development to adulthood by regulating the transcriptional activity of the nuclear hormone receptor DAF-12. The known role of DA suggests that it may be the molecular mediator of environmental condition effects on the developmental fate decision, although the mechanism is yet unknown. We used a combination of physiological and molecular biology techniques to demonstrate that commitment to reproductive adult development occurs when DA levels, produced in the neuroendocrine XXX cells, exceed a threshold. Furthermore, imaging and cell ablation experiments demonstrate that the XXX cells act as a source of DA, which, upon commitment to adult development, is amplified and propagated in the epidermis in a DAF-12 dependent manner. This positive feedback loop increases DA levels and drives adult programs in the gonad and epidermis, thus conferring the irreversibility of the decision. We show that the positive feedback loop canalizes development by ensuring that sufficient amounts of DA are dispersed throughout the body and serves as a robust fate-locking mechanism to enforce an organism-wide binary decision, despite noisy and complex environmental cues. These mechanisms are not only relevant to C. elegans but may be extended to other hormonal-based decision-making mechanisms in insects and mammals. PMID:22505848

The determinants of response time in a repeated constant-sum game: A robust Bayesian hierarchical dual-process model.

PubMed

Spiliopoulos, Leonidas

2018-03-01

The investigation of response time and behavior has a long tradition in cognitive psychology, particularly for non-strategic decision-making. Recently, experimental economists have also studied response time in strategic interactions, but with an emphasis on either one-shot games or repeated social-dilemmas. I investigate the determinants of response time in a repeated (pure-conflict) game, admitting a unique mixed strategy Nash equilibrium, with fixed partner matching. Response times depend upon the interaction of two decision models embedded in a dual-process framework (Achtziger and Alós-Ferrer, 2014; Alós-Ferrer, 2016). The first decision model is the commonly used win-stay/lose-shift heuristic and the second the pattern-detecting reinforcement learning model in Spiliopoulos (2013b). The former is less complex and can be executed more quickly than the latter. As predicted, conflict between these two models (i.e., each one recommending a different course of action) led to longer response times than cases without conflict. The dual-process framework makes other qualitative response time predictions arising from the interaction between the existence (or not) of conflict and which one of the two decision models the chosen action is consistent with-these were broadly verified by the data. Other determinants of RT were hypothesized on the basis of existing theory and tested empirically. Response times were strongly dependent on the actions chosen by both players in the previous rounds and the resulting outcomes. Specifically, response time was shortest after a win in the previous round where the maximum possible payoff was obtained; response time after losses was significantly longer. Strongly auto-correlated behavior (regardless of its sign) was also associated with longer response times. I conclude that, similar to other tasks, there is a strong coupling in repeated games between behavior and RT, which can be exploited to further our understanding of decision making. Copyright © 2017 Elsevier B.V. All rights reserved.

Simulation modelling as a tool for knowledge mobilisation in health policy settings: a case study protocol.

PubMed

Freebairn, L; Atkinson, J; Kelly, P; McDonnell, G; Rychetnik, L

2016-09-21

Evidence-informed decision-making is essential to ensure that health programs and services are effective and offer value for money; however, barriers to the use of evidence persist. Emerging systems science approaches and advances in technology are providing new methods and tools to facilitate evidence-based decision-making. Simulation modelling offers a unique tool for synthesising and leveraging existing evidence, data and expert local knowledge to examine, in a robust, low risk and low cost way, the likely impact of alternative policy and service provision scenarios. This case study will evaluate participatory simulation modelling to inform the prevention and management of gestational diabetes mellitus (GDM). The risks associated with GDM are well recognised; however, debate remains regarding diagnostic thresholds and whether screening and treatment to reduce maternal glucose levels reduce the associated risks. A diagnosis of GDM may provide a leverage point for multidisciplinary lifestyle modification interventions. This research will apply and evaluate a simulation modelling approach to understand the complex interrelation of factors that drive GDM rates, test options for screening and interventions, and optimise the use of evidence to inform policy and program decision-making. The study design will use mixed methods to achieve the objectives. Policy, clinical practice and research experts will work collaboratively to develop, test and validate a simulation model of GDM in the Australian Capital Territory (ACT). The model will be applied to support evidence-informed policy dialogues with diverse stakeholders for the management of GDM in the ACT. Qualitative methods will be used to evaluate simulation modelling as an evidence synthesis tool to support evidence-based decision-making. Interviews and analysis of workshop recordings will focus on the participants' engagement in the modelling process; perceived value of the participatory process, perceived commitment, influence and confidence of stakeholders in implementing policy and program decisions identified in the modelling process; and the impact of the process in terms of policy and program change. The study will generate empirical evidence on the feasibility and potential value of simulation modelling to support knowledge mobilisation and consensus building in health settings.

Multi-criteria analysis for municipal solid waste management in a Brazilian metropolitan area.

PubMed

Santos, Simone Machado; Silva, Maisa Mendonça; Melo, Renata Maciel; Gavazza, Savia; Florencio, Lourdinha; Kato, Mario T

2017-10-15

The decision-making process involved in municipal solid waste management (MSWM) must consider more than just financial aspects, which makes it a difficult task in developing countries. The Recife Metropolitan Region (RMR) in the Northeast of Brazil faces a MSWM problem that has been ongoing since the 1970s, with no common solution. In order to direct short-term solutions, three MSWM alternatives were outlined for the RMR, considering the current and future situations, the time and cost involved and social/environmental criteria. A multi-criteria approach, based on the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), was proposed to rank these alternatives. The alternative that included two private landfill sites and seven transfer, sorting and composting stations was confirmed as the most suitable and stable option for short-term MSWM, considering the two scenarios for the criteria weights. Sensitivity analysis was also performed to support the robustness of the results. The implementation of separate collections would minimize the amount of waste buried, while maximizing the useful life of landfill sites and increasing the timeframe of the alternative. Overall, the multi-criteria analysis was helpful and accurate during the alternative selection process, considering the similarities and restrictions of each option, which can lead to difficulties during the decision-making process.

Robust Economic Control Decision Method of Uncertain System on Urban Domestic Water Supply.

PubMed

Li, Kebai; Ma, Tianyi; Wei, Guo

2018-03-31

As China quickly urbanizes, urban domestic water generally presents the circumstances of both rising tendency and seasonal cycle fluctuation. A robust economic control decision method for dynamic uncertain systems is proposed in this paper. It is developed based on the internal model principle and pole allocation method, and it is applied to an urban domestic water supply system with rising tendency and seasonal cycle fluctuation. To achieve this goal, first a multiplicative model is used to describe the urban domestic water demand. Then, a capital stock and a labor stock are selected as the state vector, and the investment and labor are designed as the control vector. Next, the compensator subsystem is devised in light of the internal model principle. Finally, by using the state feedback control strategy and pole allocation method, the multivariable robust economic control decision method is implemented. The implementation with this model can accomplish the urban domestic water supply control goal, with the robustness for the variation of parameters. The methodology presented in this study may be applied to the water management system in other parts of the world, provided all data used in this study are available. The robust control decision method in this paper is also applicable to deal with tracking control problems as well as stabilization control problems of other general dynamic uncertain systems.

Robust Economic Control Decision Method of Uncertain System on Urban Domestic Water Supply

PubMed Central

Li, Kebai; Ma, Tianyi; Wei, Guo

2018-01-01

As China quickly urbanizes, urban domestic water generally presents the circumstances of both rising tendency and seasonal cycle fluctuation. A robust economic control decision method for dynamic uncertain systems is proposed in this paper. It is developed based on the internal model principle and pole allocation method, and it is applied to an urban domestic water supply system with rising tendency and seasonal cycle fluctuation. To achieve this goal, first a multiplicative model is used to describe the urban domestic water demand. Then, a capital stock and a labor stock are selected as the state vector, and the investment and labor are designed as the control vector. Next, the compensator subsystem is devised in light of the internal model principle. Finally, by using the state feedback control strategy and pole allocation method, the multivariable robust economic control decision method is implemented. The implementation with this model can accomplish the urban domestic water supply control goal, with the robustness for the variation of parameters. The methodology presented in this study may be applied to the water management system in other parts of the world, provided all data used in this study are available. The robust control decision method in this paper is also applicable to deal with tracking control problems as well as stabilization control problems of other general dynamic uncertain systems. PMID:29614749

Reducing regional drought vulnerabilities and multi-city robustness conflicts using many-objective optimization under deep uncertainty

NASA Astrophysics Data System (ADS)

Trindade, B. C.; Reed, P. M.; Herman, J. D.; Zeff, H. B.; Characklis, G. W.

2017-06-01

Emerging water scarcity concerns in many urban regions are associated with several deeply uncertain factors, including rapid population growth, limited coordination across adjacent municipalities and the increasing risks for sustained regional droughts. Managing these uncertainties will require that regional water utilities identify coordinated, scarcity-mitigating strategies that trigger the appropriate actions needed to avoid water shortages and financial instabilities. This research focuses on the Research Triangle area of North Carolina, seeking to engage the water utilities within Raleigh, Durham, Cary and Chapel Hill in cooperative and robust regional water portfolio planning. Prior analysis of this region through the year 2025 has identified significant regional vulnerabilities to volumetric shortfalls and financial losses. Moreover, efforts to maximize the individual robustness of any of the mentioned utilities also have the potential to strongly degrade the robustness of the others. This research advances a multi-stakeholder Many-Objective Robust Decision Making (MORDM) framework to better account for deeply uncertain factors when identifying cooperative drought management strategies. Our results show that appropriately designing adaptive risk-of-failure action triggers required stressing them with a comprehensive sample of deeply uncertain factors in the computational search phase of MORDM. Search under the new ensemble of states-of-the-world is shown to fundamentally change perceived performance tradeoffs and substantially improve the robustness of individual utilities as well as the overall region to water scarcity. Search under deep uncertainty enhanced the discovery of how cooperative water transfers, financial risk mitigation tools, and coordinated regional demand management must be employed jointly to improve regional robustness and decrease robustness conflicts between the utilities. Insights from this work have general merit for regions where adjacent municipalities can benefit from cooperative regional water portfolio planning.

Evolution with Reinforcement Learning in Negotiation

PubMed Central

Zou, Yi; Zhan, Wenjie; Shao, Yuan

2014-01-01

Adaptive behavior depends less on the details of the negotiation process and makes more robust predictions in the long term as compared to in the short term. However, the extant literature on population dynamics for behavior adjustment has only examined the current situation. To offset this limitation, we propose a synergy of evolutionary algorithm and reinforcement learning to investigate long-term collective performance and strategy evolution. The model adopts reinforcement learning with a tradeoff between historical and current information to make decisions when the strategies of agents evolve through repeated interactions. The results demonstrate that the strategies in populations converge to stable states, and the agents gradually form steady negotiation habits. Agents that adopt reinforcement learning perform better in payoff, fairness, and stableness than their counterparts using classic evolutionary algorithm. PMID:25048108

Evolution with reinforcement learning in negotiation.

PubMed

Zou, Yi; Zhan, Wenjie; Shao, Yuan

2014-01-01

Adaptive behavior depends less on the details of the negotiation process and makes more robust predictions in the long term as compared to in the short term. However, the extant literature on population dynamics for behavior adjustment has only examined the current situation. To offset this limitation, we propose a synergy of evolutionary algorithm and reinforcement learning to investigate long-term collective performance and strategy evolution. The model adopts reinforcement learning with a tradeoff between historical and current information to make decisions when the strategies of agents evolve through repeated interactions. The results demonstrate that the strategies in populations converge to stable states, and the agents gradually form steady negotiation habits. Agents that adopt reinforcement learning perform better in payoff, fairness, and stableness than their counterparts using classic evolutionary algorithm.

Adapting to extreme events related to natural variability and climate change: the imperative of coupling technology with strong regulation and governance.

PubMed

Kythreotis, A P; Mercer, T G; Frostick, L E

2013-09-03

In recent years there has been an increase in extreme events related to natural variability (such as earthquakes, tsunamis and hurricanes) and climate change (such as flooding and more extreme weather). Developing innovative technologies is crucial in making society more resilient to such events. However, little emphasis has been placed on the role of human decision-making in maximizing the positive impacts of technological developments. This is exacerbated by the lack of appropriate adaptation options and the privatization of existing infrastructure, which can leave people exposed to increasing risk. This work examines the need for more robust government regulation and legislation to complement developments and innovations in technology in order to protect communities against such extreme events.

Sensitivity Analysis in Sequential Decision Models.

PubMed

Chen, Qiushi; Ayer, Turgay; Chhatwal, Jagpreet

2017-02-01

Sequential decision problems are frequently encountered in medical decision making, which are commonly solved using Markov decision processes (MDPs). Modeling guidelines recommend conducting sensitivity analyses in decision-analytic models to assess the robustness of the model results against the uncertainty in model parameters. However, standard methods of conducting sensitivity analyses cannot be directly applied to sequential decision problems because this would require evaluating all possible decision sequences, typically in the order of trillions, which is not practically feasible. As a result, most MDP-based modeling studies do not examine confidence in their recommended policies. In this study, we provide an approach to estimate uncertainty and confidence in the results of sequential decision models. First, we provide a probabilistic univariate method to identify the most sensitive parameters in MDPs. Second, we present a probabilistic multivariate approach to estimate the overall confidence in the recommended optimal policy considering joint uncertainty in the model parameters. We provide a graphical representation, which we call a policy acceptability curve, to summarize the confidence in the optimal policy by incorporating stakeholders' willingness to accept the base case policy. For a cost-effectiveness analysis, we provide an approach to construct a cost-effectiveness acceptability frontier, which shows the most cost-effective policy as well as the confidence in that for a given willingness to pay threshold. We demonstrate our approach using a simple MDP case study. We developed a method to conduct sensitivity analysis in sequential decision models, which could increase the credibility of these models among stakeholders.

Reframing measurement for structural health monitoring: a full-field strategy for structural identification

NASA Astrophysics Data System (ADS)

Dizaji, Mehrdad S.; Harris, Devin K.; Alipour, Mohamad; Ozbulut, Osman E.

2018-03-01

Structural health monitoring (SHM) describes a decision-making framework that is fundamentally guided by state change detection of structural systems. This framework typically relies on the use of continuous or semi-continuous monitoring of measured response to quantify this state change in structural system behavior, which is often related to the initiation of some form of damage. Measurement approaches used for traditional SHM are numerous, but most are limited to either describing localized or global phenomena, making it challenging to characterize operational structural systems which exhibit both. In addition to these limitations in sensing, SHM has also suffered from the inherent robustness inherent to most full-scale structural systems, making it challenging to identify local damage. These challenges highlight the opportunity for alternative strategies for SHM, strategies that are able to provide data suitable to translate into rich information. This paper describes preliminary results from a refined structural identification (St-ID) approach using fullfield measurements derived from high-speed 3D Digital Image Correlation (HSDIC) to characterize uncertain parameters (i.e. boundary and constitutive properties) of a laboratory scale structural component. The St-ID approach builds from prior work by supplementing full-field deflection and strain response with vibration response derived from HSDIC. Inclusion of the modal characteristics within a hybrid-genetic algorithm optimization scheme allowed for simultaneous integration of mechanical and modal response, thus enabling a more robust St-ID strategy than could be achieved with traditional sensing techniques. The use of full-field data is shown to provide a more comprehensive representation of the global and local behavior, which in turn increases the robustness of the St-Id framework. This work serves as the foundation for a new paradigm in SHM that emphasizes characterizing structural performance using a smaller number, but richer set of measurements.

How to make loss aversion disappear and reverse: tests of the decision by sampling origin of loss aversion.

PubMed

Walasek, Lukasz; Stewart, Neil

2015-02-01

One of the most robust empirical findings in the behavioral sciences is loss aversion--the finding that losses loom larger than gains. We offer a new psychological explanation of the origins of loss aversion in which loss aversion emerges from differences in the distribution of gains and losses people experience. In 4 experiments, we tested this proposition by manipulating the range of gains and losses that individuals saw during the process of eliciting their loss aversion. We were able to find loss aversion, loss neutrality, and even the reverse of loss aversion.

Intelligent flight control systems

NASA Technical Reports Server (NTRS)

Stengel, Robert F.

1993-01-01

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms.

DARHT Multi-intelligence Seismic and Acoustic Data Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stevens, Garrison Nicole; Van Buren, Kendra Lu; Hemez, Francois M.

The purpose of this report is to document the analysis of seismic and acoustic data collected at the Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility at Los Alamos National Laboratory for robust, multi-intelligence decision making. The data utilized herein is obtained from two tri-axial seismic sensors and three acoustic sensors, resulting in a total of nine data channels. The goal of this analysis is to develop a generalized, automated framework to determine internal operations at DARHT using informative features extracted from measurements collected external of the facility. Our framework involves four components: (1) feature extraction, (2) data fusion, (3) classification, andmore » finally (4) robustness analysis. Two approaches are taken for extracting features from the data. The first of these, generic feature extraction, involves extraction of statistical features from the nine data channels. The second approach, event detection, identifies specific events relevant to traffic entering and leaving the facility as well as explosive activities at DARHT and nearby explosive testing sites. Event detection is completed using a two stage method, first utilizing signatures in the frequency domain to identify outliers and second extracting short duration events of interest among these outliers by evaluating residuals of an autoregressive exogenous time series model. Features extracted from each data set are then fused to perform analysis with a multi-intelligence paradigm, where information from multiple data sets are combined to generate more information than available through analysis of each independently. The fused feature set is used to train a statistical classifier and predict the state of operations to inform a decision maker. We demonstrate this classification using both generic statistical features and event detection and provide a comparison of the two methods. Finally, the concept of decision robustness is presented through a preliminary analysis where uncertainty is added to the system through noise in the measurements.« less

Future Tense and Economic Decisions: Controlling for Cultural Evolution

PubMed Central

Roberts, Seán G.; Winters, James; Chen, Keith

2015-01-01

A previous study by Chen demonstrates a correlation between languages that grammatically mark future events and their speakers' propensity to save, even after controlling for numerous economic and demographic factors. The implication is that languages which grammatically distinguish the present and the future may bias their speakers to distinguish them psychologically, leading to less future-oriented decision making. However, Chen's original analysis assumed languages are independent. This neglects the fact that languages are related, causing correlations to appear stronger than is warranted (Galton's problem). In this paper, we test the robustness of Chen's correlations to corrections for the geographic and historical relatedness of languages. While the question seems simple, the answer is complex. In general, the statistical correlation between the two variables is weaker when controlling for relatedness. When applying the strictest tests for relatedness, and when data is not aggregated across individuals, the correlation is not significant. However, the correlation did remain reasonably robust under a number of tests. We argue that any claims of synchronic patterns between cultural variables should be tested for spurious correlations, with the kinds of approaches used in this paper. However, experiments or case-studies would be more fruitful avenues for future research on this specific topic, rather than further large-scale cross-cultural correlational studies. PMID:26186527

Future Tense and Economic Decisions: Controlling for Cultural Evolution.

PubMed

Roberts, Seán G; Winters, James; Chen, Keith

2015-01-01

A previous study by Chen demonstrates a correlation between languages that grammatically mark future events and their speakers' propensity to save, even after controlling for numerous economic and demographic factors. The implication is that languages which grammatically distinguish the present and the future may bias their speakers to distinguish them psychologically, leading to less future-oriented decision making. However, Chen's original analysis assumed languages are independent. This neglects the fact that languages are related, causing correlations to appear stronger than is warranted (Galton's problem). In this paper, we test the robustness of Chen's correlations to corrections for the geographic and historical relatedness of languages. While the question seems simple, the answer is complex. In general, the statistical correlation between the two variables is weaker when controlling for relatedness. When applying the strictest tests for relatedness, and when data is not aggregated across individuals, the correlation is not significant. However, the correlation did remain reasonably robust under a number of tests. We argue that any claims of synchronic patterns between cultural variables should be tested for spurious correlations, with the kinds of approaches used in this paper. However, experiments or case-studies would be more fruitful avenues for future research on this specific topic, rather than further large-scale cross-cultural correlational studies.

Factors associated with institutional delivery in Ghana: the role of decision-making autonomy and community norms.

PubMed

Speizer, Ilene S; Story, William T; Singh, Kavita

2014-11-27

In Ghana, the site of this study, the maternal mortality ratio and under-five mortality rate remain high indicating the need to focus on maternal and child health programming. Ghana has high use of antenatal care (95%) but sub-optimum levels of institutional delivery (about 57%). Numerous barriers to institutional delivery exist including financial, physical, cognitive, organizational, and psychological and social. This study examines the psychological and social barriers to institutional delivery, namely women's decision-making autonomy and their perceptions about social support for institutional delivery in their community. This study uses cross-sectional data collected for the evaluation of the Maternal and Newborn Referrals Project of Project Fives Alive in Northern and Central districts of Ghana. In 2012 and 2013, a total of 2,527 women aged 15 to 49 were surveyed at baseline and midterm (half in 2012 and half in 2013). The analysis sample of 1,606 includes all women who had a birth three years prior to the survey date and who had no missing data. To determine the relationship between institutional delivery and the two key social barriers-women's decision-making autonomy and community perceptions of institutional delivery-we used multi-level logistic regression models, including cross-level interactions between community-level attitudes and individual-level autonomy. All analyses control for the clustered survey design by including robust standard errors in Stata 13 statistical software. The findings show that women who are more autonomous and who perceive positive attitudes toward facility delivery (among women, men and mothers-in-law) were more likely to deliver in a facility. Moreover, the interactions between autonomy and community-level perceptions of institutional delivery among men and mothers-in-law were significant, such that the effect of decision-making autonomy is more important for women who live in communities that are less supportive of institutional delivery compared to communities that are more supportive. This study builds upon prior work by using indicators that provide a more direct assessment of perceived community norms and women's decision-making autonomy. The findings lead to programmatic recommendations that go beyond individuals and engaging the broader network of people (husbands and mothers-in-law) that influence delivery behaviors.

Development and testing of study tools and methods to examine ethnic bias and clinical decision-making among medical students in New Zealand: The Bias and Decision-Making in Medicine (BDMM) study.

PubMed

Harris, Ricci; Cormack, Donna; Curtis, Elana; Jones, Rhys; Stanley, James; Lacey, Cameron

2016-07-11

Health provider racial/ethnic bias and its relationship to clinical decision-making is an emerging area of research focus in understanding and addressing ethnic health inequities. Examining potential racial/ethnic bias among medical students may provide important information to inform medical education and training. This paper describes the development, pretesting and piloting of study content, tools and processes for an online study of racial/ethnic bias (comparing Māori and New Zealand European) and clinical decision-making among final year medical students in New Zealand (NZ). The study was developed, pretested and piloted using a staged process (eight stages within five phases). Phase 1 included three stages: 1) scoping and conceptual framework development; 2) literature review and identification of potential measures and items; and, 3) development and adaptation of study content. Three main components were identified to assess different aspects of racial/ethnic bias: (1) implicit racial/ethnic bias using NZ-specific Implicit Association Tests (IATs); (2) explicit racial/ethnic bias using direct questions; and, (3) clinical decision-making, using chronic disease vignettes. Phase 2 (stage 4) comprised expert review and refinement. Formal pretesting (Phase 3) included construct testing using sorting and rating tasks (stage 5) and cognitive interviewing (stage 6). Phase 4 (stage 7) involved content revision and building of the web-based study, followed by pilot testing in Phase 5 (stage 8). Materials identified for potential inclusion performed well in construct testing among six participants. This assisted in the prioritisation and selection of measures that worked best in the New Zealand context and aligned with constructs of interest. Findings from the cognitive interviewing (nine participants) on the clarity, meaning, and acceptability of measures led to changes in the final wording of items and ordering of questions. Piloting (18 participants) confirmed the overall functionality of the web-based questionnaire, with a few minor revisions made to the final study. Robust processes are required in the development of study content to assess racial/ethnic bias in order to optimise the validity of specific measures, ensure acceptability and minimise potential problems. This paper has utility for other researchers in this area by informing potential development approaches and identifying possible measurement tools.

Improving decision making in crisis.

PubMed

Higgins, Guy; Freedman, Jennifer

2013-01-01

The most critical activity during emergencies or crises is making decisions about what to do next. This paper provides insights into the challenges that people face in making decisions at any time, but particularly during emergencies and crises. It also introduces the reader to the concept of different sense-making/decision-making domains, the human behaviours that can adversely affect decision making - decision derailers - and ways in which emergency responders can leverage this knowledge to make better decisions. While the literature on decision making is extensive, this paper is focused on those aspects that apply particularly to decision making in emergencies or times of crisis.

Multiscale Region-Level VHR Image Change Detection via Sparse Change Descriptor and Robust Discriminative Dictionary Learning

PubMed Central

Xu, Yuan; Ding, Kun; Huo, Chunlei; Zhong, Zisha; Li, Haichang; Pan, Chunhong

2015-01-01

Very high resolution (VHR) image change detection is challenging due to the low discriminative ability of change feature and the difficulty of change decision in utilizing the multilevel contextual information. Most change feature extraction techniques put emphasis on the change degree description (i.e., in what degree the changes have happened), while they ignore the change pattern description (i.e., how the changes changed), which is of equal importance in characterizing the change signatures. Moreover, the simultaneous consideration of the classification robust to the registration noise and the multiscale region-consistent fusion is often neglected in change decision. To overcome such drawbacks, in this paper, a novel VHR image change detection method is proposed based on sparse change descriptor and robust discriminative dictionary learning. Sparse change descriptor combines the change degree component and the change pattern component, which are encoded by the sparse representation error and the morphological profile feature, respectively. Robust change decision is conducted by multiscale region-consistent fusion, which is implemented by the superpixel-level cosparse representation with robust discriminative dictionary and the conditional random field model. Experimental results confirm the effectiveness of the proposed change detection technique. PMID:25918748

Decision-making in nursing practice: An integrative literature review.

PubMed

Nibbelink, Christine W; Brewer, Barbara B

2018-03-01

To identify and summarise factors and processes related to registered nurses' patient care decision-making in medical-surgical environments. A secondary goal of this literature review was to determine whether medical-surgical decision-making literature included factors that appeared to be similar to concepts and factors in naturalistic decision making (NDM). Decision-making in acute care nursing requires an evaluation of many complex factors. While decision-making research in acute care nursing is prevalent, errors in decision-making continue to lead to poor patient outcomes. Naturalistic decision making may provide a framework for further exploring decision-making in acute care nursing practice. A better understanding of the literature is needed to guide future research to more effectively support acute care nurse decision-making. PubMed and CINAHL databases were searched, and research meeting criteria was included. Data were identified from all included articles, and themes were developed based on these data. Key findings in this review include nursing experience and associated factors; organisation and unit culture influences on decision-making; education; understanding patient status; situation awareness; and autonomy. Acute care nurses employ a variety of decision-making factors and processes and informally identify experienced nurses to be important resources for decision-making. Incorporation of evidence into acute care nursing practice continues to be a struggle for acute care nurses. This review indicates that naturalistic decision making may be applicable to decision-making nursing research. Experienced nurses bring a broad range of previous patient encounters to their practice influencing their intuitive, unconscious processes which facilitates decision-making. Using naturalistic decision making as a conceptual framework to guide research may help with understanding how to better support less experienced nurses' decision-making for enhanced patient outcomes. © 2017 John Wiley & Sons Ltd.

Imitation Combined with a Characteristic Stimulus Duration Results in Robust Collective Decision-Making.

PubMed

Toulet, Sylvain; Gautrais, Jacques; Bon, Richard; Peruani, Fernando

2015-01-01

For group-living animals, reaching consensus to stay cohesive is crucial for their fitness, particularly when collective motion starts and stops. Understanding the decision-making at individual and collective levels upon sudden disturbances is central in the study of collective animal behavior, and concerns the broader question of how information is distributed and evaluated in groups. Despite the relevance of the problem, well-controlled experimental studies that quantify the collective response of groups facing disruptive events are lacking. Here we study the behavior of small-sized groups of uninformed individuals subject to the departure and stop of a trained conspecific. We find that the groups reach an effective consensus: either all uninformed individuals follow the trained one (and collective motion occurs) or none does. Combining experiments and a simple mathematical model we show that the observed phenomena results from the interplay between simple mimetic rules and the characteristic duration of the stimulus, here, the time during which the trained individual is moving away. The proposed mechanism strongly depends on group size, as observed in the experiments, and even if group splitting can occur, the most likely outcome is always a coherent collective group response (consensus). The prevalence of a consensus is expected even if the groups of naives face conflicting information, e.g. if groups contain two subgroups of trained individuals, one trained to stay and one trained to leave. Our results indicate that collective decision-making and consensus in (small) animal groups are likely to be self-organized phenomena that do not involve concertation or even communication among the group members.

Amphetamine primes enhanced motivation toward uncertain choices in rats with genetic alcohol preference.

PubMed

Oinio, Ville; Sundström, Mikko; Bäckström, Pia; Uhari-Väänänen, Johanna; Kiianmaa, Kalervo; Raasmaja, Atso; Piepponen, Petteri

2018-05-01

Comorbidity with gambling disorder (GD) and alcohol use disorder (AUD) is well documented. The purpose of our study was to examine the influence of genetic alcohol drinking tendency on reward-guided decision making behavior of rats and the impact of dopamine releaser D-amphetamine on this behavior. In this study, Alko alcohol (AA) and Wistar rats went through long periods of operant lever pressing training where the task was to choose the profitable of two options. The lever choices were guided by different-sized sucrose rewards (one or three pellets), and the probability of gaining the larger reward was slowly changed to a level where choosing the smaller reward would be the most profitable in the long run. After training, rats were injected (s.c.) with dopamine releaser D-amphetamine (0.3, 1.0 mg/kg) to study the impact of rapid dopamine release on this learned decision making behavior. Administration of D-amphetamine promoted unprofitable decision making of AA rats more robustly when compared to Wistar rats. At the same time, D-amphetamine reduced lever pressing responses. Interestingly, we found that this reduction in lever pressing was significantly greater in Wistar rats than in AA rats and it was not linked to motivation to consume sucrose. Our results indicate that conditioning to the lever pressing in uncertain environments is more pronounced in AA than in Wistar rats and indicate that the reinforcing effects of a gambling-like environment act as a stronger conditioning factor for rats that exhibit a genetic tendency for high alcohol drinking.

Shared Decision-Making in the Management of Congenital Vascular Malformations.

PubMed

Horbach, Sophie E R; Ubbink, Dirk T; Stubenrouch, Fabienne E; Koelemay, Mark J W; van der Vleuten, Carine J M; Verhoeven, Bas H; Reekers, Jim A; Schultze Kool, Leo J; van der Horst, Chantal M A M

2017-03-01

In shared decision-making, clinicians and patients arrive at a joint treatment decision, by incorporating best available evidence and the patients' personal values and preferences. Little is known about the role of shared decision-making in managing patients with congenital vascular malformations, for which preference-sensitive decision-making seems obvious. The authors investigated preferences regarding decision-making and current shared decision-making behavior during physician-patient encounters. In two Dutch university hospitals, adults and children with congenital vascular malformations facing a treatment-related decision were enrolled. Before the consultation, patients (or parents of children) expressed their preference regarding decision-making (Control Preferences Scale). Afterward, participants completed shared decision-making-specific questionnaires (nine-item Shared Decision-Making Questionnaire, CollaboRATE, and satisfaction), and physicians completed the Shared Decision-Making Questionnaire-Physician questionnaire. Consultations were audiotaped and patient involvement was scored by two independent researchers using the five-item Observing Patient Involvement instrument. All questionnaire results were expressed on a scale of 0 to 100 (optimum shared decision-making). Fifty-five participants (24 parents and 31 adult patients) were included. Two-thirds preferred the shared decision-making approach (Control Preferences Scale). Objective five-item Observing Patient Involvement scores were low (mean ± SD, 31 ± 15), whereas patient and physician Shared Decision-Making Questionnaire scores were high, with means of 68 ± 18 and 68 ± 19, respectively. The median CollaboRATE score was 93. There was no clear relationship between shared decision-making and satisfaction scores. Although adults and parents of children with vascular malformations express a strong desire for shared decision-making, objective shared decision-making behavior is still lacking, most likely because of poor awareness of the shared decision-making concept among patients, parents, and physicians. To improve shared decision-making practice, targeted interventions (e.g., decision aids, staff training) are essential.

Decision Making on the Labor and Delivery Unit: An Investigation of Influencing Factors.

PubMed

Gregory, Megan E; Sonesh, Shirley C; Feitosa, Jennifer; Benishek, Lauren E; Hughes, Ashley M; Salas, Eduardo

2017-09-01

Objective The aim of this study was to describe the relationship between negative affect (NA), decision-making style, time stress, and decision quality in health care. Background Health care providers must often make swift, high-stakes decisions. Influencing factors of the decision-making process in this context have been understudied. Method Within a sample of labor and delivery nurses, physicians, and allied personnel, we used self-report measures to examine the impact of trait factors, including NA, decision-making style, and perceived time stress, on decision quality in a situational judgment test (Study 1). In Study 2, we observed the influence of state NA, state decision-making style, state time stress, and their relationship with decision quality on real clinical decisions. Results In Study 1, we found that trait NA significantly predicted avoidant decision-making style. Furthermore, those who were higher on trait time stress and trait avoidant decision-making style exhibited poorer decisions. In Study 2, we observed associations between state NA with state avoidant and analytical decision-making styles. We also observed that these decision-making styles, when considered in tandem with time stress, were influential in predicting clinical decision quality. Conclusion NA predicts some decision-making styles, and decision-making style can affect decision quality under time stress. This is particularly true for state factors. Application Individual differences, such as affect and decision-making style, should be considered during selection. Training to reduce time stress perceptions should be provided.

Barriers to and facilitators of implementing shared decision making and decision support in a paediatric hospital: A descriptive study.

PubMed

Boland, Laura; McIsaac, Daniel I; Lawson, Margaret L

2016-04-01

To explore multiple stakeholders' perceived barriers to and facilitators of implementing shared decision making and decision support in a tertiary paediatric hospital. An interpretive descriptive qualitative study was conducted using focus groups and interviews to examine senior hospital administrators', clinicians', parents' and youths' perceived barriers to and facilitators of shared decision making and decision support implementation. Data were analyzed using inductive thematic analysis. Fifty-seven stakeholders participated. Six barrier and facilitator themes emerged. The main barrier was gaps in stakeholders' knowledge of shared decision making and decision support. Facilitators included compatibility between shared decision making and the hospital's culture and ideal practices, perceptions of positive patient and family outcomes associated with shared decision making, and positive attitudes regarding shared decision making and decision support. However, youth attitudes regarding the necessity and usefulness of a decision support program were a barrier. Two themes were both a barrier and a facilitator. First, stakeholder groups were uncertain which clinical situations are suitable for shared decision making (eg, new diagnoses, chronic illnesses, complex decisions or urgent decisions). Second, the clinical process may be hindered if shared decision making and decision support decrease efficiency and workflow; however, shared decision making may reduce repeat visits and save time over the long term. Specific knowledge translation strategies that improve shared decision making knowledge and match specific barriers identified by each stakeholder group may be required to promote successful shared decision making and decision support implementation in the authors' paediatric hospital.

Computational mate choice: theory and empirical evidence.

PubMed

Castellano, Sergio; Cadeddu, Giorgia; Cermelli, Paolo

2012-06-01

The present review is based on the thesis that mate choice results from information-processing mechanisms governed by computational rules and that, to understand how females choose their mates, we should identify which are the sources of information and how they are used to make decisions. We describe mate choice as a three-step computational process and for each step we present theories and review empirical evidence. The first step is a perceptual process. It describes the acquisition of evidence, that is, how females use multiple cues and signals to assign an attractiveness value to prospective mates (the preference function hypothesis). The second step is a decisional process. It describes the construction of the decision variable (DV), which integrates evidence (private information by direct assessment), priors (public information), and value (perceived utility) of prospective mates into a quantity that is used by a decision rule (DR) to produce a choice. We make the assumption that females are optimal Bayesian decision makers and we derive a formal model of DV that can explain the effects of preference functions, mate copying, social context, and females' state and condition on the patterns of mate choice. The third step of mating decision is a deliberative process that depends on the DRs. We identify two main categories of DRs (absolute and comparative rules), and review the normative models of mate sampling tactics associated to them. We highlight the limits of the normative approach and present a class of computational models (sequential-sampling models) that are based on the assumption that DVs accumulate noisy evidence over time until a decision threshold is reached. These models force us to rethink the dichotomy between comparative and absolute decision rules, between discrimination and recognition, and even between rational and irrational choice. Since they have a robust biological basis, we think they may represent a useful theoretical tool for behavioural ecologist interested in integrating proximate and ultimate causes of mate choice. Copyright © 2012 Elsevier B.V. All rights reserved.

Attenuated Neural Processing of Risk in Young Adults at Risk for Stimulant Dependence

PubMed Central

Reske, Martina; Stewart, Jennifer L.; Flagan, Taru M.; Paulus, Martin P.

2015-01-01

Objective Approximately 10% of young adults report non-medical use of stimulants (cocaine, amphetamine, methylphenidate), which puts them at risk for the development of dependence. This fMRI study investigates whether subjects at early stages of stimulant use show altered decision making processing. Methods 158 occasional stimulants users (OSU) and 50 comparison subjects (CS) performed a “risky gains” decision making task during which they could select safe options (cash in 20 cents) or gamble them for double or nothing in two consecutive gambles (win or lose 40 or 80 cents, “risky decisions”). The primary analysis focused on risky versus safe decisions. Three secondary analyses were conducted: First, a robust regression examined the effect of lifetime exposure to stimulants and marijuana; second, subgroups of OSU with >1000 (n = 42), or <50 lifetime marijuana uses (n = 32), were compared to CS with <50 lifetime uses (n = 46) to examine potential marijuana effects; third, brain activation associated with behavioral adjustment following monetary losses was probed. Results There were no behavioral differences between groups. OSU showed attenuated activation across risky and safe decisions in prefrontal cortex, insula, and dorsal striatum, exhibited lower anterior cingulate cortex (ACC) and dorsal striatum activation for risky decisions and greater inferior frontal gyrus activation for safe decisions. Those OSU with relatively more stimulant use showed greater dorsal ACC and posterior insula attenuation. In comparison, greater lifetime marijuana use was associated with less neural differentiation between risky and safe decisions. OSU who chose more safe responses after losses exhibited similarities with CS relative to those preferring risky options. Discussion Individuals at risk for the development of stimulant use disorders presented less differentiated neural processing of risky and safe options. Specifically, OSU show attenuated brain response in regions critical for performance monitoring, reward processing and interoceptive awareness. Marijuana had additive effects by diminishing neural risk differentiation. PMID:26076493

Decision-making on shared sanitation in the informal settlements of Kisumu, Kenya.

PubMed

Simiyu, Sheillah; Swilling, Mark; Cairncross, Sandy

2017-10-01

Unlike most quantitative studies that investigate decision-making on investing in sanitation, this study adopted a qualitative approach to investigate decision-making on shared sanitation in the informal settlements of Kisumu city, in Kenya. Using a grounded theory approach, landlords and tenants were interviewed to identify sanitation decisions, individuals involved in decision-making and factors influencing decision-making. The results indicate that the main sanitation decisions are on investment, emptying, repair and cleaning. Landlords make investment, emptying and repair decisions, while tenants make cleaning decisions. Absentee landlords are less involved in most decision-making compared to live-in landlords, who rarely consult tenants in decision-making. Tenants make decisions after consultations with a third party and often collectively with other tenants. Sanitation interventions in informal settlements should thus, target landlords and tenants, with investment efforts being directed at landlords and maintenance efforts at tenants.

Using the Situated Clinical Decision-Making framework to guide analysis of nurses' clinical decision-making.

PubMed

Gillespie, Mary

2010-11-01

Nurses' clinical decision-making is a complex process that holds potential to influence the quality of care provided and patient outcomes. The evolution of nurses' decision-making that occurs with experience has been well documented. In addition, literature includes numerous strategies and approaches purported to support development of nurses' clinical decision-making. There has been, however, significantly less attention given to the process of assessing nurses' clinical decision-making and novice clinical educators are often challenged with knowing how to best support nurses and nursing students in developing their clinical decision-making capacity. The Situated Clinical Decision-Making framework is presented for use by clinical educators: it provides a structured approach to analyzing nursing students' and novice nurses' decision-making in clinical nursing practice, assists educators in identifying specific issues within nurses' clinical decision-making, and guides selection of relevant strategies to support development of clinical decision-making. A series of questions is offered as a guide for clinical educators when assessing nurses' clinical decision-making. The discussion presents key considerations related to analysis of various decision-making components, including common sources of challenge and errors that may occur within nurses' clinical decision-making. An exemplar illustrates use of the framework and guiding questions. Implications of this approach for selection of strategies that support development of clinical decision-making are highlighted. Copyright © 2010 Elsevier Ltd. All rights reserved.

[Decision Making and Electrodermal Activity].

PubMed

Kobayakawa, Mutsutaka

2016-08-01

Decision making is aided by emotions. Bodily responses, such as sweating, heartbeat, and visceral sensation, are used to monitor the emotional state during decision making. Because decision making in dairy life is complicated and cognitively demanding, these bodily signals are thought to facilitate the decision making process by assigning positive or negative values for each of the behavioral options. The sweat response in a decision making task is measured by skin conductance response (SCR). SCR in decision making is divided into two categories: anticipatory SCR is observed before making decisions, and reward/punishment SCR is observed after the outcome of the decision is perceived. Brain lesion studies in human revealed that the amygdala and ventromedial prefrontal cortex are important in decision making. Patients with lesinon in the amygdala exhibit neither the anticipatory nor reward/punishment SCRs, while patients with the ventromedial prefrontal lesions have deficits only in the anticipatory SCRs. Decision making tasks and SCR analysis have contributed to reveal the implicit aspects of decision making. Further research is necessary for clarifying the role of explicit process of decision making and its relationship with the implicit process.

Abductive networks applied to electronic combat

NASA Astrophysics Data System (ADS)

Montgomery, Gerard J.; Hess, Paul; Hwang, Jong S.

1990-08-01

A practical approach to dealing with combinatorial decision problems and uncertainties associated with electronic combat through the use of networks of high-level functional elements called abductive networks is presented. It describes the application of the Abductory Induction Mechanism (AIMTM) a supervised inductive learning tool for synthesizing polynomial abductive networks to the electronic combat problem domain. From databases of historical expert-generated or simulated combat engagements AIM can often induce compact and robust network models for making effective real-time electronic combat decisions despite significant uncertainties or a combinatorial explosion of possible situations. The feasibility of applying abductive networks to realize advanced combat decision aiding capabilities was demonstrated by applying AIM to a set of electronic combat simulations. The networks synthesized by AIM generated accurate assessments of the intent lethality and overall risk associated with a variety of simulated threats and produced reasonable estimates of the expected effectiveness of a group of electronic countermeasures for a large number of simulated combat scenarios. This paper presents the application of abductive networks to electronic combat summarizes the results of experiments performed using AIM discusses the benefits and limitations of applying abductive networks to electronic combat and indicates why abductive networks can often result in capabilities not attainable using alternative approaches. 1. ELECTRONIC COMBAT. UNCERTAINTY. AND MACHINE LEARNING Electronic combat has become an essential part of the ability to make war and has become increasingly complex since

How experimental biology and ecology can support evidence-based decision-making in conservation: avoiding pitfalls and enabling application.

PubMed

Cooke, Steven J; Birnie-Gauvin, Kim; Lennox, Robert J; Taylor, Jessica J; Rytwinski, Trina; Rummer, Jodie L; Franklin, Craig E; Bennett, Joseph R; Haddaway, Neal R

2017-01-01

Policy development and management decisions should be based upon the best available evidence. In recent years, approaches to evidence synthesis, originating in the medical realm (such as systematic reviews), have been applied to conservation to promote evidence-based conservation and environmental management. Systematic reviews involve a critical appraisal of evidence, but studies that lack the necessary rigour (e.g. experimental, technical and analytical aspects) to justify their conclusions are typically excluded from systematic reviews or down-weighted in terms of their influence. One of the strengths of conservation physiology is the reliance on experimental approaches that help to more clearly establish cause-and-effect relationships. Indeed, experimental biology and ecology have much to offer in terms of building the evidence base that is needed to inform policy and management options related to pressing issues such as enacting endangered species recovery plans or evaluating the effectiveness of conservation interventions. Here, we identify a number of pitfalls that can prevent experimental findings from being relevant to conservation or would lead to their exclusion or down-weighting during critical appraisal in a systematic review. We conclude that conservation physiology is well positioned to support evidence-based conservation, provided that experimental designs are robust and that conservation physiologists understand the nuances associated with informing decision-making processes so that they can be more relevant.

How experimental biology and ecology can support evidence-based decision-making in conservation: avoiding pitfalls and enabling application

PubMed Central

Birnie-Gauvin, Kim; Lennox, Robert J.; Taylor, Jessica J.; Rytwinski, Trina; Rummer, Jodie L.; Franklin, Craig E.; Bennett, Joseph R.; Haddaway, Neal R.

2017-01-01

Abstract Policy development and management decisions should be based upon the best available evidence. In recent years, approaches to evidence synthesis, originating in the medical realm (such as systematic reviews), have been applied to conservation to promote evidence-based conservation and environmental management. Systematic reviews involve a critical appraisal of evidence, but studies that lack the necessary rigour (e.g. experimental, technical and analytical aspects) to justify their conclusions are typically excluded from systematic reviews or down-weighted in terms of their influence. One of the strengths of conservation physiology is the reliance on experimental approaches that help to more clearly establish cause-and-effect relationships. Indeed, experimental biology and ecology have much to offer in terms of building the evidence base that is needed to inform policy and management options related to pressing issues such as enacting endangered species recovery plans or evaluating the effectiveness of conservation interventions. Here, we identify a number of pitfalls that can prevent experimental findings from being relevant to conservation or would lead to their exclusion or down-weighting during critical appraisal in a systematic review. We conclude that conservation physiology is well positioned to support evidence-based conservation, provided that experimental designs are robust and that conservation physiologists understand the nuances associated with informing decision-making processes so that they can be more relevant. PMID:28835842

Empowering Personalized Medicine with Big Data and Semantic Web Technology: Promises, Challenges, and Use Cases.

PubMed

Panahiazar, Maryam; Taslimitehrani, Vahid; Jadhav, Ashutosh; Pathak, Jyotishman

2014-10-01

In healthcare, big data tools and technologies have the potential to create significant value by improving outcomes while lowering costs for each individual patient. Diagnostic images, genetic test results and biometric information are increasingly generated and stored in electronic health records presenting us with challenges in data that is by nature high volume, variety and velocity, thereby necessitating novel ways to store, manage and process big data. This presents an urgent need to develop new, scalable and expandable big data infrastructure and analytical methods that can enable healthcare providers access knowledge for the individual patient, yielding better decisions and outcomes. In this paper, we briefly discuss the nature of big data and the role of semantic web and data analysis for generating "smart data" which offer actionable information that supports better decision for personalized medicine. In our view, the biggest challenge is to create a system that makes big data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare costs. We highlight some of the challenges in using big data and propose the need for a semantic data-driven environment to address them. We illustrate our vision with practical use cases, and discuss a path for empowering personalized medicine using big data and semantic web technology.

Assessment of undergraduate nursing students from an Irish perspective: Decisions and dilemmas?

PubMed

Kennedy, Sara; Chesser-Smyth, Patricia

2017-11-01

Assessment of clinical competence plays a pivotal role in the education of undergraduate nursing students in preparation for registration. The challenges that face preceptors are represented in the international literature yet few studies have focused on the factors that influence the decision-making process by preceptors when students under-perform or appear to be borderline status in relation to clinical practice. This study explored the lived experiences of the preceptors during the assessment process using a phenomenological approach. This was a qualitative study that utilised a phenomenological approach to explore the lived experiences of the preceptors in relation to student assessment of those students who were incompetent and underperformed in clinical practice. Three categories emerged from the findings: First impressions, Emotional turmoil of failing a clinical assessment and competing demands in the workplace. It is proposed that employing a tripartite approach would enhance the assessment process to ensure a more robust and decision-sharing mechanism. This would support decisions that are made in the cases of incompetent or borderline nursing students and increase the objectivity of the competency assessment to ameliorate the emotional turmoil that is experienced by preceptors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Decision-making in irrigation networks: Selecting appropriate canal structures using multi-attribute decision analysis.

PubMed

Hosseinzade, Zeinab; Pagsuyoin, Sheree A; Ponnambalam, Kumaraswamy; Monem, Mohammad J

2017-12-01

The stiff competition for water between agriculture and non-agricultural production sectors makes it necessary to have effective management of irrigation networks in farms. However, the process of selecting flow control structures in irrigation networks is highly complex and involves different levels of decision makers. In this paper, we apply multi-attribute decision making (MADM) methodology to develop a decision analysis (DA) framework for evaluating, ranking and selecting check and intake structures for irrigation canals. The DA framework consists of identifying relevant attributes for canal structures, developing a robust scoring system for alternatives, identifying a procedure for data quality control, and identifying a MADM model for the decision analysis. An application is illustrated through an analysis for automation purposes of the Qazvin irrigation network, one of the oldest and most complex irrigation networks in Iran. A survey questionnaire designed based on the decision framework was distributed to experts, managers, and operators of the Qazvin network and to experts from the Ministry of Power in Iran. Five check structures and four intake structures were evaluated. A decision matrix was generated from the average scores collected from the survey, and was subsequently solved using TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method. To identify the most critical structure attributes for the selection process, optimal attribute weights were calculated using Entropy method. For check structures, results show that the duckbill weir is the preferred structure while the pivot weir is the least preferred. Use of the duckbill weir can potentially address the problem with existing Amil gates where manual intervention is required to regulate water levels during periods of flow extremes. For intake structures, the Neyrpic® gate and constant head orifice are the most and least preferred alternatives, respectively. Some advantages of the Neyrpic® gate are ease of operation and capacity to measure discharge flows. Overall, the application to the Qazvin irrigation network demonstrates the utility of the proposed DA framework in selecting appropriate structures for regulating water flows in irrigation canals. This framework systematically aids the decision process by capturing decisions made at various levels (individual farmers to high-level management). It can be applied to other cases where a new irrigation network is being designed, or where changes in irrigation structures need to be identified to improve flow control in existing networks. Copyright © 2017 Elsevier B.V. All rights reserved.

A robust multi-objective global supplier selection model under currency fluctuation and price discount

NASA Astrophysics Data System (ADS)

Zarindast, Atousa; Seyed Hosseini, Seyed Mohamad; Pishvaee, Mir Saman

2017-06-01

Robust supplier selection problem, in a scenario-based approach has been proposed, when the demand and exchange rates are subject to uncertainties. First, a deterministic multi-objective mixed integer linear programming is developed; then, the robust counterpart of the proposed mixed integer linear programming is presented using the recent extension in robust optimization theory. We discuss decision variables, respectively, by a two-stage stochastic planning model, a robust stochastic optimization planning model which integrates worst case scenario in modeling approach and finally by equivalent deterministic planning model. The experimental study is carried out to compare the performances of the three models. Robust model resulted in remarkable cost saving and it illustrated that to cope with such uncertainties, we should consider them in advance in our planning. In our case study different supplier were selected due to this uncertainties and since supplier selection is a strategic decision, it is crucial to consider these uncertainties in planning approach.

Integrated assessments of green infrastructure for flood mitigation to support robust decision-making for sponge city construction in an urbanized watershed.

PubMed

Mei, Chao; Liu, Jiahong; Wang, Hao; Yang, Zhiyong; Ding, Xiangyi; Shao, Weiwei

2018-10-15

Green Infrastructure (GI) has become increasingly important in urban stormwater management because of the effects of climate change and urbanization. To mitigate severe urban water-related problems, China is implementing GI at the national scale under its Sponge City Program (SCP). The SCP is currently in a pilot period, however, little attention has been paid to the cost-effectiveness of GI implementation in China. In this study, an evaluation framework based on the Storm Water Management Model (SWMM) and life cycle cost analysis (LCCA) was applied to undertake integrated assessments of the development of GI for flood mitigation, to support robust decision making regarding sponge city construction in urbanized watersheds. A baseline scenario and 15 GI scenarios under six design rainfall events with recurrence intervals ranging from 2-100 years were simulated and assessed. Model simulation results confirmed the effectiveness of GI for flood mitigation. Nevertheless, even under the most beneficial scenario, the results showed the hydrological performance of GI was incapable of eliminating flooding. Analysis indicated the bioretention cell (BC) plus vegetated swale (VS) scenario was the most cost-effective GI option for unit investment under all rainfall events. However, regarding the maximum potential of the implementation areas of all GI scenarios, the porous pavement plus BC + VS strategy was considered most reasonable for the study area. Although the optimal combinations are influenced by uncertainties in both the model and the GI parameters, the main trends and key insights derived remain unaffected; therefore, the conclusions are relevant regarding sponge city construction within the study area. Copyright © 2018 Elsevier B.V. All rights reserved.

A structured analysis of uncertainty surrounding modeled impacts of groundwater-extraction rules

NASA Astrophysics Data System (ADS)

Guillaume, Joseph H. A.; Qureshi, M. Ejaz; Jakeman, Anthony J.

2012-08-01

Integrating economic and groundwater models for groundwater-management can help improve understanding of trade-offs involved between conflicting socioeconomic and biophysical objectives. However, there is significant uncertainty in most strategic decision-making situations, including in the models constructed to represent them. If not addressed, this uncertainty may be used to challenge the legitimacy of the models and decisions made using them. In this context, a preliminary uncertainty analysis was conducted of a dynamic coupled economic-groundwater model aimed at assessing groundwater extraction rules. The analysis demonstrates how a variety of uncertainties in such a model can be addressed. A number of methods are used including propagation of scenarios and bounds on parameters, multiple models, block bootstrap time-series sampling and robust linear regression for model calibration. These methods are described within the context of a theoretical uncertainty management framework, using a set of fundamental uncertainty management tasks and an uncertainty typology.

Forecasting wildlife response to rapid warming in the Alaskan Arctic

USGS Publications Warehouse

Van Hemert, Caroline R.; Flint, Paul L.; Udevitz, Mark S.; Koch, Joshua C.; Atwood, Todd C.; Oakley, Karen L.; Pearce, John M.

2015-01-01

Arctic wildlife species face a dynamic and increasingly novel environment because of climate warming and the associated increase in human activity. Both marine and terrestrial environments are undergoing rapid environmental shifts, including loss of sea ice, permafrost degradation, and altered biogeochemical fluxes. Forecasting wildlife responses to climate change can facilitate proactive decisions that balance stewardship with resource development. In this article, we discuss the primary and secondary responses to physical climate-related drivers in the Arctic, associated wildlife responses, and additional sources of complexity in forecasting wildlife population outcomes. Although the effects of warming on wildlife populations are becoming increasingly well documented in the scientific literature, clear mechanistic links are often difficult to establish. An integrated science approach and robust modeling tools are necessary to make predictions and determine resiliency to change. We provide a conceptual framework and introduce examples relevant for developing wildlife forecasts useful to management decisions.

Using Bayesian Networks for Candidate Generation in Consistency-based Diagnosis

NASA Technical Reports Server (NTRS)

Narasimhan, Sriram; Mengshoel, Ole

2008-01-01

Consistency-based diagnosis relies heavily on the assumption that discrepancies between model predictions and sensor observations can be detected accurately. When sources of uncertainty like sensor noise and model abstraction exist robust schemes have to be designed to make a binary decision on whether predictions are consistent with observations. This risks the occurrence of false alarms and missed alarms when an erroneous decision is made. Moreover when multiple sensors (with differing sensing properties) are available the degree of match between predictions and observations can be used to guide the search for fault candidates. In this paper we propose a novel approach to handle this problem using Bayesian networks. In the consistency- based diagnosis formulation, automatically generated Bayesian networks are used to encode a probabilistic measure of fit between predictions and observations. A Bayesian network inference algorithm is used to compute most probable fault candidates.

Communal Cooperation in Sensor Networks for Situation Management

NASA Technical Reports Server (NTRS)

Jones, Kennie H.; Lodding, Kenneth N.; Olariu, Stephan; Wilson, Larry; Xin,Chunsheng

2006-01-01

Situation management is a rapidly evolving science where managed sources are processed as realtime streams of events and fused in a way that maximizes comprehension, thus enabling better decisions for action. Sensor networks provide a new technology that promises ubiquitous input and action throughout an environment, which can substantially improve information available to the process. Here we describe a NASA program that requires improvements in sensor networks and situation management. We present an approach for massively deployed sensor networks that does not rely on centralized control but is founded in lessons learned from the way biological ecosystems are organized. In this approach, fully distributed data aggregation and integration can be performed in a scalable fashion where individual motes operate based on local information, making local decisions that achieve globally-meaningful results. This exemplifies the robust, fault-tolerant infrastructure required for successful situation management systems.

Factors related to drug approvals: predictors of outcome?

PubMed

Liberti, Lawrence; Breckenridge, Alasdair; Hoekman, Jarno; McAuslane, Neil; Stolk, Pieter; Leufkens, Hubert

2017-06-01

There is growing interest in characterising factors associated with positive regulatory outcomes for drug marketing authorisations. We assessed empirical studies published over the past 15 years seeking to identify predictive factors. Factors were classified to one of four 'factor clusters': evidentiary support; product or indication characteristics; company experience or strategy; social and regulatory factors. We observed a heterogeneous mix of technical factors (e.g., study designs, clinical evidence of efficacy) and less studied social factors (e.g., company-regulator interactions). We confirmed factors known to be of relevance to drug approval decisions (imperative) and a cohort of less understood (compensatory) social factors. Having robust supportive clinical evidence, addressing rare or serious illness, following scientific advice and prior company experience were associated with positive outcomes, which illustrated the multifactorial nature of regulatory decision making and factors need to be considered holistically while having varying, context-dependent importance. Copyright © 2017 Elsevier Ltd. All rights reserved.

What is a “good” treatment decision?: Decisional control, knowledge, treatment decision-making, and quality of life in men with clinically localized prostate cancer

PubMed Central

Orom, Heather; Biddle, Caitlin; Underwood, Willie; Nelson, Christian J.; Homish, D. Lynn

2016-01-01

Objective We explored whether active patient involvement in decision making and greater patient knowledge are associated with better treatment decision making experiences and better quality of life (QOL) among men with clinically localized prostate cancer. Localized prostate cancer treatment decision-making is an advantageous model for studying patient treatment decision-making dynamics as there are multiple treatment options and a lack of empirical evidence to recommend one over the other; consequently, it is recommended that patients be fully involved in making the decision. Methods Men with newly diagnosed clinically localized prostate cancer (N=1529) completed measures of decisional control, prostate cancer knowledge, and their decision-making experience (decisional conflict, and decision-making satisfaction and difficulty) shortly after they made their treatment decision. Prostate cancer-specific QOL was assessed 6-months after treatment. Results More active involvement in decision making and greater knowledge were associated with lower decisional conflict and higher decision-making satisfaction, but greater decision-making difficulty. An interaction between decisional control and knowledge revealed that greater knowledge was only associated with greater difficulty for men actively involved in making the decision (67% of sample). Greater knowledge, but not decisional control predicted better QOL 6-months post-treatment. Conclusion Although men who are actively involved in decision making and more knowledgeable may make more informed decisions, they could benefit from decisional support (e.g., decision-making aids, emotional support from providers, strategies for reducing emotional distress) to make the process easier. Men who were more knowledgeable about prostate cancer and treatment side effects at the time they made their treatment decision may have appraised their QOL as higher because they had realistic expectations about side effects. PMID:26957566

What Is a "Good" Treatment Decision? Decisional Control, Knowledge, Treatment Decision Making, and Quality of Life in Men with Clinically Localized Prostate Cancer.

PubMed

Orom, Heather; Biddle, Caitlin; Underwood, Willie; Nelson, Christian J; Homish, D Lynn

2016-08-01

We explored whether active patient involvement in decision making and greater patient knowledge are associated with better treatment decision-making experiences and better quality of life (QOL) among men with clinically localized prostate cancer. Localized prostate cancer treatment decision making is an advantageous model for studying patient treatment decision-making dynamics because there are multiple treatment options and a lack of empirical evidence to recommend one over the other; consequently, it is recommended that patients be fully involved in making the decision. Men with newly diagnosed clinically localized prostate cancer (N = 1529) completed measures of decisional control, prostate cancer knowledge, and decision-making experiences (decisional conflict and decision-making satisfaction and difficulty) shortly after they made their treatment decision. Prostate cancer-specific QOL was assessed at 6 months after treatment. More active involvement in decision making and greater knowledge were associated with lower decisional conflict and higher decision-making satisfaction but greater decision-making difficulty. An interaction between decisional control and knowledge revealed that greater knowledge was only associated with greater difficulty for men actively involved in making the decision (67% of sample). Greater knowledge, but not decisional control, predicted better QOL 6 months after treatment. Although men who are actively involved in decision making and more knowledgeable may make more informed decisions, they could benefit from decisional support (e.g., decision-making aids, emotional support from providers, strategies for reducing emotional distress) to make the process easier. Men who were more knowledgeable about prostate cancer and treatment side effects at the time that they made their treatment decision may have appraised their QOL as higher because they had realistic expectations about side effects. © The Author(s) 2016.

Barriers to and facilitators of implementing shared decision making and decision support in a paediatric hospital: A descriptive study

PubMed Central

Boland, Laura; McIsaac, Daniel I; Lawson, Margaret L

2016-01-01

OBJECTIVE: To explore multiple stakeholders’ perceived barriers to and facilitators of implementing shared decision making and decision support in a tertiary paediatric hospital. METHODS: An interpretive descriptive qualitative study was conducted using focus groups and interviews to examine senior hospital administrators’, clinicians’, parents’ and youths’ perceived barriers to and facilitators of shared decision making and decision support implementation. Data were analyzed using inductive thematic analysis. RESULTS: Fifty-seven stakeholders participated. Six barrier and facilitator themes emerged. The main barrier was gaps in stakeholders’ knowledge of shared decision making and decision support. Facilitators included compatibility between shared decision making and the hospital’s culture and ideal practices, perceptions of positive patient and family outcomes associated with shared decision making, and positive attitudes regarding shared decision making and decision support. However, youth attitudes regarding the necessity and usefulness of a decision support program were a barrier. Two themes were both a barrier and a facilitator. First, stakeholder groups were uncertain which clinical situations are suitable for shared decision making (eg, new diagnoses, chronic illnesses, complex decisions or urgent decisions). Second, the clinical process may be hindered if shared decision making and decision support decrease efficiency and workflow; however, shared decision making may reduce repeat visits and save time over the long term. CONCLUSIONS: Specific knowledge translation strategies that improve shared decision making knowledge and match specific barriers identified by each stakeholder group may be required to promote successful shared decision making and decision support implementation in the authors’ paediatric hospital. PMID:27398058

Distinction between Externally vs. Internally Guided Decision-Making: Operational Differences, Meta-Analytical Comparisons and Their Theoretical Implications

PubMed Central

Nakao, Takashi; Ohira, Hideki; Northoff, Georg

2012-01-01

Most experimental studies of decision-making have specifically examined situations in which a single less-predictable correct answer exists (externally guided decision-making under uncertainty). Along with such externally guided decision-making, there are instances of decision-making in which no correct answer based on external circumstances is available for the subject (internally guided decision-making). Such decisions are usually made in the context of moral decision-making as well as in preference judgment, where the answer depends on the subject’s own, i.e., internal, preferences rather than on external, i.e., circumstantial, criteria. The neuronal and psychological mechanisms that allow guidance of decisions based on more internally oriented criteria in the absence of external ones remain unclear. This study was undertaken to compare decision-making of these two kinds empirically and theoretically. First, we reviewed studies of decision-making to clarify experimental–operational differences between externally guided and internally guided decision-making. Second, using multi-level kernel density analysis, a whole-brain-based quantitative meta-analysis of neuroimaging studies was performed. Our meta-analysis revealed that the neural network used predominantly for internally guided decision-making differs from that for externally guided decision-making under uncertainty. This result suggests that studying only externally guided decision-making under uncertainty is insufficient to account for decision-making processes in the brain. Finally, based on the review and results of the meta-analysis, we discuss the differences and relations between decision-making of these two types in terms of their operational, neuronal, and theoretical characteristics. PMID:22403525

Risk, Robustness and Water Resources Planning Under Uncertainty

NASA Astrophysics Data System (ADS)

Borgomeo, Edoardo; Mortazavi-Naeini, Mohammad; Hall, Jim W.; Guillod, Benoit P.

2018-03-01

Risk-based water resources planning is based on the premise that water managers should invest up to the point where the marginal benefit of risk reduction equals the marginal cost of achieving that benefit. However, this cost-benefit approach may not guarantee robustness under uncertain future conditions, for instance under climatic changes. In this paper, we expand risk-based decision analysis to explore possible ways of enhancing robustness in engineered water resources systems under different risk attitudes. Risk is measured as the expected annual cost of water use restrictions, while robustness is interpreted in the decision-theoretic sense as the ability of a water resource system to maintain performance—expressed as a tolerable risk of water use restrictions—under a wide range of possible future conditions. Linking risk attitudes with robustness allows stakeholders to explicitly trade-off incremental increases in robustness with investment costs for a given level of risk. We illustrate the framework through a case study of London's water supply system using state-of-the -art regional climate simulations to inform the estimation of risk and robustness.

One Way of Thinking About Decision Making.

ERIC Educational Resources Information Center

Dalis, Gus T.; Strasser, Ben B.

The authors present the DALSTRA model of decision making, a descriptive statement of ways individuals or groups respond to different kinds of decision-making problems they encounter. Decision making is viewed in two phases: the decision-making antecedents (whether to decide, how to decide) and the modes of decision making (Chance/Impulse,…

Strategic Decision Making Paradigms: A Primer for Senior Leaders

DTIC Science & Technology

2009-07-01

decision making . STRATEGIC DECISION MAKING Strategic Change: There are several strategic...influenced by stakeholders outside of the organization. The Ontology of Strategic Decision Making . Strategic decisions are non-routine and involve...Coates USAWC, July 2009 5 The Complexity of Strategic Decision Making Strategic decisions entail “ill-structured,”6 “messy” or

Training in Decision-making Strategies: An approach to enhance students' competence to deal with socio-scientific issues

NASA Astrophysics Data System (ADS)

Gresch, Helge; Hasselhorn, Marcus; Bögeholz, Susanne

2013-10-01

Dealing with socio-scientific issues in science classes enables students to participate productively in controversial discussions concerning ethical topics, such as sustainable development. In this respect, well-structured decision-making processes are essential for elaborate reasoning. To foster decision-making competence, a computer-based programme was developed that trains secondary school students (grades 11-13) in decision-making strategies. The main research question is: does training students to use these strategies foster decision-making competence? In addition, the influence of meta-decision aids was examined. Students conducted a task analysis to select an appropriate strategy prior to the decision-making process. Hence, the second research question is: does combining decision-making training with a task analysis enhance decision-making competence at a higher rate? To answer these questions, 386 students were tested in a pre-post-follow-up control-group design that included two training groups (decision-making strategies/decision-making strategies combined with a task analysis) and a control group (decision-making with additional ecological information instead of strategic training). An open-ended questionnaire was used to assess decision-making competence in situations related to sustainable development. The decision-making training led to a significant improvement in the post-test and the follow-up, which was administered three months after the training. Long-term effects on the quality of the students' decisions were evident for both training groups. Gains in competence when reflecting upon the decision-making processes of others were found, to a lesser extent, in the training group that received the additional meta-decision training. In conclusion, training in decision-making strategies is a promising approach to deal with socio-scientific issues related to sustainable development.

Shared Decision Making in ICUs: An American College of Critical Care Medicine and American Thoracic Society Policy Statement.

PubMed

Kon, Alexander A; Davidson, Judy E; Morrison, Wynne; Danis, Marion; White, Douglas B

2016-01-01

Shared decision making is endorsed by critical care organizations; however, there remains confusion about what shared decision making is, when it should be used, and approaches to promote partnerships in treatment decisions. The purpose of this statement is to define shared decision making, recommend when shared decision making should be used, identify the range of ethically acceptable decision-making models, and present important communication skills. The American College of Critical Care Medicine and American Thoracic Society Ethics Committees reviewed empirical research and normative analyses published in peer-reviewed journals to generate recommendations. Recommendations approved by consensus of the full Ethics Committees of American College of Critical Care Medicine and American Thoracic Society were included in the statement. Six recommendations were endorsed: 1) DEFINITION: Shared decision making is a collaborative process that allows patients, or their surrogates, and clinicians to make healthcare decisions together, taking into account the best scientific evidence available, as well as the patient's values, goals, and preferences. 2) Clinicians should engage in a shared decision making process to define overall goals of care (including decisions regarding limiting or withdrawing life-prolonging interventions) and when making major treatment decisions that may be affected by personal values, goals, and preferences. 3) Clinicians should use as their "default" approach a shared decision making process that includes three main elements: information exchange, deliberation, and making a treatment decision. 4) A wide range of decision-making approaches are ethically supportable, including patient- or surrogate-directed and clinician-directed models. Clinicians should tailor the decision-making process based on the preferences of the patient or surrogate. 5) Clinicians should be trained in communication skills. 6) Research is needed to evaluate decision-making strategies. Patient and surrogate preferences for decision-making roles regarding value-laden choices range from preferring to exercise significant authority to ceding such authority to providers. Clinicians should adapt the decision-making model to the needs and preferences of the patient or surrogate.

Modelling elderly cardiac patients decision making using Cognitive Work Analysis: identifying requirements for patient decision aids.

PubMed

Dhukaram, Anandhi Vivekanandan; Baber, Chris

2015-06-01

Patients make various healthcare decisions on a daily basis. Such day-to-day decision making can have significant consequences on their own health, treatment, care, and costs. While decision aids (DAs) provide effective support in enhancing patient's decision making, to date there have been few studies examining patient's decision making process or exploring how the understanding of such decision processes can aid in extracting requirements for the design of DAs. This paper applies Cognitive Work Analysis (CWA) to analyse patient's decision making in order to inform requirements for supporting self-care decision making. This study uses focus groups to elicit information from elderly cardiovascular disease (CVD) patients concerning a range of decision situations they face on a daily basis. Specifically, the focus groups addressed issues related to the decision making of CVD in terms of medication compliance, pain, diet and exercise. The results of these focus groups are used to develop high level views using CWA. CWA framework decomposes the complex decision making problem to inform three approaches to DA design: one design based on high level requirements; one based on a normative model of decision-making for patients; and the third based on a range of heuristics that patients seem to use. CWA helps in extracting and synthesising decision making from different perspectives: decision processes, work organisation, patient competencies and strategies used in decision making. As decision making can be influenced by human behaviour like skills, rules and knowledge, it is argued that patients require support to different types of decision making. This paper also provides insights for designers in using CWA framework for the design of effective DAs to support patients in self-management. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Making framing of uncertainty in water management practice explicit by using a participant-structured approach.

PubMed

Isendahl, Nicola; Dewulf, Art; Pahl-Wostl, Claudia

2010-01-01

By now, the need for addressing uncertainty in the management of water resources is widely recognized, yet there is little expertise and experience how to effectively deal with uncertainty in practice. Uncertainties in water management practice so far are mostly dealt with intuitively or based on experience. That way decisions can be quickly taken but analytic processes of deliberate reasoning are bypassed. To meet the desire of practitioners for better guidance and tools how to deal with uncertainty more practice-oriented systematic approaches are needed. For that purpose we consider it important to understand how practitioners frame uncertainties. In this paper we present an approach where water managers developed criteria of relevance to understand and address uncertainties. The empirical research took place in the Doñana region of the Guadalquivir estuary in southern Spain making use of the method of card sorting. Through the card sorting exercise a broad range of criteria to make sense of and describe uncertainties was produced by different subgroups, which were then merged into a shared list of criteria. That way framing differences were made explicit and communication on uncertainty and on framing differences was enhanced. In that, the present approach constitutes a first step to enabling reframing and overcoming framing differences, which are important features on the way to robust decision-making. Moreover, the elaborated criteria build a basis for the development of more structured approaches to deal with uncertainties in water management practice. Copyright 2009 Elsevier Ltd. All rights reserved.

Network Robustness: the whole story

NASA Astrophysics Data System (ADS)

Longjas, A.; Tejedor, A.; Zaliapin, I. V.; Ambroj, S.; Foufoula-Georgiou, E.

2014-12-01

A multitude of actual processes operating on hydrological networks may exhibit binary outcomes such as clean streams in a river network that may become contaminated. These binary outcomes can be modeled by node removal processes (attacks) acting in a network. Network robustness against attacks has been widely studied in fields as diverse as the Internet, power grids and human societies. However, the current definition of robustness is only accounting for the connectivity of the nodes unaffected by the attack. Here, we put forward the idea that the connectivity of the affected nodes can play a crucial role in proper evaluation of the overall network robustness and its future recovery from the attack. Specifically, we propose a dual perspective approach wherein at any instant in the network evolution under attack, two distinct networks are defined: (i) the Active Network (AN) composed of the unaffected nodes and (ii) the Idle Network (IN) composed of the affected nodes. The proposed robustness metric considers both the efficiency of destroying the AN and the efficiency of building-up the IN. This approach is motivated by concrete applied problems, since, for example, if we study the dynamics of contamination in river systems, it is necessary to know both the connectivity of the healthy and contaminated parts of the river to assess its ecological functionality. We show that trade-offs between the efficiency of the Active and Idle network dynamics give rise to surprising crossovers and re-ranking of different attack strategies, pointing to significant implications for decision making.

Integrated Model-Based Decisions for Water, Energy and Food Nexus

NASA Astrophysics Data System (ADS)

Zhang, X.; Vesselinov, V. V.

2015-12-01

Energy, water and food are critical resources for sustaining social development and human lives; human beings cannot survive without any one of them. Energy crises, water shortages and food security are crucial worldwide problems. The nexus of energy, water and food has received more and more attention in the past decade. Energy, water and food are closely interrelated; water is required in energy development such as electricity generation; energy is indispensable for collecting, treating, and transporting water; both energy and water are crucial inputs for food production. Changes of either of them can lead to substantial impacts on other two resources, and vice versa. Effective decisions should be based on thorough research efforts for better understanding of their complex nexus. Rapid increase of population has significantly intensified the pressures on energy, water and food. Addressing and quantifying their interactive relationships are important for making robust and cost-effective strategies for managing the three resources simultaneously. In addition, greenhouse gases (GHGs) are emitted in energy, water, food production, consequently making contributions to growing climate change. Reflecting environmental impacts of GHGs is also desired (especially, on the quality and quantity of fresh water resources). Thus, a socio-economic model is developed in this study to quantitatively address the complex connections among energy, water and food production. A synthetic problem is proposed to demonstrate the model's applicability and feasibility. Preliminary results related to integrated decisions on energy supply management, water use planning, electricity generation planning, energy facility capacity expansion, food production, and associated GHG emission control are generated for providing cost-effective supports for decision makers.

Who should take responsibility for decisions on internationally recommended datasets? The case of the mass concentration of mercury in air at saturation

NASA Astrophysics Data System (ADS)

Brown, Richard J. C.; Brewer, Paul J.; Ent, Hugo; Fisicaro, Paola; Horvat, Milena; Kim, Ki-Hyun; Quétel, Christophe R.

2015-10-01

This paper considers how decisions on internationally recommended datasets are made and implemented and, further, how the ownership of these decisions comes about. Examples are given of conventionally agreed data and values where the responsibility is clear and comes about through official designation or by common usage and practice over long time periods. The example of the dataset describing the mass concentration of mercury in air at saturation is discussed in detail. This is a case where there are now several competing datasets that are in disagreement with each other, some with historical authority and some more recent but, arguably, with more robust metrological traceability to the SI. Further, it is elaborated that there is no body charged with the responsibility to make a decision on an international recommendation for such a dataset. This has led to the situation where several competing datasets are in use simultaneously. Close parallels are drawn with the current debate over changes to the ozone absorption cross section, which has equal importance to the measurement of ozone amount fraction in air and to subsequent compliance with air quality legislation. It is noted that in the case of the ozone cross section there is already a committee appointed to deliberate over any change. We make the proposal that a similar committee, under the auspices of IUPAC or the CIPM’s CCQM (if it adopted a reference data function) could be formed to perform a similar role for the mass concentration of mercury in air at saturation.

Modeling the dynamics of recognition memory testing with an integrated model of retrieval and decision making.

PubMed

Osth, Adam F; Jansson, Anna; Dennis, Simon; Heathcote, Andrew

2018-08-01

A robust finding in recognition memory is that performance declines monotonically across test trials. Despite the prevalence of this decline, there is a lack of consensus on the mechanism responsible. Three hypotheses have been put forward: (1) interference is caused by learning of test items (2) the test items cause a shift in the context representation used to cue memory and (3) participants change their speed-accuracy thresholds through the course of testing. We implemented all three possibilities in a combined model of recognition memory and decision making, which inherits the memory retrieval elements of the Osth and Dennis (2015) model and uses the diffusion decision model (DDM: Ratcliff, 1978) to generate choice and response times. We applied the model to four datasets that represent three challenges, the findings that: (1) the number of test items plays a larger role in determining performance than the number of studied items, (2) performance decreases less for strong items than weak items in pure lists but not in mixed lists, and (3) lexical decision trials interspersed between recognition test trials do not increase the rate at which performance declines. Analysis of the model's parameter estimates suggests that item interference plays a weak role in explaining the effects of recognition testing, while context drift plays a very large role. These results are consistent with prior work showing a weak role for item noise in recognition memory and that retrieval is a strong cause of context change in episodic memory. Copyright © 2018 Elsevier Inc. All rights reserved.

Decision-making in Swiss home-like childbirth: A grounded theory study.

PubMed

Meyer, Yvonne; Frank, Franziska; Schläppy Muntwyler, Franziska; Fleming, Valerie; Pehlke-Milde, Jessica

2017-12-01

Decision-making in midwifery, including a claim for shared decision-making between midwives and women, is of major significance for the health of mother and child. Midwives have little information about how to share decision-making responsibilities with women, especially when complications arise during birth. To increase understanding of decision-making in complex home-like birth settings by exploring midwives' and women's perspectives and to develop a dynamic model integrating participatory processes for making shared decisions. The study, based on grounded theory methodology, analysed 20 interviews of midwives and 20 women who had experienced complications in home-like births. The central phenomenon that arose from the data was "defining/redefining decision as a joint commitment to healthy childbirth". The sub-indicators that make up this phenomenon were safety, responsibility, mutual and personal commitments. These sub-indicators were also identified to influence temporal conditions of decision-making and to apply different strategies for shared decision-making. Women adopted strategies such as delegating a decision, making the midwife's decision her own, challenging a decision or taking a decision driven by the dynamics of childbirth. Midwives employed strategies such as remaining indecisive, approving a woman's decision, making an informed decision or taking the necessary decision. To respond to recommendations for shared responsibility for care, midwives need to strengthen their shared decision-making skills. The visual model of decision-making in childbirth derived from the data provides a framework for transferring clinical reasoning into practice. Copyright © 2017 Australian College of Midwives. Published by Elsevier Ltd. All rights reserved.

Shared decision-making - Rhetoric and reality: Women's experiences and perceptions of adjuvant treatment decision-making for breast cancer.

PubMed

Mahmoodi, Neda; Sargeant, Sally

2017-01-01

This interview-based study uses phenomenology as a theoretical framework and thematic analysis to challenge existing explanatory frameworks of shared decision-making, in an exploration of women's experiences and perceptions of shared decision-making for adjuvant treatment in breast cancer. Three themes emerged are as follows: (1) women's desire to participate in shared decision-making, (2) the degree to which shared decision-making is perceived to be shared and (3) to what extent are women empowered within shared decision-making. Studying breast cancer patients' subjective experiences of adjuvant treatment decision-making provides a broader perspective on patient participatory role preferences and doctor-patient power dynamics within shared decision-making for breast cancer.

Accurate estimation of influenza epidemics using Google search data via ARGO.

PubMed

Yang, Shihao; Santillana, Mauricio; Kou, S C

2015-11-24

Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search-based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people's online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions.

Environmental Research Translation: Enhancing Interactions with Communities at Contaminated Sites

NASA Astrophysics Data System (ADS)

Ramirez-Andreotta, M.; Brusseau, M. L. L.; Artiola, J. F.; Maier, R. M.; Gandolfi, A. J.

2015-12-01

The characterization and remediation of contaminated sites are complex endeavors fraught with numerous challenges. One particular challenge that is receiving increased attention is the development and encouragement of full participation by communities and community members affected by a given site in all facets of decision-making. Many disciplines have been grappling with the challenges associated with environmental and risk communication, public participation in environmental data generation and decision-making, and increasing community capacity. The concepts and methods developed by these disciplines are reviewed, with a focus on their relevance to the specific dynamics associated with contaminated sites. The contributions of these disciplines are then synthesized and integrated to help develop Environmental Research Translation (ERT), a proposed framework for environmental scientists to promote interaction and communication among involved parties at contaminated sites. This holistic approach is rooted in public participation approaches to science, which includes: a transdisciplinary team, effective collaboration, information transfer, public participation in environmental projects, and a cultural model of risk communication. Although there are challenges associated with the implementation of ERT, it is anticipated that application of this proposed translational science method could promote more robust community participation at contaminated sites.

Understanding How Grammatical Aspect Influences Legal Judgment

PubMed Central

Sherrill, Andrew M.; Eerland, Anita; Zwaan, Rolf A.; Magliano, Joseph P.

2015-01-01

Recent evidence suggests that grammatical aspect can bias how individuals perceive criminal intentionality during discourse comprehension. Given that criminal intentionality is a common criterion for legal definitions (e.g., first-degree murder), the present study explored whether grammatical aspect may also impact legal judgments. In a series of four experiments participants were provided with a legal definition and a description of a crime in which the grammatical aspect of provocation and murder events were manipulated. Participants were asked to make a decision (first- vs. second-degree murder) and then indicate factors that impacted their decision. Findings suggest that legal judgments can be affected by grammatical aspect but the most robust effects were limited to temporal dynamics (i.e., imperfective aspect results in more murder actions than perfective aspect), which may in turn influence other representational systems (i.e., number of murder actions positively predicts perceived intentionality). In addition, findings demonstrate that the influence of grammatical aspect on situation model construction and evaluation is dependent upon the larger linguistic and semantic context. Together, the results suggest grammatical aspect has indirect influences on legal judgments to the extent that variability in aspect changes the features of the situation model that align with criteria for making legal judgments. PMID:26496364

Environmental Research Translation: Enhancing Interactions with Communities at Contaminated Sites

PubMed Central

Ramirez-Andreotta, Monica D.; Brusseau, Mark L.; Artiola, Janick F.; Maier, Raina M.; Gandolfi, A. Jay

2014-01-01

The characterization and remediation of contaminated sites are complex endeavors fraught with numerous challenges. One particular challenge that is receiving increased attention is the development and encouragement of full participation by communities and community members affected by a given site in all facets of decision-making. Many disciplines have been grappling with the challenges associated with environmental and risk communication, public participation in environmental data generation, and decision-making and increasing community capacity. The concepts and methods developed by these disciplines are reviewed, with a focus on their relevance to the specific dynamics associated with environmental contamination sites. The contributions of these disciplines are then synthesized and integrated to help develop Environmental Research Translation (ERT), a proposed framework for environmental scientists to promote interaction and communication among involved parties at contaminated sites. This holistic approach is rooted in public participation approaches to science, which includes: a transdisciplinary team, effective collaboration, information transfer, public participation in environmental projects, and a cultural model of risk communication. Although there are challenges associated with the implementation of ERT, it is anticipated that application of this proposed translational science method could promote more robust community participation at contaminated sites. PMID:25173762

Endoscopic feature tracking for augmented-reality assisted prosthesis selection in mitral valve repair

NASA Astrophysics Data System (ADS)

Engelhardt, Sandy; Kolb, Silvio; De Simone, Raffaele; Karck, Matthias; Meinzer, Hans-Peter; Wolf, Ivo

2016-03-01

Mitral valve annuloplasty describes a surgical procedure where an artificial prosthesis is sutured onto the anatomical structure of the mitral annulus to re-establish the valve's functionality. Choosing an appropriate commercially available ring size and shape is a difficult decision the surgeon has to make intraoperatively according to his experience. In our augmented-reality framework, digitalized ring models are superimposed onto endoscopic image streams without using any additional hardware. To place the ring model on the proper position within the endoscopic image plane, a pose estimation is performed that depends on the localization of sutures placed by the surgeon around the leaflet origins and punctured through the stiffer structure of the annulus. In this work, the tissue penetration points are tracked by the real-time capable Lucas Kanade optical flow algorithm. The accuracy and robustness of this tracking algorithm is investigated with respect to the question whether outliers influence the subsequent pose estimation. Our results suggest that optical flow is very stable for a variety of different endoscopic scenes and tracking errors do not affect the position of the superimposed virtual objects in the scene, making this approach a viable candidate for annuloplasty augmented reality-enhanced decision support.

Group personality during collective decision-making: a multi-level approach.

PubMed

Planas-Sitjà, Isaac; Deneubourg, Jean-Louis; Gibon, Céline; Sempo, Grégory

2015-03-07

Collective decision-making processes emerge from social feedback networks within a group. Many studies on collective behaviour underestimate the role of individual personality and, as a result, personality is rarely analysed in the context of collective dynamics. Here, we show evidence of sheltering behaviour personality in a gregarious insect (Periplaneta americana), which is characterized by a collective personality at the group level. We also highlight that the individuals within groups exhibited consistent personality traits in their probability of sheltering and total time sheltered during the three trials over one week. Moreover, the group personality, which arises from the synergy between the distribution of behaviour profiles in the group and social amplifications, affected the sheltering dynamics. However, owing to its robustness, personality did not affect the group probability of reaching a consensus. Finally, to prove social interactions, we developed a new statistical method that will be helpful for future research on personality traits and group behaviour. This approach will help to identify the circumstances under which particular group compositions may improve the fitness of individuals in gregarious species. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Putting intelligent structured intermittent auscultation (ISIA) into practice.

PubMed

Maude, Robyn M; Skinner, Joan P; Foureur, Maralyn J

2016-06-01

Fetal monitoring guidelines recommend intermittent auscultation for the monitoring of fetal wellbeing during labour for low-risk women. However, these guidelines are not being translated into practice and low-risk women birthing in institutional maternity units are increasingly exposed to continuous cardiotocographic monitoring, both on admission to hospital and during labour. When continuous fetal monitoring becomes routinised, midwives and obstetricians lose practical skills around intermittent auscultation. To support clinical practice and decision-making around auscultation modality, the intelligent structured intermittent auscultation (ISIA) framework was developed. The purpose of this discussion paper is to describe the application of intelligent structured intermittent auscultation in practice. The intelligent structured intermittent auscultation decision-making framework is a knowledge translation tool that supports the implementation of evidence into practice around the use of intermittent auscultation for fetal heart monitoring for low-risk women during labour. An understanding of the physiology of the materno-utero-placental unit and control of the fetal heart underpin the development of the framework. Intelligent structured intermittent auscultation provides midwives with a robust means of demonstrating their critical thinking and clinical reasoning and supports their understanding of normal physiological birth. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Measuring Shared Decision Making in Psychiatric Care

PubMed Central

Salyers, Michelle P.; Matthias, Marianne S.; Fukui, Sadaaki; Holter, Mark C.; Collins, Linda; Rose, Nichole; Thompson, John; Coffman, Melinda; Torrey, William C.

2014-01-01

Objective Shared decision making is widely recognized to facilitate effective health care; tools are needed to measure the level of shared decision making in psychiatric practice. Methods A coding scheme assessing shared decision making in medical settings (1) was adapted, including creation of a manual. Trained raters analyzed 170 audio recordings of psychiatric medication check-up visits. Results Inter-rater reliability among three raters for a subset of 20 recordings ranged from 67% to 100% agreement for the presence of each of nine elements of shared decision making and 100% for the overall agreement between provider and consumer. Just over half of the decisions met minimum criteria for shared decision making. Shared decision making was not related to length of visit after controlling for complexity of decision. Conclusions The shared decision making rating scale appears to reliably assess shared decision making in psychiatric practice and could be helpful for future research, training, and implementation efforts. PMID:22854725

Using Boosting Decision Trees in Gravitational Wave Searches triggered by Gamma-ray Bursts

NASA Astrophysics Data System (ADS)

Zuraw, Sarah; LIGO Collaboration

2015-04-01

The search for gravitational wave bursts requires the ability to distinguish weak signals from background detector noise. Gravitational wave bursts are characterized by their transient nature, making them particularly difficult to detect as they are similar to non-Gaussian noise fluctuations in the detector. The Boosted Decision Tree method is a powerful machine learning algorithm which uses Multivariate Analysis techniques to explore high-dimensional data sets in order to distinguish between gravitational wave signal and background detector noise. It does so by training with known noise events and simulated gravitational wave events. The method is tested using waveform models and compared with the performance of the standard gravitational wave burst search pipeline for Gamma-ray Bursts. It is shown that the method is able to effectively distinguish between signal and background events under a variety of conditions and over multiple Gamma-ray Burst events. This example demonstrates the usefulness and robustness of the Boosted Decision Tree and Multivariate Analysis techniques as a detection method for gravitational wave bursts. LIGO, UMass, PREP, NEGAP.

Addressing uncertainty in adaptation planning for agriculture.

PubMed

Vermeulen, Sonja J; Challinor, Andrew J; Thornton, Philip K; Campbell, Bruce M; Eriyagama, Nishadi; Vervoort, Joost M; Kinyangi, James; Jarvis, Andy; Läderach, Peter; Ramirez-Villegas, Julian; Nicklin, Kathryn J; Hawkins, Ed; Smith, Daniel R

2013-05-21

We present a framework for prioritizing adaptation approaches at a range of timeframes. The framework is illustrated by four case studies from developing countries, each with associated characterization of uncertainty. Two cases on near-term adaptation planning in Sri Lanka and on stakeholder scenario exercises in East Africa show how the relative utility of capacity vs. impact approaches to adaptation planning differ with level of uncertainty and associated lead time. An additional two cases demonstrate that it is possible to identify uncertainties that are relevant to decision making in specific timeframes and circumstances. The case on coffee in Latin America identifies altitudinal thresholds at which incremental vs. transformative adaptation pathways are robust options. The final case uses three crop-climate simulation studies to demonstrate how uncertainty can be characterized at different time horizons to discriminate where robust adaptation options are possible. We find that impact approaches, which use predictive models, are increasingly useful over longer lead times and at higher levels of greenhouse gas emissions. We also find that extreme events are important in determining predictability across a broad range of timescales. The results demonstrate the potential for robust knowledge and actions in the face of uncertainty.

Addressing uncertainty in adaptation planning for agriculture

PubMed Central

Vermeulen, Sonja J.; Challinor, Andrew J.; Thornton, Philip K.; Campbell, Bruce M.; Eriyagama, Nishadi; Vervoort, Joost M.; Kinyangi, James; Jarvis, Andy; Läderach, Peter; Ramirez-Villegas, Julian; Nicklin, Kathryn J.; Hawkins, Ed; Smith, Daniel R.

2013-01-01

We present a framework for prioritizing adaptation approaches at a range of timeframes. The framework is illustrated by four case studies from developing countries, each with associated characterization of uncertainty. Two cases on near-term adaptation planning in Sri Lanka and on stakeholder scenario exercises in East Africa show how the relative utility of capacity vs. impact approaches to adaptation planning differ with level of uncertainty and associated lead time. An additional two cases demonstrate that it is possible to identify uncertainties that are relevant to decision making in specific timeframes and circumstances. The case on coffee in Latin America identifies altitudinal thresholds at which incremental vs. transformative adaptation pathways are robust options. The final case uses three crop–climate simulation studies to demonstrate how uncertainty can be characterized at different time horizons to discriminate where robust adaptation options are possible. We find that impact approaches, which use predictive models, are increasingly useful over longer lead times and at higher levels of greenhouse gas emissions. We also find that extreme events are important in determining predictability across a broad range of timescales. The results demonstrate the potential for robust knowledge and actions in the face of uncertainty. PMID:23674681

Linking decision-making research and cancer prevention and control: important themes.

PubMed

McCaul, Kevin D; Peters, Ellen; Nelson, Wendy; Stefanek, Michael

2005-07-01

This article describes 6 themes underlying the multiple presentations from the Basic and Applied Decision Making in Cancer Control meeting, held February 19-20, 2004. The following themes have important implications for research and practice linking basic decision-making research to cancer prevention and control: (a) Traditional decision-making theories fail to capture real-world decision making, (b) decision makers are often unable to predict future preferences, (c) preferences are often constructed on the spot and thus are influenced by situational cues, (d) decision makers often rely on feelings rather than beliefs when making a decision, (e) the perspective of the decision maker is critical in determining preferences, and (f) informed decision making may--or may not--yield the best decisions.