Uncertainty

USGS Publications Warehouse

Hunt, Randall J.

2012-01-01

Management decisions will often be directly informed by model predictions. However, we now know there can be no expectation of a single ‘true’ model; thus, model results are uncertain. Understandable reporting of underlying uncertainty provides necessary context to decision-makers, as model results are used for management decisions. This, in turn, forms a mechanism by which groundwater models inform a risk-management framework because uncertainty around a prediction provides the basis for estimating the probability or likelihood of some event occurring. Given that the consequences of management decisions vary, it follows that the extent of and resources devoted to an uncertainty analysis may depend on the consequences. For events with low impact, a qualitative, limited uncertainty analysis may be sufficient for informing a decision. For events with a high impact, on the other hand, the risks might be better assessed and associated decisions made using a more robust and comprehensive uncertainty analysis. The purpose of this chapter is to provide guidance on uncertainty analysis through discussion of concepts and approaches, which can vary from heuristic (i.e. the modeller’s assessment of prediction uncertainty based on trial and error and experience) to a comprehensive, sophisticated, statistics-based uncertainty analysis. Most of the material presented here is taken from Doherty et al. (2010) if not otherwise cited. Although the treatment here is necessarily brief, the reader can find citations for the source material and additional references within this chapter.

Applying Recursive Sensitivity Analysis to Multi-Criteria Decision Models to Reduce Bias in Defense Cyber Engineering Analysis

DTIC Science & Technology

2015-10-28

techniques such as regression analysis, correlation, and multicollinearity assessment to identify the change and error on the input to the model...between many of the independent or predictor variables, the issue of multicollinearity may arise [18]. VII. SUMMARY Accurate decisions concerning

The need for consumer behavior analysis in health care coverage decisions.

PubMed

Thompson, A M; Rao, C P

1990-01-01

Demographic analysis has been the primary form of analysis connected with health care coverage decisions. This paper reviews past demographic research and shows the need to use behavioral analyses for health care coverage policy decisions. A behavioral model based research study is presented and a case is made for integrated study into why consumers make health care coverage decisions.

Stakeholder perspectives on decision-analytic modeling frameworks to assess genetic services policy.

PubMed

Guzauskas, Gregory F; Garrison, Louis P; Stock, Jacquie; Au, Sylvia; Doyle, Debra Lochner; Veenstra, David L

2013-01-01

Genetic services policymakers and insurers often make coverage decisions in the absence of complete evidence of clinical utility and under budget constraints. We evaluated genetic services stakeholder opinions on the potential usefulness of decision-analytic modeling to inform coverage decisions, and asked them to identify genetic tests for decision-analytic modeling studies. We presented an overview of decision-analytic modeling to members of the Western States Genetic Services Collaborative Reimbursement Work Group and state Medicaid representatives and conducted directed content analysis and an anonymous survey to gauge their attitudes toward decision-analytic modeling. Participants also identified and prioritized genetic services for prospective decision-analytic evaluation. Participants expressed dissatisfaction with current processes for evaluating insurance coverage of genetic services. Some participants expressed uncertainty about their comprehension of decision-analytic modeling techniques. All stakeholders reported openness to using decision-analytic modeling for genetic services assessments. Participants were most interested in application of decision-analytic concepts to multiple-disorder testing platforms, such as next-generation sequencing and chromosomal microarray. Decision-analytic modeling approaches may provide a useful decision tool to genetic services stakeholders and Medicaid decision-makers.

Modeling Adversaries in Counterterrorism Decisions Using Prospect Theory.

PubMed

Merrick, Jason R W; Leclerc, Philip

2016-04-01

Counterterrorism decisions have been an intense area of research in recent years. Both decision analysis and game theory have been used to model such decisions, and more recently approaches have been developed that combine the techniques of the two disciplines. However, each of these approaches assumes that the attacker is maximizing its utility. Experimental research shows that human beings do not make decisions by maximizing expected utility without aid, but instead deviate in specific ways such as loss aversion or likelihood insensitivity. In this article, we modify existing methods for counterterrorism decisions. We keep expected utility as the defender's paradigm to seek for the rational decision, but we use prospect theory to solve for the attacker's decision to descriptively model the attacker's loss aversion and likelihood insensitivity. We study the effects of this approach in a critical decision, whether to screen containers entering the United States for radioactive materials. We find that the defender's optimal decision is sensitive to the attacker's levels of loss aversion and likelihood insensitivity, meaning that understanding such descriptive decision effects is important in making such decisions. © 2014 Society for Risk Analysis.

LANL Institutional Decision Support By Process Modeling and Analysis Group (AET-2)

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

Booth, Steven Richard

2016-04-04

AET-2 has expertise in process modeling, economics, business case analysis, risk assessment, Lean/Six Sigma tools, and decision analysis to provide timely decision support to LANS leading to continuous improvement. This capability is critical during the current tight budgetary environment as LANS pushes to identify potential areas of cost savings and efficiencies. An important arena is business systems and operations, where processes can impact most or all laboratory employees. Lab-wide efforts are needed to identify and eliminate inefficiencies to accomplish Director McMillan’s charge of “doing more with less.” LANS faces many critical and potentially expensive choices that require sound decision supportmore » to ensure success. AET-2 is available to provide this analysis support to expedite the decisions at hand.« less

Logit Estimation of a Gravity Model of the College Enrollment Decision.

ERIC Educational Resources Information Center

Leppel, Karen

1993-01-01

A study investigated the factors influencing students' decisions about attending a college to which they had been admitted. Logit analysis confirmed gravity model predictions that geographic distance and student ability would most influence the enrollment decision and found other variables, although affecting earlier stages of decision making, did…

A Critical Analysis of HRD Evaluation Models from a Decision-Making Perspective

ERIC Educational Resources Information Center

Holton, Elwood F., III; Naquin, Sharon

2005-01-01

HRD evaluation models are recommended for use by organizations to improve decisions made about HRD interventions. However, the organizational decision-making literature has been virtually ignored by evaluation researchers. In this article, we review the organizational decision-making literature and critically review HRD evaluation research through…

Seismic slope-performance analysis: from hazard map to decision support system

USGS Publications Warehouse

Miles, Scott B.; Keefer, David K.; Ho, Carlton L.

1999-01-01

In response to the growing recognition of engineers and decision-makers of the regional effects of earthquake-induced landslides, this paper presents a general approach to conducting seismic landslide zonation, based on the popular Newmark's sliding block analogy for modeling coherent landslides. Four existing models based on the sliding block analogy are compared. The comparison shows that the models forecast notably different levels of slope performance. Considering this discrepancy along with the limitations of static maps as a decision tool, a spatial decision support system (SDSS) for seismic landslide analysis is proposed, which will support investigations over multiple scales for any number of earthquake scenarios and input conditions. Most importantly, the SDSS will allow use of any seismic landslide analysis model and zonation approach. Developments associated with the SDSS will produce an object-oriented model for encapsulating spatial data, an object-oriented specification to allow construction of models using modular objects, and a direct-manipulation, dynamic user-interface that adapts to the particular seismic landslide model configuration.

An integrated, ethically driven environmental model of clinical decision making in emergency settings.

PubMed

Wolf, Lisa

2013-02-01

To explore the relationship between multiple variables within a model of critical thinking and moral reasoning. A quantitative descriptive correlational design using a purposive sample of 200 emergency nurses. Measured variables were accuracy in clinical decision-making, moral reasoning, perceived care environment, and demographics. Analysis was by bivariate correlation using Pearson's product-moment correlation coefficients, chi square and multiple linear regression analysis. The elements as identified in the integrated ethically-driven environmental model of clinical decision-making (IEDEM-CD) corrected depict moral reasoning and environment of care as factors significantly affecting accuracy in decision-making. The integrated, ethically driven environmental model of clinical decision making is a framework useful for predicting clinical decision making accuracy for emergency nurses in practice, with further implications in education, research and policy. A diagnostic and therapeutic framework for identifying and remediating individual and environmental challenges to accurate clinical decision making. © 2012, The Author. International Journal of Nursing Knowledge © 2012, NANDA International.

Criteria for assessing problem solving and decision making in complex environments

NASA Technical Reports Server (NTRS)

Orasanu, Judith

1993-01-01

Training crews to cope with unanticipated problems in high-risk, high-stress environments requires models of effective problem solving and decision making. Existing decision theories use the criteria of logical consistency and mathematical optimality to evaluate decision quality. While these approaches are useful under some circumstances, the assumptions underlying these models frequently are not met in dynamic time-pressured operational environments. Also, applying formal decision models is both labor and time intensive, a luxury often lacking in operational environments. Alternate approaches and criteria are needed. Given that operational problem solving and decision making are embedded in ongoing tasks, evaluation criteria must address the relation between those activities and satisfaction of broader task goals. Effectiveness and efficiency become relevant for judging reasoning performance in operational environments. New questions must be addressed: What is the relation between the quality of decisions and overall performance by crews engaged in critical high risk tasks? Are different strategies most effective for different types of decisions? How can various decision types be characterized? A preliminary model of decision types found in air transport environments will be described along with a preliminary performance model based on an analysis of 30 flight crews. The performance analysis examined behaviors that distinguish more and less effective crews (based on performance errors). Implications for training and system design will be discussed.

Decision Analysis in the U.S. Army’s Capabilties Needs Analysis: Applications of Decision Analysis Methods to Capabilities Resource Allocation and Capabilities Development Decisions

DTIC Science & Technology

2015-10-01

capability to meet the task to the standard under the condition, nothing more or less, else the funding is wasted . Also, that funding for the...bin to segregate gaps qualitatively before the gap value model determined preference among gaps within the bins. Computation of a gap’s...for communication, interpretation, or processing by humans or by automatic means (as it pertains to modeling and simulation). Delphi Method -- a

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.

Modeling Opponents in Adversarial Risk Analysis.

PubMed

Rios Insua, David; Banks, David; Rios, Jesus

2016-04-01

Adversarial risk analysis has been introduced as a framework to deal with risks derived from intentional actions of adversaries. The analysis supports one of the decisionmakers, who must forecast the actions of the other agents. Typically, this forecast must take account of random consequences resulting from the set of selected actions. The solution requires one to model the behavior of the opponents, which entails strategic thinking. The supported agent may face different kinds of opponents, who may use different rationality paradigms, for example, the opponent may behave randomly, or seek a Nash equilibrium, or perform level-k thinking, or use mirroring, or employ prospect theory, among many other possibilities. We describe the appropriate analysis for these situations, and also show how to model the uncertainty about the rationality paradigm used by the opponent through a Bayesian model averaging approach, enabling a fully decision-theoretic solution. We also show how as we observe an opponent's decision behavior, this approach allows learning about the validity of each of the rationality models used to predict his decision by computing the models' (posterior) probabilities, which can be understood as a measure of their validity. We focus on simultaneous decision making by two agents. © 2015 Society for Risk Analysis.

Using the weighted area under the net benefit curve for decision curve analysis.

PubMed

Talluri, Rajesh; Shete, Sanjay

2016-07-18

Risk prediction models have been proposed for various diseases and are being improved as new predictors are identified. A major challenge is to determine whether the newly discovered predictors improve risk prediction. Decision curve analysis has been proposed as an alternative to the area under the curve and net reclassification index to evaluate the performance of prediction models in clinical scenarios. The decision curve computed using the net benefit can evaluate the predictive performance of risk models at a given or range of threshold probabilities. However, when the decision curves for 2 competing models cross in the range of interest, it is difficult to identify the best model as there is no readily available summary measure for evaluating the predictive performance. The key deterrent for using simple measures such as the area under the net benefit curve is the assumption that the threshold probabilities are uniformly distributed among patients. We propose a novel measure for performing decision curve analysis. The approach estimates the distribution of threshold probabilities without the need of additional data. Using the estimated distribution of threshold probabilities, the weighted area under the net benefit curve serves as the summary measure to compare risk prediction models in a range of interest. We compared 3 different approaches, the standard method, the area under the net benefit curve, and the weighted area under the net benefit curve. Type 1 error and power comparisons demonstrate that the weighted area under the net benefit curve has higher power compared to the other methods. Several simulation studies are presented to demonstrate the improvement in model comparison using the weighted area under the net benefit curve compared to the standard method. The proposed measure improves decision curve analysis by using the weighted area under the curve and thereby improves the power of the decision curve analysis to compare risk prediction models in a clinical scenario.

Decision analysis in clinical cardiology: When is coronary angiography required in aortic stenosis

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

Georgeson, S.; Meyer, K.B.; Pauker, S.G.

1990-03-15

Decision analysis offers a reproducible, explicit approach to complex clinical decisions. It consists of developing a model, typically a decision tree, that separates choices from chances and that specifies and assigns relative values to outcomes. Sensitivity analysis allows exploration of alternative assumptions. Cost-effectiveness analysis shows the relation between dollars spent and improved health outcomes achieved. In a tutorial format, this approach is applied to the decision whether to perform coronary angiography in a patient who requires aortic valve replacement for critical aortic stenosis.

The BCD of response time analysis in experimental economics.

PubMed

Spiliopoulos, Leonidas; Ortmann, Andreas

2018-01-01

For decisions in the wild, time is of the essence. Available decision time is often cut short through natural or artificial constraints, or is impinged upon by the opportunity cost of time. Experimental economists have only recently begun to conduct experiments with time constraints and to analyze response time (RT) data, in contrast to experimental psychologists. RT analysis has proven valuable for the identification of individual and strategic decision processes including identification of social preferences in the latter case, model comparison/selection, and the investigation of heuristics that combine speed and performance by exploiting environmental regularities. Here we focus on the benefits, challenges, and desiderata of RT analysis in strategic decision making. We argue that unlocking the potential of RT analysis requires the adoption of process-based models instead of outcome-based models, and discuss how RT in the wild can be captured by time-constrained experiments in the lab. We conclude that RT analysis holds considerable potential for experimental economics, deserves greater attention as a methodological tool, and promises important insights on strategic decision making in naturally occurring environments.

Decision-Making Models in a Tunisian University: Towards a Framework for Analysis

ERIC Educational Resources Information Center

Khefacha, I.; Belkacem, L.

2014-01-01

This study investigates how decisions are made in Tunisian public higher education establishments. Some factors are identified as having a potentially significant impact on the odds that the decision-making process follows the characteristics of one of the most well known decision-making models: collegial, political, bureaucratic or anarchical…

Probabilistic sensitivity analysis incorporating the bootstrap: an example comparing treatments for the eradication of Helicobacter pylori.

PubMed

Pasta, D J; Taylor, J L; Henning, J M

1999-01-01

Decision-analytic models are frequently used to evaluate the relative costs and benefits of alternative therapeutic strategies for health care. Various types of sensitivity analysis are used to evaluate the uncertainty inherent in the models. Although probabilistic sensitivity analysis is more difficult theoretically and computationally, the results can be much more powerful and useful than deterministic sensitivity analysis. The authors show how a Monte Carlo simulation can be implemented using standard software to perform a probabilistic sensitivity analysis incorporating the bootstrap. The method is applied to a decision-analytic model evaluating the cost-effectiveness of Helicobacter pylori eradication. The necessary steps are straightforward and are described in detail. The use of the bootstrap avoids certain difficulties encountered with theoretical distributions. The probabilistic sensitivity analysis provided insights into the decision-analytic model beyond the traditional base-case and deterministic sensitivity analyses and should become the standard method for assessing sensitivity.

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

O'Malley, Daniel; Vesselinov, Velimir V.

MADSpython (Model analysis and decision support tools in Python) is a code in Python that streamlines the process of using data and models for analysis and decision support using the code MADS. MADS is open-source code developed at LANL and written in C/C++ (MADS; http://mads.lanl.gov; LA-CC-11-035). MADS can work with external models of arbitrary complexity as well as built-in models of flow and transport in porous media. The Python scripts in MADSpython facilitate the generation of input and output file needed by MADS as wells as the external simulators which include FEHM and PFLOTRAN. MADSpython enables a number of data-more » and model-based analyses including model calibration, sensitivity analysis, uncertainty quantification, and decision analysis. MADSpython will be released under GPL V3 license. MADSpython will be distributed as a Git repo at gitlab.com and github.com. MADSpython manual and documentation will be posted at http://madspy.lanl.gov.« less

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.

Analysis of strength-of-preference measures in dichotomous choice models

Treesearch

Donald F. Dennis; Peter Newman; Robert Manning

2008-01-01

Choice models are becoming increasingly useful for soliciting and analyzing multiple objective decisions faced by recreation managers and others interested in decisions involving natural resources. Choice models are used to estimate relative values for multiple aspects of natural resource management, not individually but within the context of other relevant decision...

Applications of decision analysis and related techniques to industrial engineering problems at KSC

NASA Technical Reports Server (NTRS)

Evans, Gerald W.

1995-01-01

This report provides: (1) a discussion of the origination of decision analysis problems (well-structured problems) from ill-structured problems; (2) a review of the various methodologies and software packages for decision analysis and related problem areas; (3) a discussion of how the characteristics of a decision analysis problem affect the choice of modeling methodologies, thus providing a guide as to when to choose a particular methodology; and (4) examples of applications of decision analysis to particular problems encountered by the IE Group at KSC. With respect to the specific applications at KSC, particular emphasis is placed on the use of the Demos software package (Lumina Decision Systems, 1993).

A pilot level decision analysis of thermionic reactor development strategy for nuclear electric propulsion

NASA Technical Reports Server (NTRS)

Menke, M. M.; Judd, B. R.

1973-01-01

The development policy for thermionic reactors to provide electric propulsion and power for space exploration was analyzed to develop a logical procedure for selecting development alternatives that reflect the technical feasibility, JPL/NASA project objectives, and the economic environment of the project. The partial evolution of a decision model from the underlying philosophy of decision analysis to a deterministic pilot phase is presented, and the general manner in which this decision model can be employed to examine propulsion development alternatives is illustrated.

The use of decision analysis to examine ethical decision making by critical care nurses.

PubMed

Hughes, K K; Dvorak, E M

1997-01-01

To examine the extent to which critical care staff nurses make ethical decisions that coincide with those recommended by a decision analytic model. Nonexperimental, ex post facto. Midwestern university-affiliated 500 bed tertiary care medical center. One hundred critical care staff nurses randomly selected from seven critical care units. Complete responses were obtained from 82 nurses (for a final response rate of 82%). The dependent variable--consistent decision making--was measured as staff nurses' abilities to make ethical decisions that coincided with those prescribed by the decision model. Subjects completed two instruments, the Ethical Decision Analytic Model, a computer-administered instrument designed to measure staff nurses' abilities to make consistent decisions about a chemically-impaired colleague; and a Background Inventory. The results indicate marked consensus among nurses when informal methods were used. However, there was little consistency between the nurses' informal decisions and those recommended by the decision analytic model. Although 50% (n = 41) of all nurses chose a course of action that coincided with the model's least optimal alternative, few nurses agreed with the model as to the most optimal course of action. The findings also suggest that consistency was unrelated (p > 0.05) to the nurses' educational background or years of clinical experience; that most subjects reported receiving little or no education in decision making during their basic nursing education programs; but that exposure to decision-making strategies was related to years of nursing experience (p < 0.05). The findings differ from related studies that have found a moderate degree of consistency between nurses and decision analytic models for strictly clinical decision tasks, especially when those tasks were less complex. However, the findings partially coincide with other findings that decision analysis may not be particularly well-suited to the critical care environment. Additional research is needed to determine whether critical care nurses use the same decision-making methods as do other nurses; and to clarify the effects of decision task (clinical versus ethical) on nurses' decision making. It should not be assumed that methods used to study nurses' clinical decision making are applicable for all nurses or all types of decisions, including ethical decisions.

Effects of Practice on Task Architecture: Combined Evidence from Interference Experiments and Random-Walk Models of Decision Making

ERIC Educational Resources Information Center

Kamienkowski, Juan E.; Pashler, Harold; Dehaene, Stanislas; Sigman, Mariano

2011-01-01

Does extensive practice reduce or eliminate central interference in dual-task processing? We explored the reorganization of task architecture with practice by combining interference analysis (delays in dual-task experiment) and random-walk models of decision making (measuring the decision and non-decision contributions to RT). The main delay…

A novel computer based expert decision making model for prostate cancer disease management.

PubMed

Richman, Martin B; Forman, Ernest H; Bayazit, Yildirim; Einstein, Douglas B; Resnick, Martin I; Stovsky, Mark D

2005-12-01

We propose a strategic, computer based, prostate cancer decision making model based on the analytic hierarchy process. We developed a model that improves physician-patient joint decision making and enhances the treatment selection process by making this critical decision rational and evidence based. Two groups (patient and physician-expert) completed a clinical study comparing an initial disease management choice with the highest ranked option generated by the computer model. Participants made pairwise comparisons to derive priorities for the objectives and subobjectives related to the disease management decision. The weighted comparisons were then applied to treatment options to yield prioritized rank lists that reflect the likelihood that a given alternative will achieve the participant treatment goal. Aggregate data were evaluated by inconsistency ratio analysis and sensitivity analysis, which assessed the influence of individual objectives and subobjectives on the final rank list of treatment options. Inconsistency ratios less than 0.05 were reliably generated, indicating that judgments made within the model were mathematically rational. The aggregate prioritized list of treatment options was tabulated for the patient and physician groups with similar outcomes for the 2 groups. Analysis of the major defining objectives in the treatment selection decision demonstrated the same rank order for the patient and physician groups with cure, survival and quality of life being more important than controlling cancer, preventing major complications of treatment, preventing blood transfusion complications and limiting treatment cost. Analysis of subobjectives, including quality of life and sexual dysfunction, produced similar priority rankings for the patient and physician groups. Concordance between initial treatment choice and the highest weighted model option differed between the groups with the patient group having 59% concordance and the physician group having only 42% concordance. This study successfully validated the usefulness of a computer based prostate cancer management decision making model to produce individualized, rational, clinically appropriate disease management decisions without physician bias.

An experiment with interactive planning models

NASA Technical Reports Server (NTRS)

Beville, J.; Wagner, J. H.; Zannetos, Z. S.

1970-01-01

Experiments on decision making in planning problems are described. Executives were tested in dealing with capital investments and competitive pricing decisions under conditions of uncertainty. A software package, the interactive risk analysis model system, was developed, and two controlled experiments were conducted. It is concluded that planning models can aid management, and predicted uses of the models are as a central tool, as an educational tool, to improve consistency in decision making, to improve communications, and as a tool for consensus decision making.

Lending Officers' Decisions to Recommend Innovative Agricultural Technology.

ERIC Educational Resources Information Center

McIntosh, Wm. Alex; Zey-Ferrell, Mary

1986-01-01

Path analysis examines an analytical model of decision making by lending officers of 211 Texas banks when recommending agricultural technology to farmer-clients. Model analyzes effects of loan officers' ascribed/achieved personal characteristics and perceptions of organizational constraints during three stages of decision process: using…

A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: from cognitive maps to agent-based models.

PubMed

Elsawah, Sondoss; Guillaume, Joseph H A; Filatova, Tatiana; Rook, Josefine; Jakeman, Anthony J

2015-03-15

This paper aims to contribute to developing better ways for incorporating essential human elements in decision making processes for modelling of complex socio-ecological systems. It presents a step-wise methodology for integrating perceptions of stakeholders (qualitative) into formal simulation models (quantitative) with the ultimate goal of improving understanding and communication about decision making in complex socio-ecological systems. The methodology integrates cognitive mapping and agent based modelling. It cascades through a sequence of qualitative/soft and numerical methods comprising: (1) Interviews to elicit mental models; (2) Cognitive maps to represent and analyse individual and group mental models; (3) Time-sequence diagrams to chronologically structure the decision making process; (4) All-encompassing conceptual model of decision making, and (5) computational (in this case agent-based) Model. We apply the proposed methodology (labelled ICTAM) in a case study of viticulture irrigation in South Australia. Finally, we use strengths-weakness-opportunities-threats (SWOT) analysis to reflect on the methodology. Results show that the methodology leverages the use of cognitive mapping to capture the richness of decision making and mental models, and provides a combination of divergent and convergent analysis methods leading to the construction of an Agent Based Model. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

Decision analysis of emergency ventilation and evacuation strategies against suddenly released contaminant indoors by considering the uncertainty of source locations.

PubMed

Cai, Hao; Long, Weiding; Li, Xianting; Kong, Lingjuan; Xiong, Shuang

2010-06-15

In case hazardous contaminants are suddenly released indoors, the prompt and proper emergency responses are critical to protect occupants. This paper aims to provide a framework for determining the optimal combination of ventilation and evacuation strategies by considering the uncertainty of source locations. The certainty of source locations is classified as complete certainty, incomplete certainty, and complete uncertainty to cover all the possible situations. According to this classification, three types of decision analysis models are presented. A new concept, efficiency factor of contaminant source (EFCS), is incorporated in these models to evaluate the payoffs of the ventilation and evacuation strategies. A procedure of decision-making based on these models is proposed and demonstrated by numerical studies of one hundred scenarios with ten ventilation modes, two evacuation modes, and five source locations. The results show that the models can be useful to direct the decision analysis of both the ventilation and evacuation strategies. In addition, the certainty of the source locations has an important effect on the outcomes of the decision-making. Copyright 2010 Elsevier B.V. All rights reserved.

An EGR performance evaluation and decision-making approach based on grey theory and grey entropy analysis

PubMed Central

2018-01-01

Exhaust gas recirculation (EGR) is one of the main methods of reducing NOX emissions and has been widely used in marine diesel engines. This paper proposes an optimized comprehensive assessment method based on multi-objective grey situation decision theory, grey relation theory and grey entropy analysis to evaluate the performance and optimize rate determination of EGR, which currently lack clear theoretical guidance. First, multi-objective grey situation decision theory is used to establish the initial decision-making model according to the main EGR parameters. The optimal compromise between diesel engine combustion and emission performance is transformed into a decision-making target weight problem. After establishing the initial model and considering the characteristics of EGR under different conditions, an optimized target weight algorithm based on grey relation theory and grey entropy analysis is applied to generate the comprehensive evaluation and decision-making model. Finally, the proposed method is successfully applied to a TBD234V12 turbocharged diesel engine, and the results clearly illustrate the feasibility of the proposed method for providing theoretical support and a reference for further EGR optimization. PMID:29377956

An EGR performance evaluation and decision-making approach based on grey theory and grey entropy analysis.

PubMed

Zu, Xianghuan; Yang, Chuanlei; Wang, Hechun; Wang, Yinyan

2018-01-01

Exhaust gas recirculation (EGR) is one of the main methods of reducing NOX emissions and has been widely used in marine diesel engines. This paper proposes an optimized comprehensive assessment method based on multi-objective grey situation decision theory, grey relation theory and grey entropy analysis to evaluate the performance and optimize rate determination of EGR, which currently lack clear theoretical guidance. First, multi-objective grey situation decision theory is used to establish the initial decision-making model according to the main EGR parameters. The optimal compromise between diesel engine combustion and emission performance is transformed into a decision-making target weight problem. After establishing the initial model and considering the characteristics of EGR under different conditions, an optimized target weight algorithm based on grey relation theory and grey entropy analysis is applied to generate the comprehensive evaluation and decision-making model. Finally, the proposed method is successfully applied to a TBD234V12 turbocharged diesel engine, and the results clearly illustrate the feasibility of the proposed method for providing theoretical support and a reference for further EGR optimization.

Decision Modeling for Socio-Cultural Data

DTIC Science & Technology

2011-02-01

REFERENCES [1] Malczewski, J. (1999) GIS and Multicriteria Decision Analysis . John Wiley and Sons, New York. [2] Ehrgott, M., and Gandibleux, X. (Eds...up, nonexclusive, irrevocable worldwide license to use , modify, reproduce, release, perform, display, or disclose the work by or on behalf of the...criteria decision analysis (MCDA), into a geospatial environment to support decision making for campaign management. Our development approach supports

Characterizing Decision-Analysis Performances of Risk Prediction Models Using ADAPT Curves.

PubMed

Lee, Wen-Chung; Wu, Yun-Chun

2016-01-01

The area under the receiver operating characteristic curve is a widely used index to characterize the performance of diagnostic tests and prediction models. However, the index does not explicitly acknowledge the utilities of risk predictions. Moreover, for most clinical settings, what counts is whether a prediction model can guide therapeutic decisions in a way that improves patient outcomes, rather than to simply update probabilities.Based on decision theory, the authors propose an alternative index, the "average deviation about the probability threshold" (ADAPT).An ADAPT curve (a plot of ADAPT value against the probability threshold) neatly characterizes the decision-analysis performances of a risk prediction model.Several prediction models can be compared for their ADAPT values at a chosen probability threshold, for a range of plausible threshold values, or for the whole ADAPT curves. This should greatly facilitate the selection of diagnostic tests and prediction models.

Assessing the Impact of Financial Aid Offers on Enrollment Decisions.

ERIC Educational Resources Information Center

Somers, Patricia A.; St. John, Edward P.

1993-01-01

A study tested a model for assessing the impact of financial aid offers on 2,558 accepted students' college enrollment decisions. The analysis demonstrates that financial aid strategies have a substantial influence on enrollment and the systematic analysis of student enrollment decisions can help institutional administrators refine their financing…

Women's Values and Preferences for Thromboprophylaxis during Pregnancy: A Comparison of Direct-choice and Decision Analysis using Patient Specific Utilities

PubMed Central

Eckman, Mark H.; Alonso-Coello, Pablo; Guyatt, Gordon H.; Ebrahim, Shanil; Tikkinen, Kari A.O.; Lopes, Luciane Cruz; Neumann, Ignacio; McDonald, Sarah D.; Zhang, Yuqing; Zhou, Qi; Akl, Elie A.; Jacobsen, Ann Flem; Santamaría, Amparo; Annichino-Bizzacchi, Joyce Maria; Bitar, Wael; Sandset, Per Morten; Bates, Shannon M.

2016-01-01

Background Women with a history of venous thromboembolism (VTE) have an increased recurrence risk during pregnancy. Low molecular weight heparin (LMWH) reduces this risk, but is costly, burdensome, and may increase risk of bleeding. The decision to start thromboprophylaxis during pregnancy is sensitive to women's values and preferences. Our objective was to compare women's choices using a holistic approach in which they were presented all of the relevant information (direct-choice) versus a personalized decision analysis in which a mathematical model incorporated their preferences and VTE risk to make a treatment recommendation. Methods Multicenter, international study. Structured interviews were on women with a history of VTE who were pregnant, planning, or considering pregnancy. Women indicated their willingness to receive thromboprophylaxis based on scenarios using personalized estimates of VTE recurrence and bleeding risks. We also obtained women's values for health outcomes using a visual analog scale. We performed individualized decision analyses for each participant and compared model recommendations to decisions made when presented with the direct-choice exercise. Results Of the 123 women in the study, the decision model recommended LMWH for 51 women and recommended against LMWH for 72 women. 12% (6/51) of women for whom the decision model recommended thromboprophylaxis chose not to take LMWH; 72% (52/72) of women for whom the decision model recommended against thromboprophylaxis chose LMWH. Conclusions We observed a high degree of discordance between decisions in the direct-choice exercise and decision model recommendations. Although which approach best captures individuals’ true values remains uncertain, personalized decision support tools presenting results based on personalized risks and values may improve decision making. PMID:26033397

Women's values and preferences for thromboprophylaxis during pregnancy: a comparison of direct-choice and decision analysis using patient specific utilities.

PubMed

Eckman, Mark H; Alonso-Coello, Pablo; Guyatt, Gordon H; Ebrahim, Shanil; Tikkinen, Kari A O; Lopes, Luciane Cruz; Neumann, Ignacio; McDonald, Sarah D; Zhang, Yuqing; Zhou, Qi; Akl, Elie A; Jacobsen, Ann Flem; Santamaría, Amparo; Annichino-Bizzacchi, Joyce Maria; Bitar, Wael; Sandset, Per Morten; Bates, Shannon M

2015-08-01

Women with a history of venous thromboembolism (VTE) have an increased recurrence risk during pregnancy. Low molecular weight heparin (LMWH) reduces this risk, but is costly, burdensome, and may increase risk of bleeding. The decision to start thromboprophylaxis during pregnancy is sensitive to women's values and preferences. Our objective was to compare women's choices using a holistic approach in which they were presented all of the relevant information (direct-choice) versus a personalized decision analysis in which a mathematical model incorporated their preferences and VTE risk to make a treatment recommendation. Multicenter, international study. Structured interviews were on women with a history of VTE who were pregnant, planning, or considering pregnancy. Women indicated their willingness to receive thromboprophylaxis based on scenarios using personalized estimates of VTE recurrence and bleeding risks. We also obtained women's values for health outcomes using a visual analog scale. We performed individualized decision analyses for each participant and compared model recommendations to decisions made when presented with the direct-choice exercise. Of the 123 women in the study, the decision model recommended LMWH for 51 women and recommended against LMWH for 72 women. 12% (6/51) of women for whom the decision model recommended thromboprophylaxis chose not to take LMWH; 72% (52/72) of women for whom the decision model recommended against thromboprophylaxis chose LMWH. We observed a high degree of discordance between decisions in the direct-choice exercise and decision model recommendations. Although which approach best captures individuals' true values remains uncertain, personalized decision support tools presenting results based on personalized risks and values may improve decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using Consumer Behavior and Decision Models to Aid Students in Choosing a Major.

ERIC Educational Resources Information Center

Kaynama, Shohreh A.; Smith, Louise W.

1996-01-01

A study found that using consumer behavior and decision models to guide students to a major can be useful and enjoyable for students. Students consider many of the basic parameters through multi-attribute and decision-analysis models, so time with professors, who were found to be the most influential group, can be used for more individual and…

Patient or physician preferences for decision analysis: the prenatal genetic testing decision.

PubMed

Heckerling, P S; Verp, M S; Albert, N

1999-01-01

The choice between amniocentesis and chorionic villus sampling for prenatal genetic testing involves tradeoffs of the benefits and risks of the tests. Decision analysis is a method of explicitly weighing such tradeoffs. The authors examined the relationship between prenatal test choices made by patients and the choices prescribed by decision-analytic models based on their preferences, and separate models based on the preferences of their physicians. Preferences were assessed using written scenarios describing prenatal testing outcomes, and were recorded on linear rating scales. After adjustment for sociodemographic and obstetric confounders, test choice was significantly associated with the choice of decision models based on patient preferences (odds ratio 4.44; Cl, 2.53 to 7.78), but not with the choice of models based on the preferences of the physicians (odds ratio 1.60; Cl, 0.79 to 3.26). Agreement between decision analyses based on patient preferences and on physician preferences was little better than chance (kappa = 0.085+/-0.063). These results were robust both to changes in the decision-analytic probabilities and to changes in the model structure itself to simulate non-expected utility decision rules. The authors conclude that patient but not physician preferences, incorporated in decision models, correspond to the choice of amniocentesis or chorionic villus sampling made by the patient. Nevertheless, because patient preferences were assessed after referral for genetic testing, prospective preference-assessment studies will be necessary to confirm this association.

Multicriteria decision analysis: Overview and implications for environmental decision making

USGS Publications Warehouse

Hermans, Caroline M.; Erickson, Jon D.; Erickson, Jon D.; Messner, Frank; Ring, Irene

2007-01-01

Environmental decision making involving multiple stakeholders can benefit from the use of a formal process to structure stakeholder interactions, leading to more successful outcomes than traditional discursive decision processes. There are many tools available to handle complex decision making. Here we illustrate the use of a multicriteria decision analysis (MCDA) outranking tool (PROMETHEE) to facilitate decision making at the watershed scale, involving multiple stakeholders, multiple criteria, and multiple objectives. We compare various MCDA methods and their theoretical underpinnings, examining methods that most realistically model complex decision problems in ways that are understandable and transparent to stakeholders.

Decision-making in healthcare: a practical application of partial least square path modelling to coverage of newborn screening programmes.

PubMed

Fischer, Katharina E

2012-08-02

Decision-making in healthcare is complex. Research on coverage decision-making has focused on comparative studies for several countries, statistical analyses for single decision-makers, the decision outcome and appraisal criteria. Accounting for decision processes extends the complexity, as they are multidimensional and process elements need to be regarded as latent constructs (composites) that are not observed directly. The objective of this study was to present a practical application of partial least square path modelling (PLS-PM) to evaluate how it offers a method for empirical analysis of decision-making in healthcare. Empirical approaches that applied PLS-PM to decision-making in healthcare were identified through a systematic literature search. PLS-PM was used as an estimation technique for a structural equation model that specified hypotheses between the components of decision processes and the reasonableness of decision-making in terms of medical, economic and other ethical criteria. The model was estimated for a sample of 55 coverage decisions on the extension of newborn screening programmes in Europe. Results were evaluated by standard reliability and validity measures for PLS-PM. After modification by dropping two indicators that showed poor measures in the measurement models' quality assessment and were not meaningful for newborn screening, the structural equation model estimation produced plausible results. The presence of three influences was supported: the links between both stakeholder participation or transparency and the reasonableness of decision-making; and the effect of transparency on the degree of scientific rigour of assessment. Reliable and valid measurement models were obtained to describe the composites of 'transparency', 'participation', 'scientific rigour' and 'reasonableness'. The structural equation model was among the first applications of PLS-PM to coverage decision-making. It allowed testing of hypotheses in situations where there are links between several non-observable constructs. PLS-PM was compatible in accounting for the complexity of coverage decisions to obtain a more realistic perspective for empirical analysis. The model specification can be used for hypothesis testing by using larger sample sizes and for data in the full domain of health technologies.

Issue a Boil-Water Advisory or Wait for Definitive Information? A Decision Analysis

PubMed Central

Wagner, Michael M.; Wallstrom, Garrick L.; Onisko, Agnieszka

2005-01-01

Objective Study the decision to issue a boil-water advisory in response to a spike in sales of diarrhea remedies or wait 72 hours for the results of definitive testing of water and people. Methods Decision analysis. Results In the base-case analysis, the optimal decision is test-and-wait. If the cost of issuing a boil-water advisory is less than 13.92 cents per person per day, the optimal decision is to issue the boil-water advisory immediately. Conclusions Decisions based on surveillance data that are suggestive but not conclusive about the existence of a disease outbreak can be modeled. PMID:16779145

An improved hybrid multi-criteria/multidimensional model for strategic industrial location selection: Casablanca industrial zones as a case study.

PubMed

Boutkhoum, Omar; Hanine, Mohamed; Agouti, Tarik; Tikniouine, Abdessadek

2015-01-01

In this paper, we examine the issue of strategic industrial location selection in uncertain decision making environments for implanting new industrial corporation. In fact, the industrial location issue is typically considered as a crucial factor in business research field which is related to many calculations about natural resources, distributors, suppliers, customers, and most other things. Based on the integration of environmental, economic and social decisive elements of sustainable development, this paper presents a hybrid decision making model combining fuzzy multi-criteria analysis with analytical capabilities that OLAP systems can provide for successful and optimal industrial location selection. The proposed model mainly consists in three stages. In the first stage, a decision-making committee has been established to identify the evaluation criteria impacting the location selection process. In the second stage, we develop fuzzy AHP software based on the extent analysis method to assign the importance weights to the selected criteria, which allows us to model the linguistic vagueness, ambiguity, and incomplete knowledge. In the last stage, OLAP analysis integrated with multi-criteria analysis employs these weighted criteria as inputs to evaluate, rank and select the strategic industrial location for implanting new business corporation in the region of Casablanca, Morocco. Finally, a sensitivity analysis is performed to evaluate the impact of criteria weights and the preferences given by decision makers on the final rankings of strategic industrial locations.

Scalable methodology for large scale building energy improvement: Relevance of calibration in model-based retrofit analysis

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

Heo, Yeonsook; Augenbroe, Godfried; Graziano, Diane

2015-05-01

The increasing interest in retrofitting of existing buildings is motivated by the need to make a major contribution to enhancing building energy efficiency and reducing energy consumption and CO2 emission by the built environment. This paper examines the relevance of calibration in model-based analysis to support decision-making for energy and carbon efficiency retrofits of individual buildings and portfolios of buildings. The authors formulate a set of real retrofit decision-making situations and evaluate the role of calibration by using a case study that compares predictions and decisions from an uncalibrated model with those of a calibrated model. The case study illustratesmore » both the mechanics and outcomes of a practical alternative to the expert- and time-intense application of dynamic energy simulation models for large-scale retrofit decision-making under uncertainty.« less

Development of an evidence-based decision pathway for vestibular schwannoma treatment options.

PubMed

Linkov, Faina; Valappil, Benita; McAfee, Jacob; Goughnour, Sharon L; Hildrew, Douglas M; McCall, Andrew A; Linkov, Igor; Hirsch, Barry; Snyderman, Carl

To integrate multiple sources of clinical information with patient feedback to build evidence-based decision support model to facilitate treatment selection for patients suffering from vestibular schwannomas (VS). This was a mixed methods study utilizing focus group and survey methodology to solicit feedback on factors important for making treatment decisions among patients. Two 90-minute focus groups were conducted by an experienced facilitator. Previously diagnosed VS patients were recruited by clinical investigators at the University of Pittsburgh Medical Center (UPMC). Classical content analysis was used for focus group data analysis. Providers were recruited from practices within the UPMC system and were surveyed using Delphi methods. This information can provide a basis for multi-criteria decision analysis (MCDA) framework to develop a treatment decision support system for patients with VS. Eight themes were derived from these data (focus group + surveys): doctor/health care system, side effects, effectiveness of treatment, anxiety, mortality, family/other people, quality of life, and post-operative symptoms. These data, as well as feedback from physicians were utilized in building a multi-criteria decision model. The study illustrated steps involved in the development of a decision support model that integrates evidence-based data and patient values to select treatment alternatives. Studies focusing on the actual development of the decision support technology for this group of patients are needed, as decisions are highly multifactorial. Such tools have the potential to improve decision making for complex medical problems with alternate treatment pathways. Copyright © 2016 Elsevier Inc. All rights reserved.

The Success of Linear Bootstrapping Models: Decision Domain-, Expertise-, and Criterion-Specific Meta-Analysis

PubMed Central

Kaufmann, Esther; Wittmann, Werner W.

2016-01-01

The success of bootstrapping or replacing a human judge with a model (e.g., an equation) has been demonstrated in Paul Meehl’s (1954) seminal work and bolstered by the results of several meta-analyses. To date, however, analyses considering different types of meta-analyses as well as the potential dependence of bootstrapping success on the decision domain, the level of expertise of the human judge, and the criterion for what constitutes an accurate decision have been missing from the literature. In this study, we addressed these research gaps by conducting a meta-analysis of lens model studies. We compared the results of a traditional (bare-bones) meta-analysis with findings of a meta-analysis of the success of bootstrap models corrected for various methodological artifacts. In line with previous studies, we found that bootstrapping was more successful than human judgment. Furthermore, bootstrapping was more successful in studies with an objective decision criterion than in studies with subjective or test score criteria. We did not find clear evidence that the success of bootstrapping depended on the decision domain (e.g., education or medicine) or on the judge’s level of expertise (novice or expert). Correction of methodological artifacts increased the estimated success of bootstrapping, suggesting that previous analyses without artifact correction (i.e., traditional meta-analyses) may have underestimated the value of bootstrapping models. PMID:27327085

Decision curve analysis assessing the clinical benefit of NMP22 in the detection of bladder cancer: secondary analysis of a prospective trial.

PubMed

Barbieri, Christopher E; Cha, Eugene K; Chromecki, Thomas F; Dunning, Allison; Lotan, Yair; Svatek, Robert S; Scherr, Douglas S; Karakiewicz, Pierre I; Sun, Maxine; Mazumdar, Madhu; Shariat, Shahrokh F

2012-03-01

• To employ decision curve analysis to determine the impact of nuclear matrix protein 22 (NMP22) on clinical decision making in the detection of bladder cancer using data from a prospective trial. • The study included 1303 patients at risk for bladder cancer who underwent cystoscopy, urine cytology and measurement of urinary NMP22 levels. • We constructed several prediction models to estimate risk of bladder cancer. The base model was generated using patient characteristics (age, gender, race, smoking and haematuria); cytology and NMP22 were added to the base model to determine effects on predictive accuracy. • Clinical net benefit was calculated by summing the benefits and subtracting the harms and weighting these by the threshold probability at which a patient or clinician would opt for cystoscopy. • In all, 72 patients were found to have bladder cancer (5.5%). In univariate analyses, NMP22 was the strongest predictor of bladder cancer presence (predictive accuracy 71.3%), followed by age (67.5%) and cytology (64.3%). • In multivariable prediction models, NMP22 improved the predictive accuracy of the base model by 8.2% (area under the curve 70.2-78.4%) and of the base model plus cytology by 4.2% (area under the curve 75.9-80.1%). • Decision curve analysis revealed that adding NMP22 to other models increased clinical benefit, particularly at higher threshold probabilities. • NMP22 is a strong, independent predictor of bladder cancer. • Addition of NMP22 improves the accuracy of standard predictors by a statistically and clinically significant margin. • Decision curve analysis suggests that integration of NMP22 into clinical decision making helps avoid unnecessary cystoscopies, with minimal increased risk of missing a cancer. © 2011 THE AUTHORS. BJU INTERNATIONAL © 2011 BJU INTERNATIONAL.

Modeling as a Decision-Making Process

ERIC Educational Resources Information Center

Bleiler-Baxter, Sarah K.; Stephens, D. Christopher; Baxter, Wesley A.; Barlow, Angela T.

2017-01-01

The goal in this article is to support teachers in better understanding what it means to model with mathematics by focusing on three key decision-making processes: Simplification, Relationship Mapping, and Situation Analysis. The authors use the Theme Park task to help teachers develop a vision of how students engage in these three decision-making…

Ethics and rationality in information-enriched decisions: A model for technical communication

NASA Astrophysics Data System (ADS)

Dressel, S. B.; Carlson, P.; Killingsworth, M. J.

1993-12-01

In a technological culture, information has a crucial impact upon decisions, but exactly how information plays into decisions is not always clear. Decisions that are effective, efficient, and ethical must be rational. That is, we must be able to determine and present good reasons for our actions. The topic in this paper is how information relates to good reasons and thereby affects the best decisions. A brief sketch of a model for decision-making, is presented which offers a synthesis of theoretical approaches to argument and to information analysis. Then the model is applied to a brief hypothetical case. The main purpose is to put the model before an interested audience in hopes of stimulating discussion and further research.

Decision-analytic modeling studies: An overview for clinicians using multiple myeloma as an example.

PubMed

Rochau, U; Jahn, B; Qerimi, V; Burger, E A; Kurzthaler, C; Kluibenschaedl, M; Willenbacher, E; Gastl, G; Willenbacher, W; Siebert, U

2015-05-01

The purpose of this study was to provide a clinician-friendly overview of decision-analytic models evaluating different treatment strategies for multiple myeloma (MM). We performed a systematic literature search to identify studies evaluating MM treatment strategies using mathematical decision-analytic models. We included studies that were published as full-text articles in English, and assessed relevant clinical endpoints, and summarized methodological characteristics (e.g., modeling approaches, simulation techniques, health outcomes, perspectives). Eleven decision-analytic modeling studies met our inclusion criteria. Five different modeling approaches were adopted: decision-tree modeling, Markov state-transition modeling, discrete event simulation, partitioned-survival analysis and area-under-the-curve modeling. Health outcomes included survival, number-needed-to-treat, life expectancy, and quality-adjusted life years. Evaluated treatment strategies included novel agent-based combination therapies, stem cell transplantation and supportive measures. Overall, our review provides a comprehensive summary of modeling studies assessing treatment of MM and highlights decision-analytic modeling as an important tool for health policy decision making. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A multicriteria decision analysis model and risk assessment framework for carbon capture and storage.

PubMed

Humphries Choptiany, John Michael; Pelot, Ronald

2014-09-01

Multicriteria decision analysis (MCDA) has been applied to various energy problems to incorporate a variety of qualitative and quantitative criteria, usually spanning environmental, social, engineering, and economic fields. MCDA and associated methods such as life-cycle assessments and cost-benefit analysis can also include risk analysis to address uncertainties in criteria estimates. One technology now being assessed to help mitigate climate change is carbon capture and storage (CCS). CCS is a new process that captures CO2 emissions from fossil-fueled power plants and injects them into geological reservoirs for storage. It presents a unique challenge to decisionmakers (DMs) due to its technical complexity, range of environmental, social, and economic impacts, variety of stakeholders, and long time spans. The authors have developed a risk assessment model using a MCDA approach for CCS decisions such as selecting between CO2 storage locations and choosing among different mitigation actions for reducing risks. The model includes uncertainty measures for several factors, utility curve representations of all variables, Monte Carlo simulation, and sensitivity analysis. This article uses a CCS scenario example to demonstrate the development and application of the model based on data derived from published articles and publicly available sources. The model allows high-level DMs to better understand project risks and the tradeoffs inherent in modern, complex energy decisions. © 2014 Society for Risk Analysis.

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.

Decision-making in healthcare: a practical application of partial least square path modelling to coverage of newborn screening programmes

PubMed Central

2012-01-01

Background Decision-making in healthcare is complex. Research on coverage decision-making has focused on comparative studies for several countries, statistical analyses for single decision-makers, the decision outcome and appraisal criteria. Accounting for decision processes extends the complexity, as they are multidimensional and process elements need to be regarded as latent constructs (composites) that are not observed directly. The objective of this study was to present a practical application of partial least square path modelling (PLS-PM) to evaluate how it offers a method for empirical analysis of decision-making in healthcare. Methods Empirical approaches that applied PLS-PM to decision-making in healthcare were identified through a systematic literature search. PLS-PM was used as an estimation technique for a structural equation model that specified hypotheses between the components of decision processes and the reasonableness of decision-making in terms of medical, economic and other ethical criteria. The model was estimated for a sample of 55 coverage decisions on the extension of newborn screening programmes in Europe. Results were evaluated by standard reliability and validity measures for PLS-PM. Results After modification by dropping two indicators that showed poor measures in the measurement models’ quality assessment and were not meaningful for newborn screening, the structural equation model estimation produced plausible results. The presence of three influences was supported: the links between both stakeholder participation or transparency and the reasonableness of decision-making; and the effect of transparency on the degree of scientific rigour of assessment. Reliable and valid measurement models were obtained to describe the composites of ‘transparency’, ‘participation’, ‘scientific rigour’ and ‘reasonableness’. Conclusions The structural equation model was among the first applications of PLS-PM to coverage decision-making. It allowed testing of hypotheses in situations where there are links between several non-observable constructs. PLS-PM was compatible in accounting for the complexity of coverage decisions to obtain a more realistic perspective for empirical analysis. The model specification can be used for hypothesis testing by using larger sample sizes and for data in the full domain of health technologies. PMID:22856325

Model of the best-of-N nest-site selection process in honeybees.

PubMed

Reina, Andreagiovanni; Marshall, James A R; Trianni, Vito; Bose, Thomas

2017-05-01

The ability of a honeybee swarm to select the best nest site plays a fundamental role in determining the future colony's fitness. To date, the nest-site selection process has mostly been modeled and theoretically analyzed for the case of binary decisions. However, when the number of alternative nests is larger than two, the decision-process dynamics qualitatively change. In this work, we extend previous analyses of a value-sensitive decision-making mechanism to a decision process among N nests. First, we present the decision-making dynamics in the symmetric case of N equal-quality nests. Then, we generalize our findings to a best-of-N decision scenario with one superior nest and N-1 inferior nests, previously studied empirically in bees and ants. Whereas previous binary models highlighted the crucial role of inhibitory stop-signaling, the key parameter in our new analysis is the relative time invested by swarm members in individual discovery and in signaling behaviors. Our new analysis reveals conflicting pressures on this ratio in symmetric and best-of-N decisions, which could be solved through a time-dependent signaling strategy. Additionally, our analysis suggests how ecological factors determining the density of suitable nest sites may have led to selective pressures for an optimal stable signaling ratio.

Model of the best-of-N nest-site selection process in honeybees

NASA Astrophysics Data System (ADS)

Reina, Andreagiovanni; Marshall, James A. R.; Trianni, Vito; Bose, Thomas

2017-05-01

The ability of a honeybee swarm to select the best nest site plays a fundamental role in determining the future colony's fitness. To date, the nest-site selection process has mostly been modeled and theoretically analyzed for the case of binary decisions. However, when the number of alternative nests is larger than two, the decision-process dynamics qualitatively change. In this work, we extend previous analyses of a value-sensitive decision-making mechanism to a decision process among N nests. First, we present the decision-making dynamics in the symmetric case of N equal-quality nests. Then, we generalize our findings to a best-of-N decision scenario with one superior nest and N -1 inferior nests, previously studied empirically in bees and ants. Whereas previous binary models highlighted the crucial role of inhibitory stop-signaling, the key parameter in our new analysis is the relative time invested by swarm members in individual discovery and in signaling behaviors. Our new analysis reveals conflicting pressures on this ratio in symmetric and best-of-N decisions, which could be solved through a time-dependent signaling strategy. Additionally, our analysis suggests how ecological factors determining the density of suitable nest sites may have led to selective pressures for an optimal stable signaling ratio.

History matching through dynamic decision-making

PubMed Central

Maschio, Célio; Santos, Antonio Alberto; Schiozer, Denis; Rocha, Anderson

2017-01-01

History matching is the process of modifying the uncertain attributes of a reservoir model to reproduce the real reservoir performance. It is a classical reservoir engineering problem and plays an important role in reservoir management since the resulting models are used to support decisions in other tasks such as economic analysis and production strategy. This work introduces a dynamic decision-making optimization framework for history matching problems in which new models are generated based on, and guided by, the dynamic analysis of the data of available solutions. The optimization framework follows a ‘learning-from-data’ approach, and includes two optimizer components that use machine learning techniques, such as unsupervised learning and statistical analysis, to uncover patterns of input attributes that lead to good output responses. These patterns are used to support the decision-making process while generating new, and better, history matched solutions. The proposed framework is applied to a benchmark model (UNISIM-I-H) based on the Namorado field in Brazil. Results show the potential the dynamic decision-making optimization framework has for improving the quality of history matching solutions using a substantial smaller number of simulations when compared with a previous work on the same benchmark. PMID:28582413

Contrasting models of driver behaviour in emergencies using retrospective verbalisations and network analysis.

PubMed

Banks, Victoria A; Stanton, Neville A

2015-01-01

Automated assistance in driving emergencies aims to improve the safety of our roads by avoiding or mitigating the effects of accidents. However, the behavioural implications of such systems remain unknown. This paper introduces the driver decision-making in emergencies (DDMiEs) framework to investigate how the level and type of automation may affect driver decision-making and subsequent responses to critical braking events using network analysis to interrogate retrospective verbalisations. Four DDMiE models were constructed to represent different levels of automation within the driving task and its effects on driver decision-making. Findings suggest that whilst automation does not alter the decision-making pathway (e.g. the processes between hazard detection and response remain similar), it does appear to significantly weaken the links between information-processing nodes. This reflects an unintended yet emergent property within the task network that could mean that we may not be improving safety in the way we expect. This paper contrasts models of driver decision-making in emergencies at varying levels of automation using the Southampton University Driving Simulator. Network analysis of retrospective verbalisations indicates that increasing the level of automation in driving emergencies weakens the link between information-processing nodes essential for effective decision-making.

Defining optimum treatment of patients with pancreatic adenocarcinoma using regret-based decision curve analysis.

PubMed

Hernandez, Jonathan M; Tsalatsanis, Athanasios; Humphries, Leigh Ann; Miladinovic, Branko; Djulbegovic, Benjamin; Velanovich, Vic

2014-06-01

To use regret decision theory methodology to assess three treatment strategies in pancreatic adenocarcinoma. Pancreatic adenocarcinoma is uniformly fatal without operative intervention. Resection can prolong survival in some patients; however, it is associated with significant morbidity and mortality. Regret theory serves as a novel framework linking both rationality and intuition to determine the optimal course for physicians facing difficult decisions related to treatment. We used the Cox proportional hazards model to predict survival of patients with pancreatic adenocarcinoma and generated a decision model using regret-based decision curve analysis, which integrates both the patient's prognosis and the physician's preferences expressed in terms of regret associated with a certain action. A physician's treatment preferences are indicated by a threshold probability, which is the probability of death/survival at which the physician is uncertain whether or not to perform surgery. The analysis modeled 3 possible choices: perform surgery on all patients; never perform surgery; and act according to the prediction model. The records of 156 consecutive patients with pancreatic adenocarcinoma were retrospectively evaluated by a single surgeon at a tertiary referral center. Significant independent predictors of overall survival included preoperative stage [P = 0.005; 95% confidence interval (CI), 1.19-2.27], vitality (P < 0.001; 95% CI, 0.96-0.98), daily physical function (P < 0.001; 95% CI, 0.97-0.99), and pathological stage (P < 0.001; 95% CI, 3.06-16.05). Compared with the "always aggressive" or "always passive" surgical treatment strategies, the survival model was associated with the least amount of regret for a wide range of threshold probabilities. Regret-based decision curve analysis provides a novel perspective for making treatment-related decisions by incorporating the decision maker's preferences expressed as his or her estimates of benefits and harms associated with the treatment considered.

Determining the Best Treatment for Coronal Angular Deformity of the Knee Joint in Growing Children: A Decision Analysis

PubMed Central

Sung, Ki Hyuk; Chung, Chin Youb; Lee, Kyoung Min; Lee, Seung Yeol; Choi, In Ho; Cho, Tae-Joon; Yoo, Won Joon; Park, Moon Seok

2014-01-01

This study aimed to determine the best treatment modality for coronal angular deformity of the knee joint in growing children using decision analysis. A decision tree was created to evaluate 3 treatment modalities for coronal angular deformity in growing children: temporary hemiepiphysiodesis using staples, percutaneous screws, or a tension band plate. A decision analysis model was constructed containing the final outcome score, probability of metal failure, and incomplete correction of deformity. The final outcome was defined as health-related quality of life and was used as a utility in the decision tree. The probabilities associated with each case were obtained by literature review, and health-related quality of life was evaluated by a questionnaire completed by 25 pediatric orthopedic experts. Our decision analysis model favored temporary hemiepiphysiodesis using a tension band plate over temporary hemiepiphysiodesis using percutaneous screws or stapling, with utilities of 0.969, 0.957, and 0.962, respectively. One-way sensitivity analysis showed that hemiepiphysiodesis using a tension band plate was better than temporary hemiepiphysiodesis using percutaneous screws, when the overall complication rate of hemiepiphysiodesis using a tension band plate was lower than 15.7%. Two-way sensitivity analysis showed that hemiepiphysiodesis using a tension band plate was more beneficial than temporary hemiepiphysiodesis using percutaneous screws. PMID:25276801

Analysis of a decision model in the context of equilibrium pricing and order book pricing

NASA Astrophysics Data System (ADS)

Wagner, D. C.; Schmitt, T. A.; Schäfer, R.; Guhr, T.; Wolf, D. E.

2014-12-01

An agent-based model for financial markets has to incorporate two aspects: decision making and price formation. We introduce a simple decision model and consider its implications in two different pricing schemes. First, we study its parameter dependence within a supply-demand balance setting. We find realistic behavior in a wide parameter range. Second, we embed our decision model in an order book setting. Here, we observe interesting features which are not present in the equilibrium pricing scheme. In particular, we find a nontrivial behavior of the order book volumes which reminds of a trend switching phenomenon. Thus, the decision making model alone does not realistically represent the trading and the stylized facts. The order book mechanism is crucial.

When four principles are too many: bloodgate, integrity and an action-guiding model of ethical decision making in clinical practice.

PubMed

Muirhead, William

2012-04-01

Medical ethical analysis remains dominated by the principlist account first proposed by Beauchamp and Childress. This paper argues that the principlist model is unreflective of how ethical decisions are taken in clinical practice. Two kinds of medical ethical decisions are distinguished: biosocial ethics and clinical ethics. It is argued that principlism is an inappropriate model for clinical ethics as it is neither sufficiently action-guiding nor does it emphasise the professional integrity of the clinician. An alternative model is proposed for decision making in the realm of clinical ethics.

Dynamics of individual perceptual decisions

PubMed Central

Clark, Torin K.; Lu, Yue M.; Karmali, Faisal

2015-01-01

Perceptual decision making is fundamental to a broad range of fields including neurophysiology, economics, medicine, advertising, law, etc. Although recent findings have yielded major advances in our understanding of perceptual decision making, decision making as a function of time and frequency (i.e., decision-making dynamics) is not well understood. To limit the review length, we focus most of this review on human findings. Animal findings, which are extensively reviewed elsewhere, are included when beneficial or necessary. We attempt to put these various findings and data sets, which can appear to be unrelated in the absence of a formal dynamic analysis, into context using published models. Specifically, by adding appropriate dynamic mechanisms (e.g., high-pass filters) to existing models, it appears that a number of otherwise seemingly disparate findings from the literature might be explained. One hypothesis that arises through this dynamic analysis is that decision making includes phasic (high pass) neural mechanisms, an evidence accumulator and/or some sort of midtrial decision-making mechanism (e.g., peak detector and/or decision boundary). PMID:26467513

M&S Decision/Role-Behavior Decompositions

DTIC Science & Technology

2007-10-17

M &S Decision/Role-Behavior Decompositions Wargaming and Analysis Workshop Military Operations Research Society 17 October 2007 Paul Works, Methods...number. 1. REPORT DATE 17 OCT 2007 2. REPORT TYPE 3. DATES COVERED 00-00-2007 to 00-00-2007 4. TITLE AND SUBTITLE M &S Decision/Role-Behavior...transmission. • Combat models and simulations ( M &S) continue, in most cases, to model “effects-level” representations of SA, decisions, and behaviors. – M &S

Analysis and Management of Animal Populations: Modeling, Estimation and Decision Making

USGS Publications Warehouse

Williams, B.K.; Nichols, J.D.; Conroy, M.J.

2002-01-01

This book deals with the processes involved in making informed decisions about the management of animal populations. It covers the modeling of population responses to management actions, the estimation of quantities needed in the modeling effort, and the application of these estimates and models to the development of sound management decisions. The book synthesizes and integrates in a single volume the methods associated with these themes, as they apply to ecological assessment and conservation of animal populations. KEY FEATURES * Integrates population modeling, parameter estimation and * decision-theoretic approaches to management in a single, cohesive framework * Provides authoritative, state-of-the-art descriptions of quantitative * approaches to modeling, estimation and decision-making * Emphasizes the role of mathematical modeling in the conduct of science * and management * Utilizes a unifying biological context, consistent mathematical notation, * and numerous biological examples

An Intelligent Decision Support System for Workforce Forecast

DTIC Science & Technology

2011-01-01

ARIMA ) model to forecast the demand for construction skills in Hong Kong. This model was based...Decision Trees ARIMA Rule Based Forecasting Segmentation Forecasting Regression Analysis Simulation Modeling Input-Output Models LP and NLP Markovian...data • When results are needed as a set of easily interpretable rules 4.1.4 ARIMA Auto-regressive, integrated, moving-average ( ARIMA ) models

A Model for Estimating the Reliability and Validity of Criterion-Referenced Measures.

ERIC Educational Resources Information Center

Edmonston, Leon P.; Randall, Robert S.

A decision model designed to determine the reliability and validity of criterion referenced measures (CRMs) is presented. General procedures which pertain to the model are discussed as to: Measures of relationship, Reliability, Validity (content, criterion-oriented, and construct validation), and Item Analysis. The decision model is presented in…

Emergent collective decision-making: Control, model and behavior

NASA Astrophysics Data System (ADS)

Shen, Tian

In this dissertation we study emergent collective decision-making in social groups with time-varying interactions and heterogeneously informed individuals. First we analyze a nonlinear dynamical systems model motivated by animal collective motion with heterogeneously informed subpopulations, to examine the role of uninformed individuals. We find through formal analysis that adding uninformed individuals in a group increases the likelihood of a collective decision. Secondly, we propose a model for human shared decision-making with continuous-time feedback and where individuals have little information about the true preferences of other group members. We study model equilibria using bifurcation analysis to understand how the model predicts decisions based on the critical threshold parameters that represent an individual's tradeoff between social and environmental influences. Thirdly, we analyze continuous-time data of pairs of human subjects performing an experimental shared tracking task using our second proposed model in order to understand transient behavior and the decision-making process. We fit the model to data and show that it reproduces a wide range of human behaviors surprisingly well, suggesting that the model may have captured the mechanisms of observed behaviors. Finally, we study human behavior from a game-theoretic perspective by modeling the aforementioned tracking task as a repeated game with incomplete information. We show that the majority of the players are able to converge to playing Nash equilibrium strategies. We then suggest with simulations that the mean field evolution of strategies in the population resemble replicator dynamics, indicating that the individual strategies may be myopic. Decisions form the basis of control and problems involving deciding collectively between alternatives are ubiquitous in nature and in engineering. Understanding how multi-agent systems make decisions among alternatives also provides insight for designing decentralized control laws for engineering applications from mobile sensor networks for environmental monitoring to collective construction robots. With this dissertation we hope to provide additional methodology and mathematical models for understanding the behavior and control of collective decision-making in multi-agent systems.

Helping Health Care Providers and Clinical Scientists Understand Apparently Irrational Policy Decisions.

PubMed

Demeter, Sandor J

2016-12-21

Health care providers (HCP) and clinical scientists (CS) are generally most comfortable using evidence-based rational decision-making models. They become very frustrated when policymakers make decisions that, on the surface, seem irrational and unreasonable. However, such decisions usually make sense when analysed properly. The goal of this paper to provide a basic theoretical understanding of major policy models, to illustrate which models are most prevalent in publicly funded health care systems, and to propose a policy analysis framework to better understand the elements that drive policy decision-making. The proposed policy framework will also assist HCP and CS achieve greater success with their own proposals.

Cost-Effectiveness of Procedures for Treatment of Ostium Secundum Atrial Septal Defects Occlusion Comparing Conventional Surgery and Septal Percutaneous Implant

PubMed Central

da Costa, Márcia Gisele Santos; Santos, Marisa da Silva; Sarti, Flávia Mori; Senna, Kátia Marie Simões e.; Tura, Bernardo Rangel; Goulart, Marcelo Correia

2014-01-01

Objectives The study performs a cost-effectiveness analysis of procedures for atrial septal defects occlusion, comparing conventional surgery to septal percutaneous implant. Methods A model of analytical decision was structured with symmetric branches to estimate cost-effectiveness ratio between the procedures. The decision tree model was based on evidences gathered through meta-analysis of literature, and validated by a panel of specialists. The lower number of surgical procedures performed for atrial septal defects occlusion at each branch was considered as the effectiveness outcome. Direct medical costs and probabilities for each event were inserted in the model using data available from Brazilian public sector database system and information extracted from the literature review, using micro-costing technique. Sensitivity analysis included price variations of percutaneous implant. Results The results obtained from the decision model demonstrated that the percutaneous implant was more cost effective in cost-effectiveness analysis at a cost of US$8,936.34 with a reduction in the probability of surgery occurrence in 93% of the cases. Probability of atrial septal communication occlusion and cost of the implant are the determinant factors of cost-effectiveness ratio. Conclusions The proposal of a decision model seeks to fill a void in the academic literature. The decision model proposed includes the outcomes that present major impact in relation to the overall costs of the procedure. The atrial septal defects occlusion using percutaneous implant reduces the physical and psychological distress to the patients in relation to the conventional surgery, which represent intangible costs in the context of economic evaluation. PMID:25302806

Cost-effectiveness of procedures for treatment of ostium secundum atrial septal defects occlusion comparing conventional surgery and septal percutaneous implant.

PubMed

da Costa, Márcia Gisele Santos; Santos, Marisa da Silva; Sarti, Flávia Mori; Simões e Senna, Kátia Marie; Tura, Bernardo Rangel; Correia, Marcelo Goulart; Goulart, Marcelo Correia

2014-01-01

The study performs a cost-effectiveness analysis of procedures for atrial septal defects occlusion, comparing conventional surgery to septal percutaneous implant. A model of analytical decision was structured with symmetric branches to estimate cost-effectiveness ratio between the procedures. The decision tree model was based on evidences gathered through meta-analysis of literature, and validated by a panel of specialists. The lower number of surgical procedures performed for atrial septal defects occlusion at each branch was considered as the effectiveness outcome. Direct medical costs and probabilities for each event were inserted in the model using data available from Brazilian public sector database system and information extracted from the literature review, using micro-costing technique. Sensitivity analysis included price variations of percutaneous implant. The results obtained from the decision model demonstrated that the percutaneous implant was more cost effective in cost-effectiveness analysis at a cost of US$8,936.34 with a reduction in the probability of surgery occurrence in 93% of the cases. Probability of atrial septal communication occlusion and cost of the implant are the determinant factors of cost-effectiveness ratio. The proposal of a decision model seeks to fill a void in the academic literature. The decision model proposed includes the outcomes that present major impact in relation to the overall costs of the procedure. The atrial septal defects occlusion using percutaneous implant reduces the physical and psychological distress to the patients in relation to the conventional surgery, which represent intangible costs in the context of economic evaluation.

SPATIAL ANALYSIS AND DECISION ASSISTANCE (SADA) TRAINING COURSE

EPA Science Inventory

Spatial Analysis and Decision Assistance (SADA) is a Windows freeware program that incorporates tools from environmental assessment into an effective problem-solving environment. SADA was developed by the Institute for Environmental Modeling at the University of Tennessee and inc...

Next generation data systems and knowledge products to support agricultural producers and science-based policy decision making.

PubMed

Capalbo, Susan M; Antle, John M; Seavert, Clark

2017-07-01

Research on next generation agricultural systems models shows that the most important current limitation is data, both for on-farm decision support and for research investment and policy decision making. One of the greatest data challenges is to obtain reliable data on farm management decision making, both for current conditions and under scenarios of changed bio-physical and socio-economic conditions. This paper presents a framework for the use of farm-level and landscape-scale models and data to provide analysis that could be used in NextGen knowledge products, such as mobile applications or personal computer data analysis and visualization software. We describe two analytical tools - AgBiz Logic and TOA-MD - that demonstrate the current capability of farmlevel and landscape-scale models. The use of these tools is explored with a case study of an oilseed crop, Camelina sativa , which could be used to produce jet aviation fuel. We conclude with a discussion of innovations needed to facilitate the use of farm and policy-level models to generate data and analysis for improved knowledge products.

Use of Cost-Utility Decision Models in Business Education.

ERIC Educational Resources Information Center

Lewis, Darrell R.

1989-01-01

Explains how cost-utility analysis can be applied to the selection of curriculum and instructional methods. Describes the use of multiattribute utility models of decision making as a tool for more informed judgment in educational administration. (SK)

Triple Value System Dynamics Modeling to Help Stakeholders Engage with Food-Energy-Water Problems

EPA Science Inventory

Triple Value (3V) Community scoping projects and Triple Value Simulation (3VS) models help decision makers and stakeholders apply systems-analysis methodology to complex problems related to food production, water quality, and energy use. 3VS models are decision support tools that...

Portfolio Decisions and Brain Reactions via the CEAD method.

PubMed

Majer, Piotr; Mohr, Peter N C; Heekeren, Hauke R; Härdle, Wolfgang K

2016-09-01

Decision making can be a complex process requiring the integration of several attributes of choice options. Understanding the neural processes underlying (uncertain) investment decisions is an important topic in neuroeconomics. We analyzed functional magnetic resonance imaging (fMRI) data from an investment decision study for stimulus-related effects. We propose a new technique for identifying activated brain regions: cluster, estimation, activation, and decision method. Our analysis is focused on clusters of voxels rather than voxel units. Thus, we achieve a higher signal-to-noise ratio within the unit tested and a smaller number of hypothesis tests compared with the often used General Linear Model (GLM). We propose to first conduct the brain parcellation by applying spatially constrained spectral clustering. The information within each cluster can then be extracted by the flexible dynamic semiparametric factor model (DSFM) dimension reduction technique and finally be tested for differences in activation between conditions. This sequence of Cluster, Estimation, Activation, and Decision admits a model-free analysis of the local fMRI signal. Applying a GLM on the DSFM-based time series resulted in a significant correlation between the risk of choice options and changes in fMRI signal in the anterior insula and dorsomedial prefrontal cortex. Additionally, individual differences in decision-related reactions within the DSFM time series predicted individual differences in risk attitudes as modeled with the framework of the mean-variance model.

Decision analysis applied to the purchase of frozen premixed intravenous admixtures.

PubMed

Witte, K W; Eck, T A; Vogel, D P

1985-04-01

A structured decision-analysis model was used to evaluate frozen premixed cefazolin admixtures. Decision analysis is a process of stating the desired outcome, establishing and weighting evaluation criteria, identifying options for reaching the outcome, evaluating and numerically ranking each option for each criterion, multiplying the ranking by the weight for each criterion, and calculating total points for each option. It was used to compare objectively frozen premixed cefazolin admixtures with batch reconstitution from vials and reconstitution of lyophilized, ready-to-mix containers. In this institution the model numerically demonstrated a distinct preference for the premixed frozen admixture over these other alternatives. A comparison of these results with the total cost impact of each option resulted in a decision to purchase the frozen premixed solution. The advantages of the frozen premixed solution that contributed most to this decision were decreased waste and personnel time. The latter was especially important since it allowed for the reallocation of personnel resources to other potentially cost-reducing clinical functions. Decision analysis proved to be an effective tool for formalizing the process of selecting among various alternatives to reach a desired outcome in this hospital pharmacy.

A stochastic multicriteria model for evidence-based decision making in drug benefit-risk analysis.

PubMed

Tervonen, Tommi; van Valkenhoef, Gert; Buskens, Erik; Hillege, Hans L; Postmus, Douwe

2011-05-30

Drug benefit-risk (BR) analysis is based on firm clinical evidence regarding various safety and efficacy outcomes. In this paper, we propose a new and more formal approach for constructing a supporting multi-criteria model that fully takes into account the evidence on efficacy and adverse drug reactions. Our approach is based on the stochastic multi-criteria acceptability analysis methodology, which allows us to compute the typical value judgments that support a decision, to quantify decision uncertainty, and to compute a comprehensive BR profile. We construct a multi-criteria model for the therapeutic group of second-generation antidepressants. We assess fluoxetine and venlafaxine together with placebo according to incidence of treatment response and three common adverse drug reactions by using data from a published study. Our model shows that there are clear trade-offs among the treatment alternatives. Copyright © 2011 John Wiley & Sons, Ltd.

Modeling time-to-event (survival) data using classification tree analysis.

PubMed

Linden, Ariel; Yarnold, Paul R

2017-12-01

Time to the occurrence of an event is often studied in health research. Survival analysis differs from other designs in that follow-up times for individuals who do not experience the event by the end of the study (called censored) are accounted for in the analysis. Cox regression is the standard method for analysing censored data, but the assumptions required of these models are easily violated. In this paper, we introduce classification tree analysis (CTA) as a flexible alternative for modelling censored data. Classification tree analysis is a "decision-tree"-like classification model that provides parsimonious, transparent (ie, easy to visually display and interpret) decision rules that maximize predictive accuracy, derives exact P values via permutation tests, and evaluates model cross-generalizability. Using empirical data, we identify all statistically valid, reproducible, longitudinally consistent, and cross-generalizable CTA survival models and then compare their predictive accuracy to estimates derived via Cox regression and an unadjusted naïve model. Model performance is assessed using integrated Brier scores and a comparison between estimated survival curves. The Cox regression model best predicts average incidence of the outcome over time, whereas CTA survival models best predict either relatively high, or low, incidence of the outcome over time. Classification tree analysis survival models offer many advantages over Cox regression, such as explicit maximization of predictive accuracy, parsimony, statistical robustness, and transparency. Therefore, researchers interested in accurate prognoses and clear decision rules should consider developing models using the CTA-survival framework. © 2017 John Wiley & Sons, Ltd.

2018 Military Retirement Options: An Expected Net Present Value Decision Analysis Model

DTIC Science & Technology

2017-03-23

Decision Analysis Model Bret N. Witham Follow this and additional works at: https://scholar.afit.edu/etd Part of the Benefits and Compensation Commons...FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air Force Base, Ohio DISTRIBUTION STATEMENT A. APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED...Science in Operations Research Bret N. Witham, BS Captain, USAF March 2017 DISTRIBUTION STATEMENT A. APPROVED FOR PUBLIC RELEASE; DISTRIBUTION

Defining the optimal therapy sequence in synchronous resectable liver metastases from colorectal cancer: a decision analysis approach.

PubMed

Van Dessel, E; Fierens, K; Pattyn, P; Van Nieuwenhove, Y; Berrevoet, F; Troisi, R; Ceelen, W

2009-01-01

Approximately 5%-20% of colorectal cancer (CRC) patients present with synchronous potentially resectable liver metastatic disease. Preclinical and clinical studies suggest a benefit of the 'liver first' approach, i.e. resection of the liver metastasis followed by resection of the primary tumour. A formal decision analysis may support a rational choice between several therapy options. Survival and morbidity data were retrieved from relevant clinical studies identified by a Web of Science search. Data were entered into decision analysis software (TreeAge Pro 2009, Williamstown, MA, USA). Transition probabilities including the risk of death from complications or disease progression associated with individual therapy options were entered into the model. Sensitivity analysis was performed to evaluate the model's validity under a variety of assumptions. The result of the decision analysis confirms the superiority of the 'liver first' approach. Sensitivity analysis demonstrated that this assumption is valid on condition that the mortality associated with the hepatectomy first is < 4.5%, and that the mortality of colectomy performed after hepatectomy is < 3.2%. The results of this decision analysis suggest that, in patients with synchronous resectable colorectal liver metastases, the 'liver first' approach is to be preferred. Randomized trials will be needed to confirm the results of this simulation based outcome.

The impact of stakeholder involvement in hospital policy decision-making: a study of the hospital's business processes.

PubMed

Malfait, Simon; Van Hecke, Ann; Hellings, Johan; De Bodt, Griet; Eeckloo, Kristof

2017-02-01

In many health care systems, strategies are currently deployed to engage patients and other stakeholders in decisions affecting hospital services. In this paper, a model for stakeholder involvement is presented and evaluated in three Flemish hospitals. In the model, a stakeholder committee advises the hospital's board of directors on themes of strategic importance. To study the internal hospital's decision processes in order to identify the impact of a stakeholder involvement committee on strategic themes in the hospital decision processes. A retrospective analysis of the decision processes was conducted in three hospitals that implemented a stakeholder committee. The analysis consisted of process and outcome evaluation. Fifteen themes were discussed in the stakeholder committees, whereof 11 resulted in a considerable change. None of these were on a strategic level. The theoretical model was not applied as initially developed, but was altered by each hospital. Consequentially, the decision processes differed between the hospitals. Despite alternation of the model, the stakeholder committee showed a meaningful impact in all hospitals on the operational level. As a result of the differences in decision processes, three factors could be identified as facilitators for success: (1) a close interaction with the board of executives, (2) the inclusion of themes with a more practical and patient-oriented nature, and (3) the elaboration of decisions on lower echelons of the organization. To effectively influence the organization's public accountability, hospitals should involve stakeholders in the decision-making process of the organization. The model of a stakeholder committee was not applied as initially developed and did not affect the strategic decision-making processes in the involved hospitals. Results show only impact at the operational level in the participating hospitals. More research is needed connecting stakeholder involvement with hospital governance.

Make or buy decision model with multi-stage manufacturing process and supplier imperfect quality

NASA Astrophysics Data System (ADS)

Pratama, Mega Aria; Rosyidi, Cucuk Nur

2017-11-01

This research develops an make or buy decision model considering supplier imperfect quality. This model can be used to help companies make the right decision in case of make or buy component with the best quality and the least cost in multistage manufacturing process. The imperfect quality is one of the cost component that must be minimizing in this model. Component with imperfect quality, not necessarily defective. It still can be rework and used for assembly. This research also provide a numerical example and sensitivity analysis to show how the model work. We use simulation and help by crystal ball to solve the numerical problem. The sensitivity analysis result show that percentage of imperfect generally not affect to the model significantly, and the model is not sensitive to changes in these parameters. This is because the imperfect cost are smaller than overall total cost components.

Decision-Tree Analysis for Predicting First-Time Pass/Fail Rates for the NCLEX-RN® in Associate Degree Nursing Students.

PubMed

Chen, Hsiu-Chin; Bennett, Sean

2016-08-01

Little evidence shows the use of decision-tree algorithms in identifying predictors and analyzing their associations with pass rates for the NCLEX-RN(®) in associate degree nursing students. This longitudinal and retrospective cohort study investigated whether a decision-tree algorithm could be used to develop an accurate prediction model for the students' passing or failing the NCLEX-RN. This study used archived data from 453 associate degree nursing students in a selected program. The chi-squared automatic interaction detection analysis of the decision trees module was used to examine the effect of the collected predictors on passing/failing the NCLEX-RN. The actual percentage scores of Assessment Technologies Institute®'s RN Comprehensive Predictor(®) accurately identified students at risk of failing. The classification model correctly classified 92.7% of the students for passing. This study applied the decision-tree model to analyze a sequence database for developing a prediction model for early remediation in preparation for the NCLEXRN. [J Nurs Educ. 2016;55(8):454-457.]. Copyright 2016, SLACK Incorporated.

Use of the AHP methodology in system dynamics: Modelling and simulation for health technology assessments to determine the correct prosthesis choice for hernia diseases.

PubMed

Improta, Giovanni; Russo, Mario Alessandro; Triassi, Maria; Converso, Giuseppe; Murino, Teresa; Santillo, Liberatina Carmela

2018-05-01

Health technology assessments (HTAs) are often difficult to conduct because of the decisive procedures of the HTA algorithm, which are often complex and not easy to apply. Thus, their use is not always convenient or possible for the assessment of technical requests requiring a multidisciplinary approach. This paper aims to address this issue through a multi-criteria analysis focusing on the analytic hierarchy process (AHP). This methodology allows the decision maker to analyse and evaluate different alternatives and monitor their impact on different actors during the decision-making process. However, the multi-criteria analysis is implemented through a simulation model to overcome the limitations of the AHP methodology. Simulations help decision-makers to make an appropriate decision and avoid unnecessary and costly attempts. Finally, a decision problem regarding the evaluation of two health technologies, namely, the evaluation of two biological prostheses for incisional infected hernias, will be analysed to assess the effectiveness of the model. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Predicting species distributions for conservation decisions

PubMed Central

Guisan, Antoine; Tingley, Reid; Baumgartner, John B; Naujokaitis-Lewis, Ilona; Sutcliffe, Patricia R; Tulloch, Ayesha I T; Regan, Tracey J; Brotons, Lluis; McDonald-Madden, Eve; Mantyka-Pringle, Chrystal; Martin, Tara G; Rhodes, Jonathan R; Maggini, Ramona; Setterfield, Samantha A; Elith, Jane; Schwartz, Mark W; Wintle, Brendan A; Broennimann, Olivier; Austin, Mike; Ferrier, Simon; Kearney, Michael R; Possingham, Hugh P; Buckley, Yvonne M

2013-01-01

Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of ‘translators’ between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes. PMID:24134332

Different decision deficits impair response inhibition in progressive supranuclear palsy and Parkinson’s disease

PubMed Central

Rittman, Timothy; Nombela, Cristina; Fois, Alessandro; Coyle-Gilchrist, Ian; Barker, Roger A.; Hughes, Laura E.; Rowe, James B.

2016-01-01

Abstract Progressive supranuclear palsy and Parkinson’s disease have distinct underlying neuropathology, but both diseases affect cognitive function in addition to causing a movement disorder. They impair response inhibition and may lead to impulsivity, which can occur even in the presence of profound akinesia and rigidity. The current study examined the mechanisms of cognitive impairments underlying disinhibition, using horizontal saccadic latencies that obviate the impact of limb slowness on executing response decisions. Nineteen patients with clinically diagnosed progressive supranuclear palsy (Richardson’s syndrome), 24 patients with clinically diagnosed Parkinson’s disease and 26 healthy control subjects completed a saccadic Go/No-Go task with a head-mounted infrared saccadometer. Participants were cued on each trial to make a pro-saccade to a horizontal target or withhold their responses. Both patient groups had impaired behavioural performance, with more commission errors than controls. Mean saccadic latencies were similar between all three groups. We analysed behavioural responses as a binary decision between Go and No-Go choices. By using Bayesian parameter estimation, we fitted a hierarchical drift–diffusion model to individual participants’ single trial data. The model decomposes saccadic latencies into parameters for the decision process: decision boundary, drift rate of accumulation, decision bias, and non-decision time. In a leave-one-out three-way classification analysis, the model parameters provided better discrimination between patients and controls than raw behavioural measures. Furthermore, the model revealed disease-specific deficits in the Go/No-Go decision process. Both patient groups had slower drift rate of accumulation, and shorter non-decision time than controls. But patients with progressive supranuclear palsy were strongly biased towards a pro-saccade decision boundary compared to Parkinson’s patients and controls. This indicates a prepotency of responding in combination with a reduction in further accumulation of evidence, which provides a parsimonious explanation for the apparently paradoxical combination of disinhibition and severe akinesia. The combination of the well-tolerated oculomotor paradigm and the sensitivity of the model-based analysis provides a valuable approach for interrogating decision-making processes in neurodegenerative disorders. The mechanistic differences underlying participants’ poor performance were not observable from classical analysis of behavioural data, but were clearly revealed by modelling. These differences provide a rational basis on which to develop and assess new therapeutic strategies for cognition and behaviour in these disorders. PMID:26582559

Different decision deficits impair response inhibition in progressive supranuclear palsy and Parkinson's disease.

PubMed

Zhang, Jiaxiang; Rittman, Timothy; Nombela, Cristina; Fois, Alessandro; Coyle-Gilchrist, Ian; Barker, Roger A; Hughes, Laura E; Rowe, James B

2016-01-01

Progressive supranuclear palsy and Parkinson's disease have distinct underlying neuropathology, but both diseases affect cognitive function in addition to causing a movement disorder. They impair response inhibition and may lead to impulsivity, which can occur even in the presence of profound akinesia and rigidity. The current study examined the mechanisms of cognitive impairments underlying disinhibition, using horizontal saccadic latencies that obviate the impact of limb slowness on executing response decisions. Nineteen patients with clinically diagnosed progressive supranuclear palsy (Richardson's syndrome), 24 patients with clinically diagnosed Parkinson's disease and 26 healthy control subjects completed a saccadic Go/No-Go task with a head-mounted infrared saccadometer. Participants were cued on each trial to make a pro-saccade to a horizontal target or withhold their responses. Both patient groups had impaired behavioural performance, with more commission errors than controls. Mean saccadic latencies were similar between all three groups. We analysed behavioural responses as a binary decision between Go and No-Go choices. By using Bayesian parameter estimation, we fitted a hierarchical drift-diffusion model to individual participants' single trial data. The model decomposes saccadic latencies into parameters for the decision process: decision boundary, drift rate of accumulation, decision bias, and non-decision time. In a leave-one-out three-way classification analysis, the model parameters provided better discrimination between patients and controls than raw behavioural measures. Furthermore, the model revealed disease-specific deficits in the Go/No-Go decision process. Both patient groups had slower drift rate of accumulation, and shorter non-decision time than controls. But patients with progressive supranuclear palsy were strongly biased towards a pro-saccade decision boundary compared to Parkinson's patients and controls. This indicates a prepotency of responding in combination with a reduction in further accumulation of evidence, which provides a parsimonious explanation for the apparently paradoxical combination of disinhibition and severe akinesia. The combination of the well-tolerated oculomotor paradigm and the sensitivity of the model-based analysis provides a valuable approach for interrogating decision-making processes in neurodegenerative disorders. The mechanistic differences underlying participants' poor performance were not observable from classical analysis of behavioural data, but were clearly revealed by modelling. These differences provide a rational basis on which to develop and assess new therapeutic strategies for cognition and behaviour in these disorders. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain.

[Analysis of the characteristics of the older adults with depression using data mining decision tree analysis].

PubMed

Park, Myonghwa; Choi, Sora; Shin, A Mi; Koo, Chul Hoi

2013-02-01

The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

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.

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

A study on spatial decision support systems for HIV/AIDS prevention based on COM GIS technology

NASA Astrophysics Data System (ADS)

Yang, Kun; Luo, Huasong; Peng, Shungyun; Xu, Quanli

2007-06-01

Based on the deeply analysis of the current status and the existing problems of GIS technology applications in Epidemiology, this paper has proposed the method and process for establishing the spatial decision support systems of AIDS epidemic prevention by integrating the COM GIS, Spatial Database, GPS, Remote Sensing, and Communication technologies, as well as ASP and ActiveX software development technologies. One of the most important issues for constructing the spatial decision support systems of AIDS epidemic prevention is how to integrate the AIDS spreading models with GIS. The capabilities of GIS applications in the AIDS epidemic prevention have been described here in this paper firstly. Then some mature epidemic spreading models have also been discussed for extracting the computation parameters. Furthermore, a technical schema has been proposed for integrating the AIDS spreading models with GIS and relevant geospatial technologies, in which the GIS and model running platforms share a common spatial database and the computing results can be spatially visualized on Desktop or Web GIS clients. Finally, a complete solution for establishing the decision support systems of AIDS epidemic prevention has been offered in this paper based on the model integrating methods and ESRI COM GIS software packages. The general decision support systems are composed of data acquisition sub-systems, network communication sub-systems, model integrating sub-systems, AIDS epidemic information spatial database sub-systems, AIDS epidemic information querying and statistical analysis sub-systems, AIDS epidemic dynamic surveillance sub-systems, AIDS epidemic information spatial analysis and decision support sub-systems, as well as AIDS epidemic information publishing sub-systems based on Web GIS.

An Introduction to Solar Decision-Making Tools

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

Mow, Benjamin

2017-09-12

The National Renewable Energy Laboratory (NREL) offers a variety of models and analysis tools to help decision makers evaluate and make informed decisions about solar projects, policies, and programs. This fact sheet aims to help decision makers determine which NREL tool to use for a given solar project or policy question, depending on its scope.

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.

Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines.

PubMed

Lee, Saro; Park, Inhye

2013-09-30

Subsidence of ground caused by underground mines poses hazards to human life and property. This study analyzed the hazard to ground subsidence using factors that can affect ground subsidence and a decision tree approach in a geographic information system (GIS). The study area was Taebaek, Gangwon-do, Korea, where many abandoned underground coal mines exist. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 50/50 for training and validation of the models. A data-mining classification technique was applied to the GSH mapping, and decision trees were constructed using the chi-squared automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. The frequency ratio model was also applied to the GSH mapping for comparing with probabilistic model. The resulting GSH maps were validated using area-under-the-curve (AUC) analysis with the subsidence area data that had not been used for training the model. The highest accuracy was achieved by the decision tree model using CHAID algorithm (94.01%) comparing with QUEST algorithms (90.37%) and frequency ratio model (86.70%). These accuracies are higher than previously reported results for decision tree. Decision tree methods can therefore be used efficiently for GSH analysis and might be widely used for prediction of various spatial events. Copyright © 2013. Published by Elsevier Ltd.

Do violations of the axioms of expected utility theory threaten decision analysis?

PubMed

Nease, R F

1996-01-01

Research demonstrates that people violate the independence principle of expected utility theory, raising the question of whether expected utility theory is normative for medical decision making. The author provides three arguments that violations of the independence principle are less problematic than they might first appear. First, the independence principle follows from other more fundamental axioms whose appeal may be more readily apparent than that of the independence principle. Second, the axioms need not be descriptive to be normative, and they need not be attractive to all decision makers for expected utility theory to be useful for some. Finally, by providing a metaphor of decision analysis as a conversation between the actual decision maker and a model decision maker, the author argues that expected utility theory need not be purely normative for decision analysis to be useful. In short, violations of the independence principle do not necessarily represent direct violations of the axioms of expected utility theory; behavioral violations of the axioms of expected utility theory do not necessarily imply that decision analysis is not normative; and full normativeness is not necessary for decision analysis to generate valuable insights.

Psychophysical Models for Signal Detection with Time Varying Uncertainty. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Gai, E.

1975-01-01

Psychophysical models for the behavior of the human operator in detection tasks which include change in detectability, correlation between observations and deferred decisions are developed. Classical Signal Detection Theory (SDT) is discussed and its emphasis on the sensory processes is contrasted to decision strategies. The analysis of decision strategies utilizes detection tasks with time varying signal strength. The classical theory is modified to include such tasks and several optimal decision strategies are explored. Two methods of classifying strategies are suggested. The first method is similar to the analysis of ROC curves, while the second is based on the relation between the criterion level (CL) and the detectability. Experiments to verify the analysis of tasks with changes of signal strength are designed. The results show that subjects are aware of changes in detectability and tend to use strategies that involve changes in the CL's.

Multi-Agent simulation of generation capacity expansion decisions.

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

Botterud, A.; Mahalik, M.; Conzelmann, G.

2008-01-01

In this paper, we use a multi-agent simulation model, EMCAS, to analyze generation expansion in the Iberian electricity market. The expansion model simulates generation investment decisions of decentralized generating companies (GenCos) interacting in a complex, multidimensional environment. A probabilistic dispatch algorithm calculates prices and profits for new candidate units in different future states of the system. Uncertainties in future load, hydropower conditions, and competitorspsila actions are represented in a scenario tree, and decision analysis is used to identify the optimal expansion decision for each individual GenCo. We run the model using detailed data for the Iberian market. In a scenariomore » analysis, we look at the impact of market design variables, such as the energy price cap and carbon emission prices. We also analyze how market concentration and GenCospsila risk preferences influence the timing and choice of new generating capacity.« less

Administrative Decision Making and Resource Allocation.

ERIC Educational Resources Information Center

Sardy, Susan; Sardy, Hyman

This paper considers selected aspects of the systems analysis of administrative decisionmaking regarding resource allocations in an educational system. A model of the instructional materials purchase system is presented. The major components of this model are: environment, input, decision process, conversion structure, conversion process, output,…

Traffic analysis toolbox volume XI : weather and traffic analysis, modeling and simulation.

DOT National Transportation Integrated Search

2010-12-01

This document presents a weather module for the traffic analysis tools program. It provides traffic engineers, transportation modelers and decisions makers with a guide that can incorporate weather impacts into transportation system analysis and mode...

Mads.jl

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

Vesselinov, Velimir; O'Malley, Daniel; Lin, Youzuo

2016-07-01

Mads.jl (Model analysis and decision support in Julia) is a code that streamlines the process of using data and models for analysis and decision support. It is based on another open-source code developed at LANL and written in C/C++ (MADS; http://mads.lanl.gov; LA-CC-11- 035). Mads.jl can work with external models of arbitrary complexity as well as built-in models of flow and transport in porous media. It enables a number of data- and model-based analyses including model calibration, sensitivity analysis, uncertainty quantification, and decision analysis. The code also can use a series of alternative adaptive computational techniques for Bayesian sampling, Monte Carlo,more » and Bayesian Information-Gap Decision Theory. The code is implemented in the Julia programming language, and has high-performance (parallel) and memory management capabilities. The code uses a series of third party modules developed by others. The code development will also include contributions to the existing third party modules written in Julia; this contributions will be important for the efficient implementation of the algorithm used by Mads.jl. The code also uses a series of LANL developed modules that are developed by Dan O'Malley; these modules will be also a part of the Mads.jl release. Mads.jl will be released under GPL V3 license. The code will be distributed as a Git repo at gitlab.com and github.com. Mads.jl manual and documentation will be posted at madsjulia.lanl.gov.« less

Decision analysis with cumulative prospect theory.

PubMed

Bayoumi, A M; Redelmeier, D A

2000-01-01

Individuals sometimes express preferences that do not follow expected utility theory. Cumulative prospect theory adjusts for some phenomena by using decision weights rather than probabilities when analyzing a decision tree. The authors examined how probability transformations from cumulative prospect theory might alter a decision analysis of a prophylactic therapy in AIDS, eliciting utilities from patients with HIV infection (n = 75) and calculating expected outcomes using an established Markov model. They next focused on transformations of three sets of probabilities: 1) the probabilities used in calculating standard-gamble utility scores; 2) the probabilities of being in discrete Markov states; 3) the probabilities of transitioning between Markov states. The same prophylaxis strategy yielded the highest quality-adjusted survival under all transformations. For the average patient, prophylaxis appeared relatively less advantageous when standard-gamble utilities were transformed. Prophylaxis appeared relatively more advantageous when state probabilities were transformed and relatively less advantageous when transition probabilities were transformed. Transforming standard-gamble and transition probabilities simultaneously decreased the gain from prophylaxis by almost half. Sensitivity analysis indicated that even near-linear probability weighting transformations could substantially alter quality-adjusted survival estimates. The magnitude of benefit estimated in a decision-analytic model can change significantly after using cumulative prospect theory. Incorporating cumulative prospect theory into decision analysis can provide a form of sensitivity analysis and may help describe when people deviate from expected utility theory.

A diffusion decision model analysis of evidence variability in the lexical decision task.

PubMed

Tillman, Gabriel; Osth, Adam F; van Ravenzwaaij, Don; Heathcote, Andrew

2017-12-01

The lexical-decision task is among the most commonly used paradigms in psycholinguistics. In both the signal-detection theory and Diffusion Decision Model (DDM; Ratcliff, Gomez, & McKoon, Psychological Review, 111, 159-182, 2004) frameworks, lexical-decisions are based on a continuous source of word-likeness evidence for both words and non-words. The Retrieving Effectively from Memory model of Lexical-Decision (REM-LD; Wagenmakers et al., Cognitive Psychology, 48(3), 332-367, 2004) provides a comprehensive explanation of lexical-decision data and makes the prediction that word-likeness evidence is more variable for words than non-words and that higher frequency words are more variable than lower frequency words. To test these predictions, we analyzed five lexical-decision data sets with the DDM. For all data sets, drift-rate variability changed across word frequency and non-word conditions. For the most part, REM-LD's predictions about the ordering of evidence variability across stimuli in the lexical-decision task were confirmed.

Integrating Climate and Risk-Informed Science to Support Critical Decisions

ScienceCinema

None

2018-01-16

The PNNL Environmental Health and Remediation Sector stewards several decision support capabilities to integrate climate- and risk-informed science to support critical decisions. Utilizing our expertise in risk and decision analysis, integrated Earth systems modeling, and remote sensing and geoinformatics, PNNL is influencing the way science informs high level decisions at national, regional and local scales to protect and preserve our most critical assets.

Integrating Climate and Risk-Informed Science to Support Critical Decisions

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

None

2016-07-27

The PNNL Environmental Health and Remediation Sector stewards several decision support capabilities to integrate climate- and risk-informed science to support critical decisions. Utilizing our expertise in risk and decision analysis, integrated Earth systems modeling, and remote sensing and geoinformatics, PNNL is influencing the way science informs high level decisions at national, regional and local scales to protect and preserve our most critical assets.

Multicenter external validation of two malignancy risk prediction models in patients undergoing 18F-FDG-PET for solitary pulmonary nodule evaluation.

PubMed

Perandini, Simone; Soardi, G A; Larici, A R; Del Ciello, A; Rizzardi, G; Solazzo, A; Mancino, L; Zeraj, F; Bernhart, M; Signorini, M; Motton, M; Montemezzi, S

2017-05-01

To achieve multicentre external validation of the Herder and Bayesian Inference Malignancy Calculator (BIMC) models. Two hundred and fifty-nine solitary pulmonary nodules (SPNs) collected from four major hospitals which underwent 18-FDG-PET characterization were included in this multicentre retrospective study. The Herder model was tested on all available lesions (group A). A subgroup of 180 SPNs (group B) was used to provide unbiased comparison between the Herder and BIMC models. Receiver operating characteristic (ROC) area under the curve (AUC) analysis was performed to assess diagnostic accuracy. Decision analysis was performed by adopting the risk threshold stated in British Thoracic Society (BTS) guidelines. Unbiased comparison performed In Group B showed a ROC AUC for the Herder model of 0.807 (95 % CI 0.742-0.862) and for the BIMC model of 0.822 (95 % CI 0.758-0.875). Both the Herder and the BIMC models were proven to accurately predict the risk of malignancy when tested on a large multicentre external case series. The BIMC model seems advantageous on the basis of a more favourable decision analysis. • The Herder model showed a ROC AUC of 0.807 on 180 SPNs. • The BIMC model showed a ROC AUC of 0.822 on 180 SPNs. • Decision analysis is more favourable to the BIMC model.

Managing Uncertainty: Environmental Analysis/Forecasting in Academic Planning.

ERIC Educational Resources Information Center

Morrison, James L.; Mecca, Thomas V.

An approach to environmental analysis and forecasting that educational policymakers can employ in dealing with the level of uncertainty in strategic decision making is presented. Traditional planning models are weak in identifying environmental changes and assessing their organizational impact. The proposed approach does not lead decision makers…

Development of a decision support system for analysis and solutions of prolonged standing in the workplace.

PubMed

Halim, Isa; Arep, Hambali; Kamat, Seri Rahayu; Abdullah, Rohana; Omar, Abdul Rahman; Ismail, Ahmad Rasdan

2014-06-01

Prolonged standing has been hypothesized as a vital contributor to discomfort and muscle fatigue in the workplace. The objective of this study was to develop a decision support system that could provide systematic analysis and solutions to minimize the discomfort and muscle fatigue associated with prolonged standing. The integration of object-oriented programming and a Model Oriented Simultaneous Engineering System were used to design the architecture of the decision support system. Validation of the decision support system was carried out in two manufacturing companies. The validation process showed that the decision support system produced reliable results. The decision support system is a reliable advisory tool for providing analysis and solutions to problems related to the discomfort and muscle fatigue associated with prolonged standing. Further testing of the decision support system is suggested before it is used commercially.

Development of a Decision Support System for Analysis and Solutions of Prolonged Standing in the Workplace

PubMed Central

Halim, Isa; Arep, Hambali; Kamat, Seri Rahayu; Abdullah, Rohana; Omar, Abdul Rahman; Ismail, Ahmad Rasdan

2014-01-01

Background Prolonged standing has been hypothesized as a vital contributor to discomfort and muscle fatigue in the workplace. The objective of this study was to develop a decision support system that could provide systematic analysis and solutions to minimize the discomfort and muscle fatigue associated with prolonged standing. Methods The integration of object-oriented programming and a Model Oriented Simultaneous Engineering System were used to design the architecture of the decision support system. Results Validation of the decision support system was carried out in two manufacturing companies. The validation process showed that the decision support system produced reliable results. Conclusion The decision support system is a reliable advisory tool for providing analysis and solutions to problems related to the discomfort and muscle fatigue associated with prolonged standing. Further testing of the decision support system is suggested before it is used commercially. PMID:25180141

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis.

PubMed

Allyn, Jérôme; Allou, Nicolas; Augustin, Pascal; Philip, Ivan; Martinet, Olivier; Belghiti, Myriem; Provenchere, Sophie; Montravers, Philippe; Ferdynus, Cyril

2017-01-01

The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755-0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691-0.783) and 0.742 (0.698-0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold. According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction.

Financial Management and Control for Decision Making in Urban Local Bodies in India Using Statistical Techniques

NASA Astrophysics Data System (ADS)

Bhattacharyya, Sidhakam; Bandyopadhyay, Gautam

2010-10-01

The council of most of the Urban Local Bodies (ULBs) has a limited scope for decision making in the absence of appropriate financial control mechanism. The information about expected amount of own fund during a particular period is of great importance for decision making. Therefore, in this paper, efforts are being made to present set of findings and to establish a model of estimating receipts of own sources and payments thereof using multiple regression analysis. Data for sixty months from a reputed ULB in West Bengal have been considered for ascertaining the regression models. This can be used as a part of financial management and control procedure by the council to estimate the effect on own fund. In our study we have considered two models using multiple regression analysis. "Model I" comprises of total adjusted receipt as the dependent variable and selected individual receipts as the independent variables. Similarly "Model II" consists of total adjusted payments as the dependent variable and selected individual payments as independent variables. The resultant of Model I and Model II is the surplus or deficit effecting own fund. This may be applied for decision making purpose by the council.

Investigating the Nexus of Climate, Energy, Water, and Land at Decision-Relevant Scales: The Platform for Regional Integrated Modeling and Analysis (PRIMA)

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

Kraucunas, Ian P.; Clarke, Leon E.; Dirks, James A.

2015-04-01

The Platform for Regional Integrated Modeling and Analysis (PRIMA) is an innovative modeling system developed at Pacific Northwest National Laboratory (PNNL) to simulate interactions among natural and human systems at scales relevant to regional decision making. PRIMA brings together state-of-the-art models of regional climate, hydrology, agriculture, socioeconomics, and energy systems using a flexible coupling approach. The platform can be customized to inform a variety of complex questions and decisions, such as the integrated evaluation of mitigation and adaptation options across a range of sectors. Research into stakeholder decision support needs underpins the platform's application to regional issues, including uncertainty characterization.more » Ongoing numerical experiments are yielding new insights into the interactions among human and natural systems on regional scales with an initial focus on the energy-land-water nexus in the upper U.S. Midwest. This paper focuses on PRIMA’s functional capabilities and describes some lessons learned to date about integrated regional modeling.« less

Intuitive Cognition and Models of Human-Automation Interaction.

PubMed

Patterson, Robert Earl

2017-02-01

The aim of this study was to provide an analysis of the implications of the dominance of intuitive cognition in human reasoning and decision making for conceptualizing models and taxonomies of human-automation interaction, focusing on the Parasuraman et al. model and taxonomy. Knowledge about how humans reason and make decisions, which has been shown to be largely intuitive, has implications for the design of future human-machine systems. One hundred twenty articles and books cited in other works as well as those obtained from an Internet search were reviewed. Works were deemed eligible if they were published within the past 50 years and common to a given literature. Analysis shows that intuitive cognition dominates human reasoning and decision making in all situations examined. The implications of the dominance of intuitive cognition for the Parasuraman et al. model and taxonomy are discussed. A taxonomy of human-automation interaction that incorporates intuitive cognition is suggested. Understanding the ways in which human reasoning and decision making is intuitive can provide insight for future models and taxonomies of human-automation interaction.

Use of Inverse Reinforcement Learning for Identity Prediction

NASA Technical Reports Server (NTRS)

Hayes, Roy; Bao, Jonathan; Beling, Peter; Horowitz, Barry

2011-01-01

We adopt Markov Decision Processes (MDP) to model sequential decision problems, which have the characteristic that the current decision made by a human decision maker has an uncertain impact on future opportunity. We hypothesize that the individuality of decision makers can be modeled as differences in the reward function under a common MDP model. A machine learning technique, Inverse Reinforcement Learning (IRL), was used to learn an individual's reward function based on limited observation of his or her decision choices. This work serves as an initial investigation for using IRL to analyze decision making, conducted through a human experiment in a cyber shopping environment. Specifically, the ability to determine the demographic identity of users is conducted through prediction analysis and supervised learning. The results show that IRL can be used to correctly identify participants, at a rate of 68% for gender and 66% for one of three college major categories.

To Spray or Not to Spray: A Decision Analysis of Coffee Berry Borer in Hawaii

PubMed Central

2017-01-01

Integrated pest management strategies were adopted to combat the coffee berry borer (CBB) after its arrival in Hawaii in 2010. A decision tree framework is used to model the CBB integrated pest management recommendations, for potential use by growers and to assist in developing and evaluating management strategies and policies. The model focuses on pesticide spraying (spray/no spray) as the most significant pest management decision within each period over the entire crop season. The main result from the analysis suggests the most important parameter to maximize net benefit is to ensure a low initial infestation level. A second result looks at the impact of a subsidy for the cost of pesticides and shows a typical farmer receives a positive net benefit of $947.17. Sensitivity analysis of parameters checks the robustness of the model and further confirms the importance of a low initial infestation level vis-a-vis any level of subsidy. The use of a decision tree is shown to be an effective method for understanding integrated pest management strategies and solutions. PMID:29065464

Ignorance- versus Evidence-Based Decision Making: A Decision Time Analysis of the Recognition Heuristic

ERIC Educational Resources Information Center

Hilbig, Benjamin E.; Pohl, Rudiger F.

2009-01-01

According to part of the adaptive toolbox notion of decision making known as the recognition heuristic (RH), the decision process in comparative judgments--and its duration--is determined by whether recognition discriminates between objects. By contrast, some recently proposed alternative models predict that choices largely depend on the amount of…

Five reasons not to use numerical models in water resource management (Arne Richter Award Lecture for OYS)

NASA Astrophysics Data System (ADS)

Pianosi, Francesca

2015-04-01

Sustainable water resource management in a quickly changing world poses new challenges to hydrology and decision sciences. Systems analysis can contribute to promote sustainable practices by providing the theoretical background and the operational tools for an objective and transparent appraisal of policy options for water resource systems (WRS) management. Traditionally, limited availability of data and computing resources imposed to use oversimplified WRS models, with little consideration of modeling uncertainties and of the non-stationarity and feedbacks between WRS drivers, and a priori aggregation of costs and benefits. Nowadays we increasingly recognize the inadequacy of these simplifications, and consider them among the reasons for the limited use of model-generated information in actual decision-making processes. On the other hand, fast-growing availability of data and computing resources are opening up unprecedented possibilities in the way we build and apply numerical models. In this talk I will discuss my experiences and ideas on how we can exploit this potential to improve model-informed decision-making while facing the challenges of uncertainty, non-stationarity, feedbacks and conflicting objectives. In particular, through practical examples of WRS design and operation problems, my talk will aim at stimulating discussion about the impact of uncertainty on decisions: can inaccurate and imprecise predictions still carry valuable information for decision-making? Does uncertainty in predictions necessarily limit our ability to make 'good' decisions? Or can uncertainty even be of help for decision-making, for instance by reducing the projected conflict between competing water use? Finally, I will also discuss how the traditionally separate disciplines of numerical modelling, optimization, and uncertainty and sensitivity analysis have in my experience been just different facets of the same 'systems approach'.

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.

Midwives׳ clinical reasoning during second stage labour: Report on an interpretive study.

PubMed

Jefford, Elaine; Fahy, Kathleen

2015-05-01

clinical reasoning was once thought to be the exclusive domain of medicine - setting it apart from 'non-scientific' occupations like midwifery. Poor assessment, clinical reasoning and decision-making skills are well known contributors to adverse outcomes in maternity care. Midwifery decision-making models share a common deficit: they are insufficiently detailed to guide reasoning processes for midwives in practice. For these reasons we wanted to explore if midwives actively engaged in clinical reasoning processes within their clinical practice and if so to what extent. The study was conducted using post structural, feminist methodology. to what extent do midwives engage in clinical reasoning processes when making decisions in the second stage labour? twenty-six practising midwives were interviewed. Feminist interpretive analysis was conducted by two researchers guided by the steps of a model of clinical reasoning process. Six narratives were excluded from analysis because they did not sufficiently address the research question. The midwives narratives were prepared via data reduction. A theoretically informed analysis and interpretation was conducted. using a feminist, interpretive approach we created a model of midwifery clinical reasoning grounded in the literature and consistent with the data. Thirteen of the 20 participant narratives demonstrate analytical clinical reasoning abilities but only nine completed the process and implemented the decision. Seven midwives used non-analytical decision-making without adequately checking against assessment data. over half of the participants demonstrated the ability to use clinical reasoning skills. Less than half of the midwives demonstrated clinical reasoning as their way of making decisions. The new model of Midwifery Clinical Reasoning includes 'intuition' as a valued way of knowing. Using intuition, however, should not replace clinical reasoning which promotes through decision-making can be made transparent and be consensually validated. Copyright © 2015 Elsevier Ltd. All rights reserved.

Decision Analysis for Multicandidate Voting Systems. Applications of Elementary Decision Analysis to Political Science. [and] An Application of Voting Theory to Congress. Applications of Decision Theory and Game Theory to American Politics. Modules and Monographs in Undergraduate Mathematics and Its Applications Project. UMAP Units 384 and 386.

ERIC Educational Resources Information Center

Merrill, Samuel, III; Enelow, James M.

This document consists of two modules. The first studies a variety of multicandidate voting systems, including approval, Borda, and cumulative voting, using a model which takes account of a voter's intensity of preference for candidates. The voter's optimal strategy is investigated for each voting system using decision criteria under uncertainty…

Robustness analysis of a green chemistry-based model for the ...

EPA Pesticide Factsheets

This paper proposes a robustness analysis based on Multiple Criteria Decision Aiding (MCDA). The ensuing model was used to assess the implementation of green chemistry principles in the synthesis of silver nanoparticles. Its recommendations were also compared to an earlier developed model for the same purpose to investigate concordance between the models and potential decision support synergies. A three-phase procedure was adopted to achieve the research objectives. Firstly, an ordinal ranking of the evaluation criteria used to characterize the implementation of green chemistry principles was identified through relative ranking analysis. Secondly, a structured selection process for an MCDA classification method was conducted, which ensued in the identification of Stochastic Multi-Criteria Acceptability Analysis (SMAA). Lastly, the agreement of the classifications by the two MCDA models and the resulting synergistic role of decision recommendations were studied. This comparison showed that the results of the two models agree between 76% and 93% of the simulation set-ups and it confirmed that different MCDA models provide a more inclusive and transparent set of recommendations. This integrative research confirmed the beneficial complementary use of MCDA methods to aid responsible development of nanosynthesis, by accounting for multiple objectives and helping communication of complex information in a comprehensive and traceable format, suitable for stakeholders and

Analyzing stakeholder preferences for managing elk and bison at the National Elk Refuge and Grand Teton National Park: An example of the disparate stakeholder management approach

USGS Publications Warehouse

Koontz, Lynne; Hoag, Dana L.

2005-01-01

Many programs and tools have been developed by different disciplines to facilitate group negotiation and decision making. Three examples are relevant here. First, decision analysis models such as the Analytical Hierarchy Process (AHP) are commonly used to prioritize the goals and objectives of stakeholders’ preferences for resource planning by formally structuring conflicts and assisting decision makers in developing a compromised solution (Forman, 1998). Second, institutional models such as the Legal Institutional Analysis Model (LIAM) have been used to describe the organizational rules of behavior and the institutional boundaries constraining management decisions (Lamb and others, 1998). Finally, public choice models have been used to predict the potential success of rent-seeking activity (spending additional time and money to exert political pressure) to change the political rules (Becker, 1983). While these tools have been successful at addressing various pieces of the natural resource decision making process, their use in isolation is not enough to fully depict the complexities of the physical and biological systems with the rules and constraints of the underlying economic and political systems. An approach is needed that combines natural sciences, economics, and politics.

INTEGRATING SOCIAL, ECONOMIC AND ECOLOGICAL ANALYSIS TO IMPROVE WILDFIRE MANAGEMENT IN THE U.S.: TESTING A NEW ORGANIZING APPROACH FOR ENVIRONMENTAL ASSESSMENT

EPA Science Inventory

Conducting an integrated analysis to evaluate the societal and ecological consequences of environmental management actions requires decisions about data collection, theory development, modeling and valuation. Approaching these decisions in coordinated fashion necessitates a syste...

Outsourcing within aerospace manufacturing enterprises: A phenomenological study and outsourcing leadership model

NASA Astrophysics Data System (ADS)

Sampson, Enrique, Jr.

Many aerospace workers believe transferring work projects abroad has an erosive effect on the U.S. aerospace industry (Pritchard, 2002). This qualitative phenomenological study examines factors for outsourcing decisions and the perceived effects of outsourcing on U.S. aerospace workers. The research sample consists of aerospace industry leaders and nonleaders from the East Coast, Midwest, and West Coast of the United States. Moustakas' modified van Kaam methods of analysis (1994) and Decision Explorer analysis software were applied to the interview transcripts. Resultant data identified five core themes: communication, best value, opportunities, cost, and offset consideration. The themes provided the framework for a model designed to assist leaders in making effective decisions and communicating the benefits of those decisions when considering outsourcing of work projects.

Decision making in asthma exacerbation: a clinical judgement analysis

PubMed Central

Jenkins, John; Shields, Mike; Patterson, Chris; Kee, Frank

2007-01-01

Background Clinical decisions which impact directly on patient safety and quality of care are made during acute asthma attacks by individual doctors based on their knowledge and experience. Decisions include administration of systemic corticosteroids (CS) and oral antibiotics, and admission to hospital. Clinical judgement analysis provides a methodology for comparing decisions between practitioners with different training and experience, and improving decision making. Methods Stepwise linear regression was used to select clinical cues based on visual analogue scale assessments of the propensity of 62 clinicians to prescribe a short course of oral CS (decision 1), a course of antibiotics (decision 2), and/or admit to hospital (decision 3) for 60 “paper” patients. Results When compared by specialty, paediatricians' models for decision 1 were more likely to include level of alertness as a cue (54% vs 16%); for decision 2 they were more likely to include presence of crepitations (49% vs 16%) and less likely to include inhaled CS (8% vs 40%), respiratory rate (0% vs 24%) and air entry (70% vs 100%). When compared to other grades, the models derived for decision 3 by consultants/general practitioners were more likely to include wheeze severity as a cue (39% vs 6%). Conclusions Clinicians differed in their use of individual cues and the number included in their models. Patient safety and quality of care will benefit from clarification of decision‐making strategies as general learning points during medical training, in the development of guidelines and care pathways, and by clinicians developing self‐awareness of their own preferences. PMID:17428817

A Participants' DSS for a Management Game with a DSS Generator.

ERIC Educational Resources Information Center

Yeo, Gee Kin; Nah, Fui Hoon

1992-01-01

Describes the design of a decision support system (DSS) for a management game called MAGNUS (Management Game for National University of Singapore). Built-in models for performance analysis and decision making are explained; database query and model building are described; and future work is discussed. (11 references) (LRW)

SADA: A FREEWARE DECISION SUPPORT TOOL INTEGRATING GIS, SAMPLE DESIGN, SPATIAL MODELING AND RISK ASSESSMENT (SLIDE PRESENTATION)

EPA Science Inventory

Spatial Analysis and Decision Assistance (SADA) is a Windows freeware program that incorporates tools from environmental assessment into an effective problem-solving environment. SADA was developed by the Institute for Environmental Modeling at the University of Tennessee and inc...

MEETING IN CHICAGO: SADA: A FREEWARE DECISION SUPPORT TOOL INTEGRATING GIS, SAMPLE DESIGN, SPATIAL MODELING, AND ENVIRONMENTAL RISK ASSESSMENT

EPA Science Inventory

Spatial Analysis and Decision Assistance (SADA) is a Windows freeware program that incorporates tools from environmental assessment into an effective problem-solving environment. SADA was developed by the Institute for Environmental Modeling at the University of Tennessee and inc...

MEETING IN CZECH REPUBLIC: SADA: A FREEWARE DECISION SUPPORT TOOL INTEGRATING GIS, SAMPLE DESIGN, SPATIAL MODELING, AND RISK ASSESSMENT

EPA Science Inventory

Spatial Analysis and Decision Assistance (SADA) is a Windows freeware program that incorporates tools from environmental assessment into an effective problem-solving environment. SADA was developed by the Institute for Environmental Modeling at the University of Tennessee and inc...

Review of early assessment models of innovative medical technologies.

PubMed

Fasterholdt, Iben; Krahn, Murray; Kidholm, Kristian; Yderstræde, Knud Bonnet; Pedersen, Kjeld Møller

2017-08-01

Hospitals increasingly make decisions regarding the early development of and investment in technologies, but a formal evaluation model for assisting hospitals early on in assessing the potential of innovative medical technologies is lacking. This article provides an overview of models for early assessment in different health organisations and discusses which models hold most promise for hospital decision makers. A scoping review of published studies between 1996 and 2015 was performed using nine databases. The following information was collected: decision context, decision problem, and a description of the early assessment model. 2362 articles were identified and 12 studies fulfilled the inclusion criteria. An additional 12 studies were identified and included in the review by searching reference lists. The majority of the 24 early assessment studies were variants of traditional cost-effectiveness analysis. Around one fourth of the studies presented an evaluation model with a broader focus than cost-effectiveness. Uncertainty was mostly handled by simple sensitivity or scenario analysis. This review shows that evaluation models using known methods assessing cost-effectiveness are most prevalent in early assessment, but seems ill-suited for early assessment in hospitals. Four models provided some usable elements for the development of a hospital-based model. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Two retailer-supplier supply chain models with default risk under trade credit policy.

PubMed

Wu, Chengfeng; Zhao, Qiuhong

2016-01-01

The purpose of the paper is to formulate two uncooperative replenishment models with demand and default risk which are the functions of the trade credit period, i.e., a Nash equilibrium model and a supplier-Stackelberg model. Firstly, we present the optimal results of decentralized decision and centralized decision without trade credit. Secondly, we derive the existence and uniqueness conditions of the optimal solutions under the two games, respectively. Moreover, we present a set of theorems and corollary to determine the optimal solutions. Finally, we provide an example and sensitivity analysis to illustrate the proposed strategy and optimal solutions. Sensitivity analysis reveals that the total profits of supply chain under the two games both are better than the results under the centralized decision only if the optimal trade credit period isn't too short. It also reveals that the size of trade credit period, demand, retailer's profit and supplier's profit have strong relationship with the increasing demand coefficient, wholesale price, default risk coefficient and production cost. The major contribution of the paper is that we comprehensively compare between the results of decentralized decision and centralized decision without trade credit, Nash equilibrium and supplier-Stackelberg models with trade credit, and obtain some interesting managerial insights and practical implications.

The Decision Module Working Paper

DTIC Science & Technology

1973-12-01

and goal change has received very little attention In the litera- ture on the analysis of choice situations. It has generally been the case that the...Decision Making: Approach and Prototype" (197:0, done In context of the Mesarovlc - Pestel World Model Projet’ The Issues dealing with «-he cho ce...Nelson, Winder, and Schuette (1973) on evolutionary economic growth models. The discussion of the two components of the decision module that follows

Atlas-guided volumetric diffuse optical tomography enhanced by generalized linear model analysis to image risk decision-making responses in young adults.

PubMed

Lin, Zi-Jing; Li, Lin; Cazzell, Mary; Liu, Hanli

2014-08-01

Diffuse optical tomography (DOT) is a variant of functional near infrared spectroscopy and has the capability of mapping or reconstructing three dimensional (3D) hemodynamic changes due to brain activity. Common methods used in DOT image analysis to define brain activation have limitations because the selection of activation period is relatively subjective. General linear model (GLM)-based analysis can overcome this limitation. In this study, we combine the atlas-guided 3D DOT image reconstruction with GLM-based analysis (i.e., voxel-wise GLM analysis) to investigate the brain activity that is associated with risk decision-making processes. Risk decision-making is an important cognitive process and thus is an essential topic in the field of neuroscience. The Balloon Analog Risk Task (BART) is a valid experimental model and has been commonly used to assess human risk-taking actions and tendencies while facing risks. We have used the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making from 37 human participants (22 males and 15 females). Voxel-wise GLM analysis was performed after a human brain atlas template and a depth compensation algorithm were combined to form atlas-guided DOT images. In this work, we wish to demonstrate the excellence of using voxel-wise GLM analysis with DOT to image and study cognitive functions in response to risk decision-making. Results have shown significant hemodynamic changes in the dorsal lateral prefrontal cortex (DLPFC) during the active-choice mode and a different activation pattern between genders; these findings correlate well with published literature in functional magnetic resonance imaging (fMRI) and fNIRS studies. Copyright © 2014 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

A Critical Meta-Analysis of Lens Model Studies in Human Judgment and Decision-Making

PubMed Central

Kaufmann, Esther; Reips, Ulf-Dietrich; Wittmann, Werner W.

2013-01-01

Achieving accurate judgment (‘judgmental achievement’) is of utmost importance in daily life across multiple domains. The lens model and the lens model equation provide useful frameworks for modeling components of judgmental achievement and for creating tools to help decision makers (e.g., physicians, teachers) reach better judgments (e.g., a correct diagnosis, an accurate estimation of intelligence). Previous meta-analyses of judgment and decision-making studies have attempted to evaluate overall judgmental achievement and have provided the basis for evaluating the success of bootstrapping (i.e., replacing judges by linear models that guide decision making). However, previous meta-analyses have failed to appropriately correct for a number of study design artifacts (e.g., measurement error, dichotomization), which may have potentially biased estimations (e.g., of the variability between studies) and led to erroneous interpretations (e.g., with regards to moderator variables). In the current study we therefore conduct the first psychometric meta-analysis of judgmental achievement studies that corrects for a number of study design artifacts. We identified 31 lens model studies (N = 1,151, k = 49) that met our inclusion criteria. We evaluated overall judgmental achievement as well as whether judgmental achievement depended on decision domain (e.g., medicine, education) and/or the level of expertise (expert vs. novice). We also evaluated whether using corrected estimates affected conclusions with regards to the success of bootstrapping with psychometrically-corrected models. Further, we introduce a new psychometric trim-and-fill method to estimate the effect sizes of potentially missing studies correct psychometric meta-analyses for effects of publication bias. Comparison of the results of the psychometric meta-analysis with the results of a traditional meta-analysis (which only corrected for sampling error) indicated that artifact correction leads to a) an increase in values of the lens model components, b) reduced heterogeneity between studies, and c) increases the success of bootstrapping. We argue that psychometric meta-analysis is useful for accurately evaluating human judgment and show the success of bootstrapping. PMID:24391781

A critical meta-analysis of lens model studies in human judgment and decision-making.

PubMed

Kaufmann, Esther; Reips, Ulf-Dietrich; Wittmann, Werner W

2013-01-01

Achieving accurate judgment ('judgmental achievement') is of utmost importance in daily life across multiple domains. The lens model and the lens model equation provide useful frameworks for modeling components of judgmental achievement and for creating tools to help decision makers (e.g., physicians, teachers) reach better judgments (e.g., a correct diagnosis, an accurate estimation of intelligence). Previous meta-analyses of judgment and decision-making studies have attempted to evaluate overall judgmental achievement and have provided the basis for evaluating the success of bootstrapping (i.e., replacing judges by linear models that guide decision making). However, previous meta-analyses have failed to appropriately correct for a number of study design artifacts (e.g., measurement error, dichotomization), which may have potentially biased estimations (e.g., of the variability between studies) and led to erroneous interpretations (e.g., with regards to moderator variables). In the current study we therefore conduct the first psychometric meta-analysis of judgmental achievement studies that corrects for a number of study design artifacts. We identified 31 lens model studies (N = 1,151, k = 49) that met our inclusion criteria. We evaluated overall judgmental achievement as well as whether judgmental achievement depended on decision domain (e.g., medicine, education) and/or the level of expertise (expert vs. novice). We also evaluated whether using corrected estimates affected conclusions with regards to the success of bootstrapping with psychometrically-corrected models. Further, we introduce a new psychometric trim-and-fill method to estimate the effect sizes of potentially missing studies correct psychometric meta-analyses for effects of publication bias. Comparison of the results of the psychometric meta-analysis with the results of a traditional meta-analysis (which only corrected for sampling error) indicated that artifact correction leads to a) an increase in values of the lens model components, b) reduced heterogeneity between studies, and c) increases the success of bootstrapping. We argue that psychometric meta-analysis is useful for accurately evaluating human judgment and show the success of bootstrapping.

A Multi-Objective Decision-Making Model for Resources Allocation in Humanitarian Relief

DTIC Science & Technology

2007-03-01

Applied Mathematics and Computation 163, 2005, pp756 19. Malczewski, J., GIS and Multicriteria Decision Analysis , John Wiley and Sons, New York... used when interpreting the results of the analysis . (Raimo et al. 2002) (7) Sensitivity analysis Sensitivity analysis in a DA process answers...Budget Scenario Analysis The MILP is solved ( using LINDO 6.1) for high, medium and low budget scenarios in both damage degree levels. Tables 17 and

Probabilistic approach to decision making under uncertainty during volcanic crises. Retrospective analysis of the 2011 eruption of El Hierro, in the Canary Islands

NASA Astrophysics Data System (ADS)

Sobradelo, Rosa; Martí, Joan; Kilburn, Christopher; López, Carmen

2014-05-01

Understanding the potential evolution of a volcanic crisis is crucial to improving the design of effective mitigation strategies. This is especially the case for volcanoes close to densely-populated regions, where inappropriate decisions may trigger widespread loss of life, economic disruption and public distress. An outstanding goal for improving the management of volcanic crises, therefore, is to develop objective, real-time methodologies for evaluating how an emergency will develop and how scientists communicate with decision makers. Here we present a new model BADEMO (Bayesian Decision Model) that applies a general and flexible, probabilistic approach to managing volcanic crises. The model combines the hazard and risk factors that decision makers need for a holistic analysis of a volcanic crisis. These factors include eruption scenarios and their probabilities of occurrence, the vulnerability of populations and their activities, and the costs of false alarms and failed forecasts. The model can be implemented before an emergency, to identify actions for reducing the vulnerability of a district; during an emergency, to identify the optimum mitigating actions and how these may change as new information is obtained; and after an emergency, to assess the effectiveness of a mitigating response and, from the results, to improve strategies before another crisis occurs. As illustrated by a retrospective analysis of the 2011 eruption of El Hierro, in the Canary Islands, BADEMO provides the basis for quantifying the uncertainty associated with each recommended action as an emergency evolves, and serves as a mechanism for improving communications between scientists and decision makers.

Linked Sensitivity Analysis, Calibration, and Uncertainty Analysis Using a System Dynamics Model for Stroke Comparative Effectiveness Research.

PubMed

Tian, Yuan; Hassmiller Lich, Kristen; Osgood, Nathaniel D; Eom, Kirsten; Matchar, David B

2016-11-01

As health services researchers and decision makers tackle more difficult problems using simulation models, the number of parameters and the corresponding degree of uncertainty have increased. This often results in reduced confidence in such complex models to guide decision making. To demonstrate a systematic approach of linked sensitivity analysis, calibration, and uncertainty analysis to improve confidence in complex models. Four techniques were integrated and applied to a System Dynamics stroke model of US veterans, which was developed to inform systemwide intervention and research planning: Morris method (sensitivity analysis), multistart Powell hill-climbing algorithm and generalized likelihood uncertainty estimation (calibration), and Monte Carlo simulation (uncertainty analysis). Of 60 uncertain parameters, sensitivity analysis identified 29 needing calibration, 7 that did not need calibration but significantly influenced key stroke outcomes, and 24 not influential to calibration or stroke outcomes that were fixed at their best guess values. One thousand alternative well-calibrated baselines were obtained to reflect calibration uncertainty and brought into uncertainty analysis. The initial stroke incidence rate among veterans was identified as the most influential uncertain parameter, for which further data should be collected. That said, accounting for current uncertainty, the analysis of 15 distinct prevention and treatment interventions provided a robust conclusion that hypertension control for all veterans would yield the largest gain in quality-adjusted life years. For complex health care models, a mixed approach was applied to examine the uncertainty surrounding key stroke outcomes and the robustness of conclusions. We demonstrate that this rigorous approach can be practical and advocate for such analysis to promote understanding of the limits of certainty in applying models to current decisions and to guide future data collection. © The Author(s) 2016.

Spatial planning using probabilistic flood maps

NASA Astrophysics Data System (ADS)

Alfonso, Leonardo; Mukolwe, Micah; Di Baldassarre, Giuliano

2015-04-01

Probabilistic flood maps account for uncertainty in flood inundation modelling and convey a degree of certainty in the outputs. Major sources of uncertainty include input data, topographic data, model structure, observation data and parametric uncertainty. Decision makers prefer less ambiguous information from modellers; this implies that uncertainty is suppressed to yield binary flood maps. Though, suppressing information may potentially lead to either surprise or misleading decisions. Inclusion of uncertain information in the decision making process is therefore desirable and transparent. To this end, we utilise the Prospect theory and information from a probabilistic flood map to evaluate potential decisions. Consequences related to the decisions were evaluated using flood risk analysis. Prospect theory explains how choices are made given options for which probabilities of occurrence are known and accounts for decision makers' characteristics such as loss aversion and risk seeking. Our results show that decision making is pronounced when there are high gains and loss, implying higher payoffs and penalties, therefore a higher gamble. Thus the methodology may be appropriately considered when making decisions based on uncertain information.

A model of pathways to artificial superintelligence catastrophe for risk and decision analysis

NASA Astrophysics Data System (ADS)

Barrett, Anthony M.; Baum, Seth D.

2017-03-01

An artificial superintelligence (ASI) is an artificial intelligence that is significantly more intelligent than humans in all respects. Whilst ASI does not currently exist, some scholars propose that it could be created sometime in the future, and furthermore that its creation could cause a severe global catastrophe, possibly even resulting in human extinction. Given the high stakes, it is important to analyze ASI risk and factor the risk into decisions related to ASI research and development. This paper presents a graphical model of major pathways to ASI catastrophe, focusing on ASI created via recursive self-improvement. The model uses the established risk and decision analysis modelling paradigms of fault trees and influence diagrams in order to depict combinations of events and conditions that could lead to AI catastrophe, as well as intervention options that could decrease risks. The events and conditions include select aspects of the ASI itself as well as the human process of ASI research, development and management. Model structure is derived from published literature on ASI risk. The model offers a foundation for rigorous quantitative evaluation and decision-making on the long-term risk of ASI catastrophe.

Single- versus dual-process models of lexical decision performance: insights from response time distributional analysis.

PubMed

Yap, Melvin J; Balota, David A; Cortese, Michael J; Watson, Jason M

2006-12-01

This article evaluates 2 competing models that address the decision-making processes mediating word recognition and lexical decision performance: a hybrid 2-stage model of lexical decision performance and a random-walk model. In 2 experiments, nonword type and word frequency were manipulated across 2 contrasts (pseudohomophone-legal nonword and legal-illegal nonword). When nonwords became more wordlike (i.e., BRNTA vs. BRANT vs. BRANE), response latencies to nonwords were slowed and the word frequency effect increased. More important, distributional analyses revealed that the Nonword Type = Word Frequency interaction was modulated by different components of the response time distribution, depending on the specific nonword contrast. A single-process random-walk model was able to account for this particular set of findings more successfully than the hybrid 2-stage model. (c) 2006 APA, all rights reserved.

A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

PubMed

Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

2015-12-01

This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Hospice decision making: diagnosis makes a difference.

PubMed

Waldrop, Deborah P; Meeker, Mary Ann

2012-10-01

This study explored the process of decision making about hospice enrollment and identified factors that influence the timing of that decision. This study employed an exploratory, descriptive, cross-sectional design and was conducted using qualitative methods. In-depth in-person semistructured interviews were conducted with 36 hospice patients and 55 caregivers after 2 weeks of hospice care. The study was guided by Janis and Mann's conflict theory model (CTM) of decision making. Qualitative data analysis involved a directed content analysis using concepts from the CTM. A model of hospice enrollment decision making is presented. Concepts from the CTM (appraisal, surveying and weighing the alternatives, deliberations, adherence) were used as an organizing framework to illustrate the dynamics. Distinct differences were found by diagnosis (cancer vs. other chronic illness, e.g., heart and lung diseases) during the pre-encounter phase or before the hospice referral but no differences emerged during the post-encounter phase. Differences in decision making by diagnosis suggest the need for research about effective means for tailored communication in end-of-life decision making by type of illness. Recognition that decision making about hospice admission varies is important for clinicians who aim to provide person-centered and family-focused care.

Designing a Hydro-Economic Collaborative Computer Decision Support System: Approaches, Best Practices, Lessons Learned, and Future Trends

NASA Astrophysics Data System (ADS)

Rosenberg, D. E.

2008-12-01

Designing and implementing a hydro-economic computer model to support or facilitate collaborative decision making among multiple stakeholders or users can be challenging and daunting. Collaborative modeling is distinguished and more difficult than non-collaborative efforts because of a large number of users with different backgrounds, disagreement or conflict among stakeholders regarding problem definitions, modeling roles, and analysis methods, plus evolving ideas of model scope and scale and needs for information and analysis as stakeholders interact, use the model, and learn about the underlying water system. This presentation reviews the lifecycle for collaborative model making and identifies some key design decisions that stakeholders and model developers must make to develop robust and trusted, verifiable and transparent, integrated and flexible, and ultimately useful models. It advances some best practices to implement and program these decisions. Among these best practices are 1) modular development of data- aware input, storage, manipulation, results recording and presentation components plus ways to couple and link to other models and tools, 2) explicitly structure both input data and the meta data that describes data sources, who acquired it, gaps, and modifications or translations made to put the data in a form usable by the model, 3) provide in-line documentation on model inputs, assumptions, calculations, and results plus ways for stakeholders to document their own model use and share results with others, and 4) flexibly program with graphical object-oriented properties and elements that allow users or the model maintainers to easily see and modify the spatial, temporal, or analysis scope as the collaborative process moves forward. We draw on examples of these best practices from the existing literature, the author's prior work, and some new applications just underway. The presentation concludes by identifying some future directions for collaborative modeling including geo-spatial display and analysis, real-time operations, and internet-based tools plus the design and programming needed to implement these capabilities.

How infants' reaches reveal principles of sensorimotor decision making

NASA Astrophysics Data System (ADS)

Dineva, Evelina; Schöner, Gregor

2018-01-01

In Piaget's classical A-not-B-task, infants repeatedly make a sensorimotor decision to reach to one of two cued targets. Perseverative errors are induced by switching the cue from A to B, while spontaneous errors are unsolicited reaches to B when only A is cued. We argue that theoretical accounts of sensorimotor decision-making fail to address how motor decisions leave a memory trace that may impact future sensorimotor decisions. Instead, in extant neural models, perseveration is caused solely by the history of stimulation. We present a neural dynamic model of sensorimotor decision-making within the framework of Dynamic Field Theory, in which a dynamic instability amplifies fluctuations in neural activation into macroscopic, stable neural activation states that leave memory traces. The model predicts perseveration, but also a tendency to repeat spontaneous errors. To test the account, we pool data from several A-not-B experiments. A conditional probabilities analysis accounts quantitatively how motor decisions depend on the history of reaching. The results provide evidence for the interdependence among subsequent reaching decisions that is explained by the model, showing that by amplifying small differences in activation and affecting learning, decisions have consequences beyond the individual behavioural act.

Decision analysis to address extreme weather : extreme weather effects on ridership and modeling the decision to invest in canopy coverage.

DOT National Transportation Integrated Search

2016-09-01

This project applies a decision analytic methodology that takes considerations of extreme weather events to quantify and assess canopy investment options. The project collected data for two cases studies in two different transit agencies: Chicago Tra...

Signal detection in animal psychoacoustics: Analysis and simulation of sensory and decision-related influences

PubMed Central

Alves-Pinto, A.; Sollini, J.; Sumner, C.J.

2012-01-01

Signal detection theory (SDT) provides a framework for interpreting psychophysical experiments, separating the putative internal sensory representation and the decision process. SDT was used to analyse ferret behavioural responses in a (yes–no) tone-in-noise detection task. Instead of measuring the receiver-operating characteristic (ROC), we tested SDT by comparing responses collected using two common psychophysical data collection methods. These (Constant Stimuli, Limits) differ in the set of signal levels presented within and across behavioural sessions. The results support the use of SDT as a method of analysis: SDT sensory component was unchanged between the two methods, even though decisions depended on the stimuli presented within a behavioural session. Decision criterion varied trial-by-trial: a ‘yes’ response was more likely after a correct rejection trial than a hit trial. Simulation using an SDT model with several decision components reproduced the experimental observations accurately, leaving only ∼10% of the variance unaccounted for. The model also showed that trial-by-trial dependencies were unlikely to influence measured psychometric functions or thresholds. An additional model component suggested that inattention did not contribute substantially. Further analysis showed that ferrets were changing their decision criteria, almost optimally, to maximise the reward obtained in a session. The data suggest trial-by-trial reward-driven optimization of the decision process. Understanding the factors determining behavioural responses is important for correlating neural activity and behaviour. SDT provides a good account of animal psychoacoustics, and can be validated using standard psychophysical methods and computer simulations, without recourse to ROC measurements. PMID:22698686

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.

Estimating the Expected Value of Sample Information Using the Probabilistic Sensitivity Analysis Sample

PubMed Central

Oakley, Jeremy E.; Brennan, Alan; Breeze, Penny

2015-01-01

Health economic decision-analytic models are used to estimate the expected net benefits of competing decision options. The true values of the input parameters of such models are rarely known with certainty, and it is often useful to quantify the value to the decision maker of reducing uncertainty through collecting new data. In the context of a particular decision problem, the value of a proposed research design can be quantified by its expected value of sample information (EVSI). EVSI is commonly estimated via a 2-level Monte Carlo procedure in which plausible data sets are generated in an outer loop, and then, conditional on these, the parameters of the decision model are updated via Bayes rule and sampled in an inner loop. At each iteration of the inner loop, the decision model is evaluated. This is computationally demanding and may be difficult if the posterior distribution of the model parameters conditional on sampled data is hard to sample from. We describe a fast nonparametric regression-based method for estimating per-patient EVSI that requires only the probabilistic sensitivity analysis sample (i.e., the set of samples drawn from the joint distribution of the parameters and the corresponding net benefits). The method avoids the need to sample from the posterior distributions of the parameters and avoids the need to rerun the model. The only requirement is that sample data sets can be generated. The method is applicable with a model of any complexity and with any specification of model parameter distribution. We demonstrate in a case study the superior efficiency of the regression method over the 2-level Monte Carlo method. PMID:25810269

Role of scientific data in health decisions.

PubMed Central

Samuels, S W

1979-01-01

The distinction between reality and models or methodological assumptions is necessary for an understanding of the use of data--economic, technical or biological--in decision-making. The traditional modes of analysis used in decisions are discussed historically and analytically. Utilitarian-based concepts such as cost-benefit analysis and cannibalistic concepts such as "acceptable risk" are rejected on logical and moral grounds. Historical reality suggests the concept of socially necessary risk determined through the dialectic process in democracy. PMID:120251

Multi-agent simulation of generation expansion in electricity markets.

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

Botterud, A; Mahalik, M. R.; Veselka, T. D.

2007-06-01

We present a new multi-agent model of generation expansion in electricity markets. The model simulates generation investment decisions of decentralized generating companies (GenCos) interacting in a complex, multidimensional environment. A probabilistic dispatch algorithm calculates prices and profits for new candidate units in different future states of the system. Uncertainties in future load, hydropower conditions, and competitors actions are represented in a scenario tree, and decision analysis is used to identify the optimal expansion decision for each individual GenCo. We test the model using real data for the Korea power system under different assumptions about market design, market concentration, and GenCo'smore » assumed expectations about their competitors investment decisions.« less

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis

PubMed Central

Allyn, Jérôme; Allou, Nicolas; Augustin, Pascal; Philip, Ivan; Martinet, Olivier; Belghiti, Myriem; Provenchere, Sophie; Montravers, Philippe; Ferdynus, Cyril

2017-01-01

Background The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. Methods and finding We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755–0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691–0.783) and 0.742 (0.698–0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold. Conclusions According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction. PMID:28060903

Decision Modeling Framework to Minimize Arrival Delays from Ground Delay Programs

NASA Astrophysics Data System (ADS)

Mohleji, Nandita

Convective weather and other constraints create uncertainty in air transportation, leading to costly delays. A Ground Delay Program (GDP) is a strategy to mitigate these effects. Systematic decision support can increase GDP efficacy, reduce delays, and minimize direct operating costs. In this study, a decision analysis (DA) model is constructed by combining a decision tree and Bayesian belief network. Through a study of three New York region airports, the DA model demonstrates that larger GDP scopes that include more flights in the program, along with longer lead times that provide stakeholders greater notice of a pending program, trigger the fewest average arrival delays. These findings are demonstrated to result in a savings of up to $1,850 per flight. Furthermore, when convective weather is predicted, forecast weather confidences remain the same level or greater at least 70% of the time, supporting more strategic decision making. The DA model thus enables quantification of uncertainties and insights on causal relationships, providing support for future GDP decisions.

How Do Cultural Producers Make Creative Decisions? Lessons from the Catwalk

ERIC Educational Resources Information Center

Godart, Frederic C. Mears, Ashley

2009-01-01

Faced with high uncertainty, how do producers in the cultural economy make creative decisions? We present a case study of the fashion modeling industry. Using participant observation, interviews and network analysis of the Spring/Summer 2007 Fashion Week collections, we explain how producers select models for fashion shows. While fashion producers…

Decision support models for solid waste management: Review and game-theoretic approaches

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

Karmperis, Athanasios C., E-mail: athkarmp@mail.ntua.gr; Army Corps of Engineers, Hellenic Army General Staff, Ministry of Defence; Aravossis, Konstantinos

Highlights: ► The mainly used decision support frameworks for solid waste management are reviewed. ► The LCA, CBA and MCDM models are presented and their strengths, weaknesses, similarities and possible combinations are analyzed. ► The game-theoretic approach in a solid waste management context is presented. ► The waste management bargaining game is introduced as a specific decision support framework. ► Cooperative and non-cooperative game-theoretic approaches to decision support for solid waste management are discussed. - Abstract: This paper surveys decision support models that are commonly used in the solid waste management area. Most models are mainly developed within three decisionmore » support frameworks, which are the life-cycle assessment, the cost–benefit analysis and the multi-criteria decision-making. These frameworks are reviewed and their strengths and weaknesses as well as their critical issues are analyzed, while their possible combinations and extensions are also discussed. Furthermore, the paper presents how cooperative and non-cooperative game-theoretic approaches can be used for the purpose of modeling and analyzing decision-making in situations with multiple stakeholders. Specifically, since a waste management model is sustainable when considering not only environmental and economic but also social aspects, the waste management bargaining game is introduced as a specific decision support framework in which future models can be developed.« less

Applying air pollution modelling within a multi-criteria decision analysis framework to evaluate UK air quality policies

NASA Astrophysics Data System (ADS)

Chalabi, Zaid; Milojevic, Ai; Doherty, Ruth M.; Stevenson, David S.; MacKenzie, Ian A.; Milner, James; Vieno, Massimo; Williams, Martin; Wilkinson, Paul

2017-10-01

A decision support system for evaluating UK air quality policies is presented. It combines the output from a chemistry transport model, a health impact model and other impact models within a multi-criteria decision analysis (MCDA) framework. As a proof-of-concept, the MCDA framework is used to evaluate and compare idealized emission reduction policies in four sectors (combustion in energy and transformation industries, non-industrial combustion plants, road transport and agriculture) and across six outcomes or criteria (mortality, health inequality, greenhouse gas emissions, biodiversity, crop yield and air quality legal compliance). To illustrate a realistic use of the MCDA framework, the relative importance of the criteria were elicited from a number of stakeholders acting as proxy policy makers. In the prototype decision problem, we show that reducing emissions from industrial combustion (followed very closely by road transport and agriculture) is more advantageous than equivalent reductions from the other sectors when all the criteria are taken into account. Extensions of the MCDA framework to support policy makers in practice are discussed.

[Parameter of evidence-based medicine in health care economics].

PubMed

Wasem, J; Siebert, U

1999-08-01

In the view of scarcity of resources, economic evaluations in health care, in which not only effects but also costs related to a medical intervention are examined and a incremental cost-outcome-ratio is build, are an important supplement to the program of evidence based medicine. Outcomes of a medical intervention can be measured by clinical effectiveness, quality-adjusted life years, and monetary evaluation of benefits. As far as costs are concerned, direct medical costs, direct non-medical costs and indirect costs have to be considered in an economic evaluation. Data can be used from primary studies or secondary analysis; metaanalysis for synthesizing of data may be adequate. For calculation of incremental cost-benefit-ratios, models of decision analysis (decision tree models, Markov-models) often are necessary. Methodological and ethical limits for application of the results of economic evaluation in resource allocation decision in health care have to be regarded: Economic evaluations and the calculation of cost-outcome-rations should only support decision making but cannot replace it.

Two-Stage Fracturing Wastewater Management in Shale Gas Development

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

Zhang, Xiaodong; Sun, Alexander Y.; Duncan, Ian J.

Here, management of shale gas wastewater treatment, disposal, and reuse has become a significant environmental challenge, driven by an ongoing boom in development of U.S. shale gas reservoirs. Systems-analysis based decision support is helpful for effective management of wastewater, and provision of cost-effective decision alternatives from a whole-system perspective. Uncertainties are inherent in many modeling parameters, affecting the generated decisions. In order to effectively deal with the recourse issue in decision making, in this work a two-stage stochastic fracturing wastewater management model, named TSWM, is developed to provide decision support for wastewater management planning in shale plays. Using the TSWMmore » model, probabilistic and nonprobabilistic uncertainties are effectively handled. The TSWM model provides flexibility in generating shale gas wastewater management strategies, in which the first-stage decision predefined by decision makers before uncertainties are unfolded is corrected in the second stage to achieve the whole-system’s optimality. Application of the TSWM model to a comprehensive synthetic example demonstrates its practical applicability and feasibility. Optimal results are generated for allowable wastewater quantities, excess wastewater, and capacity expansions of hazardous wastewater treatment plants to achieve the minimized total system cost. The obtained interval solutions encompass both optimistic and conservative decisions. Trade-offs between economic and environmental objectives are made depending on decision makers’ knowledge and judgment, as well as site-specific information. In conclusion, the proposed model is helpful in forming informed decisions for wastewater management associated with shale gas development.« less

Two-Stage Fracturing Wastewater Management in Shale Gas Development

DOE PAGES

Zhang, Xiaodong; Sun, Alexander Y.; Duncan, Ian J.; ...

2017-01-19

Here, management of shale gas wastewater treatment, disposal, and reuse has become a significant environmental challenge, driven by an ongoing boom in development of U.S. shale gas reservoirs. Systems-analysis based decision support is helpful for effective management of wastewater, and provision of cost-effective decision alternatives from a whole-system perspective. Uncertainties are inherent in many modeling parameters, affecting the generated decisions. In order to effectively deal with the recourse issue in decision making, in this work a two-stage stochastic fracturing wastewater management model, named TSWM, is developed to provide decision support for wastewater management planning in shale plays. Using the TSWMmore » model, probabilistic and nonprobabilistic uncertainties are effectively handled. The TSWM model provides flexibility in generating shale gas wastewater management strategies, in which the first-stage decision predefined by decision makers before uncertainties are unfolded is corrected in the second stage to achieve the whole-system’s optimality. Application of the TSWM model to a comprehensive synthetic example demonstrates its practical applicability and feasibility. Optimal results are generated for allowable wastewater quantities, excess wastewater, and capacity expansions of hazardous wastewater treatment plants to achieve the minimized total system cost. The obtained interval solutions encompass both optimistic and conservative decisions. Trade-offs between economic and environmental objectives are made depending on decision makers’ knowledge and judgment, as well as site-specific information. In conclusion, the proposed model is helpful in forming informed decisions for wastewater management associated with shale gas development.« less

Decision analysis to complete diagnostic research by closing the gap between test characteristics and cost-effectiveness.

PubMed

Schaafsma, Joanna D; van der Graaf, Yolanda; Rinkel, Gabriel J E; Buskens, Erik

2009-12-01

The lack of a standard methodology in diagnostic research impedes adequate evaluation before implementation of constantly developing diagnostic techniques. We discuss the methodology of diagnostic research and underscore the relevance of decision analysis in the process of evaluation of diagnostic tests. Overview and conceptual discussion. Diagnostic research requires a stepwise approach comprising assessment of test characteristics followed by evaluation of added value, clinical outcome, and cost-effectiveness. These multiple goals are generally incompatible with a randomized design. Decision-analytic models provide an important alternative through integration of the best available evidence. Thus, critical assessment of clinical value and efficient use of resources can be achieved. Decision-analytic models should be considered part of the standard methodology in diagnostic research. They can serve as a valid alternative to diagnostic randomized clinical trials (RCTs).

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.

Decision-case mix model for analyzing variation in cesarean rates.

PubMed

Eldenburg, L; Waller, W S

2001-01-01

This article contributes a decision-case mix model for analyzing variation in c-section rates. Like recent contributions to the literature, the model systematically takes into account the effect of case mix. Going beyond past research, the model highlights differences in physician decision making in response to obstetric factors. Distinguishing the effects of physician decision making and case mix is important in understanding why c-section rates vary and in developing programs to effect change in physician behavior. The model was applied to a sample of deliveries at a hospital where physicians exhibited considerable variation in their c-section rates. Comparing groups with a low versus high rate, the authors' general conclusion is that the difference in physician decision tendencies (to perform a c-section), in response to specific obstetric factors, is at least as important as case mix in explaining variation in c-section rates. The exact effects of decision making versus case mix depend on how the model application defines the obstetric condition of interest and on the weighting of deliveries by their estimated "risk of Cesarean." The general conclusion is supported by an additional analysis that uses the model's elements to predict individual physicians' annual c-section rates.

Monte Carlo decision curve analysis using aggregate data.

PubMed

Hozo, Iztok; Tsalatsanis, Athanasios; Djulbegovic, Benjamin

2017-02-01

Decision curve analysis (DCA) is an increasingly used method for evaluating diagnostic tests and predictive models, but its application requires individual patient data. The Monte Carlo (MC) method can be used to simulate probabilities and outcomes of individual patients and offers an attractive option for application of DCA. We constructed a MC decision model to simulate individual probabilities of outcomes of interest. These probabilities were contrasted against the threshold probability at which a decision-maker is indifferent between key management strategies: treat all, treat none or use predictive model to guide treatment. We compared the results of DCA with MC simulated data against the results of DCA based on actual individual patient data for three decision models published in the literature: (i) statins for primary prevention of cardiovascular disease, (ii) hospice referral for terminally ill patients and (iii) prostate cancer surgery. The results of MC DCA and patient data DCA were identical. To the extent that patient data DCA were used to inform decisions about statin use, referral to hospice or prostate surgery, the results indicate that MC DCA could have also been used. As long as the aggregate parameters on distribution of the probability of outcomes and treatment effects are accurately described in the published reports, the MC DCA will generate indistinguishable results from individual patient data DCA. We provide a simple, easy-to-use model, which can facilitate wider use of DCA and better evaluation of diagnostic tests and predictive models that rely only on aggregate data reported in the literature. © 2017 Stichting European Society for Clinical Investigation Journal Foundation.

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China

PubMed Central

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-01-01

Background: In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. Methods: As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6–12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. Results: The prevalence of anemia was 12.60% with a range of 3.47%–40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. Conclusions: The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities. PMID:27174328

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China.

PubMed

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-05-20

In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6-12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. The prevalence of anemia was 12.60% with a range of 3.47%-40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities.

Exploratory Use of Decision Tree Analysis in Classification of Outcome in Hypoxic-Ischemic Brain Injury.

PubMed

Phan, Thanh G; Chen, Jian; Singhal, Shaloo; Ma, Henry; Clissold, Benjamin B; Ly, John; Beare, Richard

2018-01-01

Prognostication following hypoxic ischemic encephalopathy (brain injury) is important for clinical management. The aim of this exploratory study is to use a decision tree model to find clinical and MRI associates of severe disability and death in this condition. We evaluate clinical model and then the added value of MRI data. The inclusion criteria were as follows: age ≥17 years, cardio-respiratory arrest, and coma on admission (2003-2011). Decision tree analysis was used to find clinical [Glasgow Coma Score (GCS), features about cardiac arrest, therapeutic hypothermia, age, and sex] and MRI (infarct volume) associates of severe disability and death. We used the area under the ROC (auROC) to determine accuracy of model. There were 41 (63.7% males) patients having MRI imaging with the average age 51.5 ± 18.9 years old. The decision trees showed that infarct volume and age were important factors for discrimination between mild to moderate disability and severe disability and death at day 0 and day 2. The auROC for this model was 0.94 (95% CI 0.82-1.00). At day 7, GCS value was the only predictor; the auROC was 0.96 (95% CI 0.86-1.00). Our findings provide proof of concept for further exploration of the role of MR imaging and decision tree analysis in the early prognostication of hypoxic ischemic brain injury.

Development of a Computer-Based Air Force Installation Restoration Workstation for Contaminant Modeling and Decision-Making

DTIC Science & Technology

1995-03-01

advisory system provides a decision framework for selecting an appropriate model from the nuimerous available transport models conditinni-ed on...l1, T ,TV Groundwater Modeling, Contaminant Transport , Optimi2atio’ 2; Total Reliability, Remediation Si , , -J % UNCLASSIFIED UNCLASSIFIED...0 0 0 0 S 0 Sn S Even with the choice of an appropriate transport model, considlrable uncertainty is likely to be present in the analysis of

Opinion Dynamics and Decision of Vote in Bipolar Political Systems

NASA Astrophysics Data System (ADS)

Caruso, Filippo; Castorina, Paolo

A model of the opinion dynamics underlying the political decision is proposed. The analysis is restricted to a bipolar scheme with a possible third political area. The interaction among voters is local but the final decision strongly depends on global effects such as the rating of the governments. As in the realistic case, the individual decision making process is determined by the most relevant personal interests and problems. The phenomenological analysis of the national vote in Italy and Germany has been carried out and a prediction of the next Italian vote as a function of the government rating is presented.

Behavioral and Organizational Considerations in the Design of Information Systems and Processes for Planning and Decision Support,

DTIC Science & Technology

1981-06-01

analysis and display capability provided by management information systems to include interpretation and aggregation of information and values such as...accomplishment of these) 2. analysis of the issue d) systems analysis and modeling (determination of the structure of the decision situation, the...existingltrtie2) Surveying lsata i situation’ alternatives I altraivDsad Is this alternative -" altrnav acceptable? ANALYSIS o NOYES SHave a sufficient

Comparative analysis of tree classification models for detecting fusarium oxysporum f. sp cubense (TR4) based on multi soil sensor parameters

NASA Astrophysics Data System (ADS)

Estuar, Maria Regina Justina; Victorino, John Noel; Coronel, Andrei; Co, Jerelyn; Tiausas, Francis; Señires, Chiara Veronica

2017-09-01

Use of wireless sensor networks and smartphone integration design to monitor environmental parameters surrounding plantations is made possible because of readily available and affordable sensors. Providing low cost monitoring devices would be beneficial, especially to small farm owners, in a developing country like the Philippines, where agriculture covers a significant amount of the labor market. This study discusses the integration of wireless soil sensor devices and smartphones to create an application that will use multidimensional analysis to detect the presence or absence of plant disease. Specifically, soil sensors are designed to collect soil quality parameters in a sink node from which the smartphone collects data from via Bluetooth. Given these, there is a need to develop a classification model on the mobile phone that will report infection status of a soil. Though tree classification is the most appropriate approach for continuous parameter-based datasets, there is a need to determine whether tree models will result to coherent results or not. Soil sensor data that resides on the phone is modeled using several variations of decision tree, namely: decision tree (DT), best-fit (BF) decision tree, functional tree (FT), Naive Bayes (NB) decision tree, J48, J48graft and LAD tree, where decision tree approaches the problem by considering all sensor nodes as one. Results show that there are significant differences among soil sensor parameters indicating that there are variances in scores between the infected and uninfected sites. Furthermore, analysis of variance in accuracy, recall, precision and F1 measure scores from tree classification models homogeneity among NBTree, J48graft and J48 tree classification models.

A decision modeling for phasor measurement unit location selection in smart grid systems

NASA Astrophysics Data System (ADS)

Lee, Seung Yup

As a key technology for enhancing the smart grid system, Phasor Measurement Unit (PMU) provides synchronized phasor measurements of voltages and currents of wide-area electric power grid. With various benefits from its application, one of the critical issues in utilizing PMUs is the optimal site selection of units. The main aim of this research is to develop a decision support system, which can be used in resource allocation task for smart grid system analysis. As an effort to suggest a robust decision model and standardize the decision modeling process, a harmonized modeling framework, which considers operational circumstances of component, is proposed in connection with a deterministic approach utilizing integer programming. With the results obtained from the optimal PMU placement problem, the advantages and potential that the harmonized modeling process possesses are assessed and discussed.

Acquisition Management for Systems-of-Systems: Exploratory Model Development and Experimentation

DTIC Science & Technology

2009-04-22

outputs of the Requirements Development and Logical Analysis processes into alternative design solutions and selects a final design solution. Decision...Analysis Provides the basis for evaluating and selecting alternatives when decisions need to be made. Implementation Yields the lowest-level system... Dependenc y Matrix 1 ⎥ ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎢ ⎣ ⎡ 011 100 110 2 ⎥ ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎢ ⎣ ⎡ 000 100 100 a) Example of SoS b) Model Structure for Example SoS

Decision Making Analysis: Critical Factors-Based Methodology

DTIC Science & Technology

2010-04-01

the pitfalls associated with current wargaming methods such as assuming a western view of rational values in decision - making regardless of the cultures...Utilization theory slightly expands the rational decision making model as it states that “actors try to maximize their expected utility by weighing the...items to categorize the decision - making behavior of political leaders which tend to demonstrate either a rational or cognitive leaning. Leaders

Exploration decisions and firms in the mineral industries

USGS Publications Warehouse

Attanasi, E.D.

1981-01-01

The purpose of this paper is to demonstrate how physical characteristics of deposits and results of past exploration enter future exploration decisions. A proposed decision model is presented that is consistent with a set of primitive probabilistic assumptions associated with deposit size distributions and discoverability. Analysis of optimal field exploration strategy showed the likely firm responses to alternative exploration taxes and effects on the distribution of future discoveries. Examination of the probabilistic elements of the decision model indicates that changes in firm expectations associated with the distribution of deposits cannot be totally offset by changes in economic variables. ?? 1981.

A decision model applied to alcohol effects on driver signal light behavior

NASA Technical Reports Server (NTRS)

Schwartz, S. H.; Allen, R. W.

1978-01-01

A decision model including perceptual noise or inconsistency is developed from expected value theory to explain driver stop and go decisions at signaled intersections. The model is applied to behavior in a car simulation and instrumented vehicle. Objective and subjective changes in driver decision making were measured with changes in blood alcohol concentration (BAC). Treatment levels averaged 0.00, 0.10 and 0.14 BAC for a total of 26 male subjects. Data were taken for drivers approaching signal lights at three timing configurations. The correlation between model predictions and behavior was highly significant. In contrast to previous research, analysis indicates that increased BAC results in increased perceptual inconsistency, which is the primary cause of increased risk taking at low probability of success signal lights.

Selection of adequate site location during early stages of construction project management: A multi-criteria decision analysis approach

NASA Astrophysics Data System (ADS)

Marović, Ivan; Hanak, Tomaš

2017-10-01

In the management of construction projects special attention should be given to the planning as the most important phase of decision-making process. Quality decision-making based on adequate and comprehensive collaboration of all involved stakeholders is crucial in project’s early stages. Fundamental reasons for existence of this problem arise from: specific conditions of construction industry (final products are inseparable from the location i.e. location has a strong influence of building design and its structural characteristics as well as technology which will be used during construction), investors’ desires and attitudes, and influence of socioeconomic and environment aspects. Considering all mentioned reasons one can conclude that selection of adequate construction site location for future investment is complex, low structured and multi-criteria problem. To take into account all the dimensions, the proposed model for selection of adequate site location is devised. The model is based on AHP (for designing the decision-making hierarchy) and PROMETHEE (for pairwise comparison of investment locations) methods. As a result of mixing basis feature of both methods, operational synergies can be achieved in multi-criteria decision analysis. Such gives the decision-maker a sense of assurance, knowing that if the procedure proposed by the presented model has been followed, it will lead to a rational decision, carefully and systematically thought out.

Implementation of a participatory management model: analysis from a political perspective.

PubMed

Bernardes, Andrea; G Cummings, Greta; Gabriel, Carmen Silvia; Martinez Évora, Yolanda Dora; Gomes Maziero, Vanessa; Coleman-Miller, Glenda

2015-10-01

To analyse experiences of managers and nursing staff in the implementation of participatory management, specifically processes of decision-making, communication and power in a Canadian hospital. Implementing a Participatory Management Model involves change because it is focused on the needs of patients and encourages decentralisation of power and shared decisions. The study design is qualitative using observational sessions and content analysis for data analysis. We used Bolman and Deal's four-frame theoretical framework to interpret our findings. Participatory management led to advances in care, because it allowed for more dialogue and shared decision making. However, the biggest challenge has been that all major changes are still being decided centrally by the provincial executive board. Managers and directors are facing difficulties related to this change process, such as the resistance to change by some employees and limited input to decision-making affecting their areas of responsibility; however, they and their teams are working to utilise the values and principles underlying participatory management in their daily work practices. Innovative management models encourage accountability, increased motivation and satisfaction of nursing staff, and improve the quality of care. © 2014 John Wiley & Sons Ltd.

Accepting uncertainty, assessing risk: decision quality in managing wildfire, forest resource values, and new technology

Treesearch

Jeffrey G. Borchers

2005-01-01

The risks, uncertainties, and social conflicts surrounding uncharacteristic wildfire and forest resource values have defied conventional approaches to planning and decision-making. Paradoxically, the adoption of technological innovations such as risk assessment, decision analysis, and landscape simulation models by land management organizations has been limited. The...

Decision science: a scientific approach to enhance public health budgeting.

PubMed

Honoré, Peggy A; Fos, Peter J; Smith, Torney; Riley, Michael; Kramarz, Kim

2010-01-01

The allocation of resources for public health programming is a complicated and daunting responsibility. Financial decision-making processes within public health agencies are especially difficult when not supported with techniques for prioritizing and ranking alternatives. This article presents a case study of a decision analysis software model that was applied to the process of identifying funding priorities for public health services in the Spokane Regional Health District. Results on the use of this decision support system provide insights into how decision science models, which have been used for decades in business and industry, can be successfully applied to public health budgeting as a means of strengthening agency financial management processes.

Tutorial in medical decision modeling incorporating waiting lines and queues using discrete event simulation.

PubMed

Jahn, Beate; Theurl, Engelbert; Siebert, Uwe; Pfeiffer, Karl-Peter

2010-01-01

In most decision-analytic models in health care, it is assumed that there is treatment without delay and availability of all required resources. Therefore, waiting times caused by limited resources and their impact on treatment effects and costs often remain unconsidered. Queuing theory enables mathematical analysis and the derivation of several performance measures of queuing systems. Nevertheless, an analytical approach with closed formulas is not always possible. Therefore, simulation techniques are used to evaluate systems that include queuing or waiting, for example, discrete event simulation. To include queuing in decision-analytic models requires a basic knowledge of queuing theory and of the underlying interrelationships. This tutorial introduces queuing theory. Analysts and decision-makers get an understanding of queue characteristics, modeling features, and its strength. Conceptual issues are covered, but the emphasis is on practical issues like modeling the arrival of patients. The treatment of coronary artery disease with percutaneous coronary intervention including stent placement serves as an illustrative queuing example. Discrete event simulation is applied to explicitly model resource capacities, to incorporate waiting lines and queues in the decision-analytic modeling example.

How Participatory Should Environmental Governance Be? Testing the Applicability of the Vroom-Yetton-Jago Model in Public Environmental Decision-Making

NASA Astrophysics Data System (ADS)

Lührs, Nikolas; Jager, Nicolas W.; Challies, Edward; Newig, Jens

2018-02-01

Public participation is potentially useful to improve public environmental decision-making and management processes. In corporate management, the Vroom-Yetton-Jago normative decision-making model has served as a tool to help managers choose appropriate degrees of subordinate participation for effective decision-making given varying decision-making contexts. But does the model recommend participatory mechanisms that would actually benefit environmental management? This study empirically tests the improved Vroom-Jago version of the model in the public environmental decision-making context. To this end, the key variables of the Vroom-Jago model are operationalized and adapted to a public environmental governance context. The model is tested using data from a meta-analysis of 241 published cases of public environmental decision-making, yielding three main sets of findings: (1) The Vroom-Jago model proves limited in its applicability to public environmental governance due to limited variance in its recommendations. We show that adjustments to key model equations make it more likely to produce meaningful recommendations. (2) We find that in most of the studied cases, public environmental managers (implicitly) employ levels of participation close to those that would have been recommended by the model. (3) An ANOVA revealed that such cases, which conform to model recommendations, generally perform better on stakeholder acceptance and environmental standards of outputs than those that diverge from the model. Public environmental management thus benefits from carefully selected and context-sensitive modes of participation.

How Participatory Should Environmental Governance Be? Testing the Applicability of the Vroom-Yetton-Jago Model in Public Environmental Decision-Making.

PubMed

Lührs, Nikolas; Jager, Nicolas W; Challies, Edward; Newig, Jens

2018-02-01

Public participation is potentially useful to improve public environmental decision-making and management processes. In corporate management, the Vroom-Yetton-Jago normative decision-making model has served as a tool to help managers choose appropriate degrees of subordinate participation for effective decision-making given varying decision-making contexts. But does the model recommend participatory mechanisms that would actually benefit environmental management? This study empirically tests the improved Vroom-Jago version of the model in the public environmental decision-making context. To this end, the key variables of the Vroom-Jago model are operationalized and adapted to a public environmental governance context. The model is tested using data from a meta-analysis of 241 published cases of public environmental decision-making, yielding three main sets of findings: (1) The Vroom-Jago model proves limited in its applicability to public environmental governance due to limited variance in its recommendations. We show that adjustments to key model equations make it more likely to produce meaningful recommendations. (2) We find that in most of the studied cases, public environmental managers (implicitly) employ levels of participation close to those that would have been recommended by the model. (3) An ANOVA revealed that such cases, which conform to model recommendations, generally perform better on stakeholder acceptance and environmental standards of outputs than those that diverge from the model. Public environmental management thus benefits from carefully selected and context-sensitive modes of participation.

Accommodating complexity and human behaviors in decision analysis.

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

Backus, George A.; Siirola, John Daniel; Schoenwald, David Alan

2007-11-01

This is the final report for a LDRD effort to address human behavior in decision support systems. One sister LDRD effort reports the extension of this work to include actual human choices and additional simulation analyses. Another provides the background for this effort and the programmatic directions for future work. This specific effort considered the feasibility of five aspects of model development required for analysis viability. To avoid the use of classified information, healthcare decisions and the system embedding them became the illustrative example for assessment.

Use of multicriteria decision analysis for assessing the benefit and risk of over-the-counter analgesics.

PubMed

Moore, Andrew; Crossley, Anne; Ng, Bernard; Phillips, Lawrence; Sancak, Özgür; Rainsford, K D

2017-10-01

To test the ability of a multicriteria decision analysis (MCDA) model to incorporate disparate data sources of varying quality along with clinical judgement in a benefit-risk assessment of six well-known pain-relief drugs. Six over-the-counter (OTC) analgesics were evaluated against three favourable effects and eight unfavourable effects by seven experts who specialise in the relief of pain, two in a 2-day facilitated workshop whose input data and judgements were later peer-reviewed by five additional experts. Ibuprofen salts and solubilised emerged with the best benefit-risk profile, followed by naproxen, ibuprofen acid, diclofenac, paracetamol and aspirin. Multicriteria decision analysis enabled participants to evaluate the OTC analgesics against a range of favourable and unfavourable effects in a group setting that enabled all issues to be openly aired and debated. The model was easily communicated and understood by the peer reviewers, so the model should be comprehensible to physicians, pharmacists and other health professionals. © 2017 Royal Pharmaceutical Society.

Graph-based analysis of connectivity in spatially-explicit population models: HexSim and the Connectivity Analysis Toolkit

EPA Science Inventory

Background / Question / Methods Planning for the recovery of threatened species is increasingly informed by spatially-explicit population models. However, using simulation model results to guide land management decisions can be difficult due to the volume and complexity of model...

Engineering High Assurance Distributed Cyber Physical Systems

DTIC Science & Technology

2015-01-15

decisions: number of interacting agents and co-dependent decisions made in real-time without causing interference . To engineer a high assurance DART...environment specification, architecture definition, domain-specific languages, design patterns, code - generation, analysis, test-generation, and simulation...include synchronization between the models and source code , debugging at the model level, expression of the design intent, and quality of service

Clarity versus complexity: land-use modeling as a practical tool for decision-makers

USGS Publications Warehouse

Sohl, Terry L.; Claggett, Peter

2013-01-01

The last decade has seen a remarkable increase in the number of modeling tools available to examine future land-use and land-cover (LULC) change. Integrated modeling frameworks, agent-based models, cellular automata approaches, and other modeling techniques have substantially improved the representation of complex LULC systems, with each method using a different strategy to address complexity. However, despite the development of new and better modeling tools, the use of these tools is limited for actual planning, decision-making, or policy-making purposes. LULC modelers have become very adept at creating tools for modeling LULC change, but complicated models and lack of transparency limit their utility for decision-makers. The complicated nature of many LULC models also makes it impractical or even impossible to perform a rigorous analysis of modeling uncertainty. This paper provides a review of land-cover modeling approaches and the issues causes by the complicated nature of models, and provides suggestions to facilitate the increased use of LULC models by decision-makers and other stakeholders. The utility of LULC models themselves can be improved by 1) providing model code and documentation, 2) through the use of scenario frameworks to frame overall uncertainties, 3) improving methods for generalizing key LULC processes most important to stakeholders, and 4) adopting more rigorous standards for validating models and quantifying uncertainty. Communication with decision-makers and other stakeholders can be improved by increasing stakeholder participation in all stages of the modeling process, increasing the transparency of model structure and uncertainties, and developing user-friendly decision-support systems to bridge the link between LULC science and policy. By considering these options, LULC science will be better positioned to support decision-makers and increase real-world application of LULC modeling results.

Characterising bias in regulatory risk and decision analysis: An analysis of heuristics applied in health technology appraisal, chemicals regulation, and climate change governance.

PubMed

MacGillivray, Brian H

2017-08-01

In many environmental and public health domains, heuristic methods of risk and decision analysis must be relied upon, either because problem structures are ambiguous, reliable data is lacking, or decisions are urgent. This introduces an additional source of uncertainty beyond model and measurement error - uncertainty stemming from relying on inexact inference rules. Here we identify and analyse heuristics used to prioritise risk objects, to discriminate between signal and noise, to weight evidence, to construct models, to extrapolate beyond datasets, and to make policy. Some of these heuristics are based on causal generalisations, yet can misfire when these relationships are presumed rather than tested (e.g. surrogates in clinical trials). Others are conventions designed to confer stability to decision analysis, yet which may introduce serious error when applied ritualistically (e.g. significance testing). Some heuristics can be traced back to formal justifications, but only subject to strong assumptions that are often violated in practical applications. Heuristic decision rules (e.g. feasibility rules) in principle act as surrogates for utility maximisation or distributional concerns, yet in practice may neglect costs and benefits, be based on arbitrary thresholds, and be prone to gaming. We highlight the problem of rule-entrenchment, where analytical choices that are in principle contestable are arbitrarily fixed in practice, masking uncertainty and potentially introducing bias. Strategies for making risk and decision analysis more rigorous include: formalising the assumptions and scope conditions under which heuristics should be applied; testing rather than presuming their underlying empirical or theoretical justifications; using sensitivity analysis, simulations, multiple bias analysis, and deductive systems of inference (e.g. directed acyclic graphs) to characterise rule uncertainty and refine heuristics; adopting "recovery schemes" to correct for known biases; and basing decision rules on clearly articulated values and evidence, rather than convention. Copyright © 2017. Published by Elsevier Ltd.

Diffusion Modelling Reveals the Decision Making Processes Underlying Negative Judgement Bias in Rats.

PubMed

Hales, Claire A; Robinson, Emma S J; Houghton, Conor J

2016-01-01

Human decision making is modified by emotional state. Rodents exhibit similar biases during interpretation of ambiguous cues that can be altered by affective state manipulations. In this study, the impact of negative affective state on judgement bias in rats was measured using an ambiguous-cue interpretation task. Acute treatment with an anxiogenic drug (FG7142), and chronic restraint stress and social isolation both induced a bias towards more negative interpretation of the ambiguous cue. The diffusion model was fit to behavioural data to allow further analysis of the underlying decision making processes. To uncover the way in which parameters vary together in relation to affective state manipulations, independent component analysis was conducted on rate of information accumulation and distances to decision threshold parameters for control data. Results from this analysis were applied to parameters from negative affective state manipulations. These projected components were compared to control components to reveal the changes in decision making processes that are due to affective state manipulations. Negative affective bias in rodents induced by either FG7142 or chronic stress is due to a combination of more negative interpretation of the ambiguous cue, reduced anticipation of the high reward and increased anticipation of the low reward.

Integrating info-gap decision theory with robust population management: a case study using the Mountain Plover.

PubMed

van der Burg, Max Post; Tyre, Andrew J

2011-01-01

Wildlife managers often make decisions under considerable uncertainty. In the most extreme case, a complete lack of data leads to uncertainty that is unquantifiable. Information-gap decision theory deals with assessing management decisions under extreme uncertainty, but it is not widely used in wildlife management. So too, robust population management methods were developed to deal with uncertainties in multiple-model parameters. However, the two methods have not, as yet, been used in tandem to assess population management decisions. We provide a novel combination of the robust population management approach for matrix models with the information-gap decision theory framework for making conservation decisions under extreme uncertainty. We applied our model to the problem of nest survival management in an endangered bird species, the Mountain Plover (Charadrius montanus). Our results showed that matrix sensitivities suggest that nest management is unlikely to have a strong effect on population growth rate, confirming previous analyses. However, given the amount of uncertainty about adult and juvenile survival, our analysis suggested that maximizing nest marking effort was a more robust decision to maintain a stable population. Focusing on the twin concepts of opportunity and robustness in an information-gap model provides a useful method of assessing conservation decisions under extreme uncertainty.

A decision model for selecting sustainable drinking water supply and greywater reuse systems for developing communities with a case study in Cimahi, Indonesia.

PubMed

Henriques, Justin J; Louis, Garrick E

2011-01-01

Capacity Factor Analysis is a decision support system for selection of appropriate technologies for municipal sanitation services in developing communities. Developing communities are those that lack the capability to provide adequate access to one or more essential services, such as water and sanitation, to their residents. This research developed two elements of Capacity Factor Analysis: a capacity factor based classification for technologies using requirements analysis, and a matching policy for choosing technology options. First, requirements analysis is used to develop a ranking for drinking water supply and greywater reuse technologies. Second, using the Capacity Factor Analysis approach, a matching policy is developed to guide decision makers in selecting the appropriate drinking water supply or greywater reuse technology option for their community. Finally, a scenario-based informal hypothesis test is developed to assist in qualitative model validation through case study. Capacity Factor Analysis is then applied in Cimahi Indonesia as a form of validation. The completed Capacity Factor Analysis model will allow developing communities to select drinking water supply and greywater reuse systems that are safe, affordable, able to be built and managed by the community using local resources, and are amenable to expansion as the community's management capacity increases. Copyright © 2010 Elsevier Ltd. All rights reserved.

Gain Modulation by an Urgency Signal Controls the Speed–Accuracy Trade-Off in a Network Model of a Cortical Decision Circuit

PubMed Central

Standage, Dominic; You, Hongzhi; Wang, Da-Hui; Dorris, Michael C.

2011-01-01

The speed–accuracy trade-off (SAT) is ubiquitous in decision tasks. While the neural mechanisms underlying decisions are generally well characterized, the application of decision-theoretic methods to the SAT has been difficult to reconcile with experimental data suggesting that decision thresholds are inflexible. Using a network model of a cortical decision circuit, we demonstrate the SAT in a manner consistent with neural and behavioral data and with mathematical models that optimize speed and accuracy with respect to one another. In simulations of a reaction time task, we modulate the gain of the network with a signal encoding the urgency to respond. As the urgency signal builds up, the network progresses through a series of processing stages supporting noise filtering, integration of evidence, amplification of integrated evidence, and choice selection. Analysis of the network's dynamics formally characterizes this progression. Slower buildup of urgency increases accuracy by slowing down the progression. Faster buildup has the opposite effect. Because the network always progresses through the same stages, decision-selective firing rates are stereotyped at decision time. PMID:21415911

Gain modulation by an urgency signal controls the speed-accuracy trade-off in a network model of a cortical decision circuit.

PubMed

Standage, Dominic; You, Hongzhi; Wang, Da-Hui; Dorris, Michael C

2011-01-01

The speed-accuracy trade-off (SAT) is ubiquitous in decision tasks. While the neural mechanisms underlying decisions are generally well characterized, the application of decision-theoretic methods to the SAT has been difficult to reconcile with experimental data suggesting that decision thresholds are inflexible. Using a network model of a cortical decision circuit, we demonstrate the SAT in a manner consistent with neural and behavioral data and with mathematical models that optimize speed and accuracy with respect to one another. In simulations of a reaction time task, we modulate the gain of the network with a signal encoding the urgency to respond. As the urgency signal builds up, the network progresses through a series of processing stages supporting noise filtering, integration of evidence, amplification of integrated evidence, and choice selection. Analysis of the network's dynamics formally characterizes this progression. Slower buildup of urgency increases accuracy by slowing down the progression. Faster buildup has the opposite effect. Because the network always progresses through the same stages, decision-selective firing rates are stereotyped at decision time.

Reflections in the clinical practice.

PubMed

Borrell-Carrió, F; Hernández-Clemente, J C

2014-03-01

The purpose of this article is to analyze some models of expert decision and their impact on the clinical practice. We have analyzed decision-making considering the cognitive aspects (explanatory models, perceptual skills, analysis of the variability of a phenomenon, creating habits and inertia of reasoning and declarative models based on criteria). We have added the importance of emotions in decision making within highly complex situations, such as those occurring within the clinical practice. The quality of the reflective act depends, among other factors, on the ability of metacognition (thinking about what we think). Finally, we propose an educational strategy based on having a task supervisor and rectification scenarios to improve the quality of medical decision making. Copyright © 2013 Elsevier España, S.L. All rights reserved.

Consultant psychiatrists’ experiences of and attitudes towards shared decision making in antipsychotic prescribing, a qualitative study

PubMed Central

2014-01-01

Background Shared decision making represents a clinical consultation model where both clinician and service user are conceptualised as experts; information is shared bilaterally and joint treatment decisions are reached. Little previous research has been conducted to assess experience of this model in psychiatric practice. The current project therefore sought to explore the attitudes and experiences of consultant psychiatrists relating to shared decision making in the prescribing of antipsychotic medications. Methods A qualitative research design allowed the experiences and beliefs of participants in relation to shared decision making to be elicited. Purposive sampling was used to recruit participants from a range of clinical backgrounds and with varying length of clinical experience. A semi-structured interview schedule was utilised and was adapted in subsequent interviews to reflect emergent themes. Data analysis was completed in parallel with interviews in order to guide interview topics and to inform recruitment. A directed analysis method was utilised for interview analysis with themes identified being fitted to a framework identified from the research literature as applicable to the practice of shared decision making. Examples of themes contradictory to, or not adequately explained by, the framework were sought. Results A total of 26 consultant psychiatrists were interviewed. Participants expressed support for the shared decision making model, but also acknowledged that it was necessary to be flexible as the clinical situation dictated. A number of potential barriers to the process were perceived however: The commonest barrier was the clinician’s beliefs regarding the service users’ insight into their mental disorder, presented in some cases as an absolute barrier to shared decision making. In addition factors external to the clinician - service user relationship were identified as impacting on the decision making process, including; environmental factors, financial constraints as well as societal perceptions of mental disorder in general and antipsychotic medication in particular. Conclusions This project has allowed identification of potential barriers to shared decision making in psychiatric practice. Further work is necessary to observe the decision making process in clinical practice and also to identify means in which the identified barriers, in particular ‘lack of insight’, may be more effectively managed. PMID:24886121

Consultant psychiatrists' experiences of and attitudes towards shared decision making in antipsychotic prescribing, a qualitative study.

PubMed

Shepherd, Andrew; Shorthouse, Oliver; Gask, Linda

2014-05-01

Shared decision making represents a clinical consultation model where both clinician and service user are conceptualised as experts; information is shared bilaterally and joint treatment decisions are reached. Little previous research has been conducted to assess experience of this model in psychiatric practice. The current project therefore sought to explore the attitudes and experiences of consultant psychiatrists relating to shared decision making in the prescribing of antipsychotic medications. A qualitative research design allowed the experiences and beliefs of participants in relation to shared decision making to be elicited. Purposive sampling was used to recruit participants from a range of clinical backgrounds and with varying length of clinical experience. A semi-structured interview schedule was utilised and was adapted in subsequent interviews to reflect emergent themes.Data analysis was completed in parallel with interviews in order to guide interview topics and to inform recruitment. A directed analysis method was utilised for interview analysis with themes identified being fitted to a framework identified from the research literature as applicable to the practice of shared decision making. Examples of themes contradictory to, or not adequately explained by, the framework were sought. A total of 26 consultant psychiatrists were interviewed. Participants expressed support for the shared decision making model, but also acknowledged that it was necessary to be flexible as the clinical situation dictated. A number of potential barriers to the process were perceived however: The commonest barrier was the clinician's beliefs regarding the service users' insight into their mental disorder, presented in some cases as an absolute barrier to shared decision making. In addition factors external to the clinician - service user relationship were identified as impacting on the decision making process, including; environmental factors, financial constraints as well as societal perceptions of mental disorder in general and antipsychotic medication in particular. This project has allowed identification of potential barriers to shared decision making in psychiatric practice. Further work is necessary to observe the decision making process in clinical practice and also to identify means in which the identified barriers, in particular 'lack of insight', may be more effectively managed.

Predictive factors of synchronous colorectal peritoneal metastases: Development of a nomogram and study of its utilities using decision curve analysis.

PubMed

Mo, Shaobo; Dai, Weixing; Xiang, Wenqiang; Li, Qingguo; Wang, Renjie; Cai, Guoxiang

2018-05-03

The objective of this study was to summarize the clinicopathological and molecular features of synchronous colorectal peritoneal metastases (CPM). We then combined clinical and pathological variables associated with synchronous CPM into a nomogram and confirmed its utilities using decision curve analysis. Synchronous metastatic colorectal cancer (mCRC) patients who received primary tumor resection and underwent KRAS, NRAS, and BRAF gene mutation detection at our center from January 2014 to September 2015 were included in this retrospective study. An analysis was performed to investigate the clinicopathological and molecular features for independent risk factors of synchronous CPM and to subsequently develop a nomogram for synchronous CPM based on multivariate logistic regression. Model performance was quantified in terms of calibration and discrimination. We studied the utility of the nomogram using decision curve analysis. In total, 226 patients were diagnosed with synchronous mCRC, of whom 50 patients (22.1%) presented with CPM. After uni- and multivariate analysis, a nomogram was built based on tumor site, histological type, age, and T4 status. The model had good discrimination with an area under the curve (AUC) at 0.777 (95% CI 0.703-0.850) and adequate calibration. By decision curve analysis, the model was shown to be relevant between thresholds of 0.10 and 0.66. Synchronous CPM is more likely to happen to patients with age ≤60, right-sided primary lesions, signet ring cell cancer or T4 stage. This is the first nomogram to predict synchronous CPM. To ensure generalizability, this model needs to be externally validated. Copyright © 2018 IJS Publishing Group Ltd. Published by Elsevier Ltd. All rights reserved.

A new approach to hazardous materials transportation risk analysis: decision modeling to identify critical variables.

PubMed

Clark, Renee M; Besterfield-Sacre, Mary E

2009-03-01

We take a novel approach to analyzing hazardous materials transportation risk in this research. Previous studies analyzed this risk from an operations research (OR) or quantitative risk assessment (QRA) perspective by minimizing or calculating risk along a transport route. Further, even though the majority of incidents occur when containers are unloaded, the research has not focused on transportation-related activities, including container loading and unloading. In this work, we developed a decision model of a hazardous materials release during unloading using actual data and an exploratory data modeling approach. Previous studies have had a theoretical perspective in terms of identifying and advancing the key variables related to this risk, and there has not been a focus on probability and statistics-based approaches for doing this. Our decision model empirically identifies the critical variables using an exploratory methodology for a large, highly categorical database involving latent class analysis (LCA), loglinear modeling, and Bayesian networking. Our model identified the most influential variables and countermeasures for two consequences of a hazmat incident, dollar loss and release quantity, and is one of the first models to do this. The most influential variables were found to be related to the failure of the container. In addition to analyzing hazmat risk, our methodology can be used to develop data-driven models for strategic decision making in other domains involving risk.

Cloud Geospatial Analysis Tools for Global-Scale Comparisons of Population Models for Decision Making

NASA Astrophysics Data System (ADS)

Hancher, M.; Lieber, A.; Scott, L.

2017-12-01

The volume of satellite and other Earth data is growing rapidly. Combined with information about where people are, these data can inform decisions in a range of areas including food and water security, disease and disaster risk management, biodiversity, and climate adaptation. Google's platform for planetary-scale geospatial data analysis, Earth Engine, grants access to petabytes of continually updating Earth data, programming interfaces for analyzing the data without the need to download and manage it, and mechanisms for sharing the analyses and publishing results for data-driven decision making. In addition to data about the planet, data about the human planet - population, settlement and urban models - are now available for global scale analysis. The Earth Engine APIs enable these data to be joined, combined or visualized with economic or environmental indicators such as nighttime lights trends, global surface water, or climate projections, in the browser without the need to download anything. We will present our newly developed application intended to serve as a resource for government agencies, disaster response and public health programs, or other consumers of these data to quickly visualize the different population models, and compare them to ground truth tabular data to determine which model suits their immediate needs. Users can further tap into the power of Earth Engine and other Google technologies to perform a range of analysis from simple statistics in custom regions to more complex machine learning models. We will highlight case studies in which organizations around the world have used Earth Engine to combine population data with multiple other sources of data, such as water resources and roads data, over deep stacks of temporal imagery to model disease risk and accessibility to inform decisions.

Towards Rational Decision-Making in Secondary Education.

ERIC Educational Resources Information Center

Cohn, Elchanan

Without a conscious effort to achieve optimum resource allocation, there is a real danger that educational resources may be wasted. This document uses input-output analysis to develop a model for rational decision-making in secondary education. (LLR)

Health versus money. Value judgments in the perspective of decision analysis.

PubMed

Thompson, M S

1983-01-01

An important, but largely uninvestigated, value trade-off balances marginal nonhealth consumption against marginal medical care. Benefit-cost analysts have traditionally, if not fully satisfactorily, dealt with this issue by valuing health gains by their effects on productivity. Cost-effectiveness analysts compare monetary and health effects and leave their relative valuations to decision makers. A decision-analytic model using the satisfaction or utility gained from nonhealth consumption and the level of health enables one to calculate willingness to pay--a theoretically superior way of assigning monetary values to effects for benefit-cost analysis-and to determine minimally acceptable cost-effectiveness ratios. Examples show how a decision-analytic model of utility can differentiate medical actions so essential that failure to take them would be considered negligent from actions so expensive as to be unjustifiable, and can help to determine optimal legal arrangements for compensation for medical malpractice.

Quick Fix for Managing Risks

NASA Technical Reports Server (NTRS)

2002-01-01

Under a Phase II SBIR contract, Kennedy and Lumina Decision Systems, Inc., jointly developed the Schedule and Cost Risk Analysis Modeling (SCRAM) system, based on a version of Lumina's flagship software product, Analytica(R). Acclaimed as "the best single decision-analysis program yet produced" by MacWorld magazine, Analytica is a "visual" tool used in decision-making environments worldwide to build, revise, and present business models, minus the time-consuming difficulty commonly associated with spreadsheets. With Analytica as their platform, Kennedy and Lumina created the SCRAM system in response to NASA's need to identify the importance of major delays in Shuttle ground processing, a critical function in project management and process improvement. As part of the SCRAM development project, Lumina designed a version of Analytica called the Analytica Design Engine (ADE) that can be easily incorporated into larger software systems. ADE was commercialized and utilized in many other developments, including web-based decision support.

Decision Aid Use in Primary Care: An Overview and Theory-Based Framework.

PubMed

Shultz, Cameron G; Jimbo, Masahito

2015-10-01

Increasing patients' participation in health care is a commonly cited goal. While patient decision aids can promote participation, they remain underutilized. Theory-based models that assess barriers and facilitators to sustained decision aid use are needed. The ready, willing, and able model specifies three preconditions for behavioral change. We present a descriptive analysis of the uptake of patient decision aids in the primary care setting and show how the ready, willing, and able model can be used to identify potential barriers and facilitators. An Ovid Medline literature search from January 2004 to November 2014 was used; additional sources were identified from reference lists and through peer consultations. Barriers and facilitators to decision aid use were identified and grouped into salient themes. The ready, willing, and able model provided a simple yet practical framework for identifying the mechanisms that facilitate (or work against) the adoption of patient decision aids within primary care. While time was a prominent barrier, additional barriers such as perceived legitimacy, clinic capacity, processes of care, and the overarching health care environment were also noted. The ready, willing, and able model posits that several preconditions must first be satisfied before sustained use of patient decision aids can take hold. By pinpointing bottlenecks, the model can inform policies and tailored interventions to target identified problems. Using the model to troubleshoot for bottlenecks prior to the implementation of a decision aid could help to improve uptake and sustained use within the primary care setting.

Modeling the value for money of changing clinical practice change: a stochastic application in diabetes care.

PubMed

Hoomans, Ties; Abrams, Keith R; Ament, Andre J H A; Evers, Silvia M A A; Severens, Johan L

2009-10-01

Decision making about resource allocation for guideline implementation to change clinical practice is inevitably undertaken in a context of uncertainty surrounding the cost-effectiveness of both clinical guidelines and implementation strategies. Adopting a total net benefit approach, a model was recently developed to overcome problems with the use of combined ratio statistics when analyzing decision uncertainty. To demonstrate the stochastic application of the model for informing decision making about the adoption of an audit and feedback strategy for implementing a guideline recommending intensive blood glucose control in type 2 diabetes in primary care in the Netherlands. An integrated Bayesian approach to decision modeling and evidence synthesis is adopted, using Markov Chain Monte Carlo simulation in WinBUGs. Data on model parameters is gathered from various sources, with effectiveness of implementation being estimated using pooled, random-effects meta-analysis. Decision uncertainty is illustrated using cost-effectiveness acceptability curves and frontier. Decisions about whether to adopt intensified glycemic control and whether to adopt audit and feedback alter for the maximum values that decision makers are willing to pay for health gain. Through simultaneously incorporating uncertain economic evidence on both guidance and implementation strategy, the cost-effectiveness acceptability curves and cost-effectiveness acceptability frontier show an increase in decision uncertainty concerning guideline implementation. The stochastic application in diabetes care demonstrates that the model provides a simple and useful tool for quantifying and exploring the (combined) uncertainty associated with decision making about adopting guidelines and implementation strategies and, therefore, for informing decisions about efficient resource allocation to change clinical practice.

Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model.

PubMed

Damij, Nadja; Boškoski, Pavle; Bohanec, Marko; Mileva Boshkoska, Biljana

2016-01-01

The omnipresent need for optimisation requires constant improvements of companies' business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and "what-if" scenarios. An effectual business process simulations software (BPSS) is a prerequisite for accurate analysis of an BP. Characterisation of an BPSS tool is a challenging task due to the complex selection criteria that includes quality of visual aspects, simulation capabilities, statistical facilities, quality reporting etc. Under such circumstances, making an optimal decision is challenging. Therefore, various decision support models are employed aiding the BPSS tool selection. The currently established decision support models are either proprietary or comprise only a limited subset of criteria, which affects their accuracy. Addressing this issue, this paper proposes a new hierarchical decision support model for ranking of BPSS based on their technical characteristics by employing DEX and qualitative to quantitative (QQ) methodology. Consequently, the decision expert feeds the required information in a systematic and user friendly manner. There are three significant contributions of the proposed approach. Firstly, the proposed hierarchical model is easily extendible for adding new criteria in the hierarchical structure. Secondly, a fully operational decision support system (DSS) tool that implements the proposed hierarchical model is presented. Finally, the effectiveness of the proposed hierarchical model is assessed by comparing the resulting rankings of BPSS with respect to currently available results.

A simple way to unify multicriteria decision analysis (MCDA) and stochastic multicriteria acceptability analysis (SMAA) using a Dirichlet distribution in benefit-risk assessment.

PubMed

Saint-Hilary, Gaelle; Cadour, Stephanie; Robert, Veronique; Gasparini, Mauro

2017-05-01

Quantitative methodologies have been proposed to support decision making in drug development and monitoring. In particular, multicriteria decision analysis (MCDA) and stochastic multicriteria acceptability analysis (SMAA) are useful tools to assess the benefit-risk ratio of medicines according to the performances of the treatments on several criteria, accounting for the preferences of the decision makers regarding the relative importance of these criteria. However, even in its probabilistic form, MCDA requires the exact elicitations of the weights of the criteria by the decision makers, which may be difficult to achieve in practice. SMAA allows for more flexibility and can be used with unknown or partially known preferences, but it is less popular due to its increased complexity and the high degree of uncertainty in its results. In this paper, we propose a simple model as a generalization of MCDA and SMAA, by applying a Dirichlet distribution to the weights of the criteria and by making its parameters vary. This unique model permits to fit both MCDA and SMAA, and allows for a more extended exploration of the benefit-risk assessment of treatments. The precision of its results depends on the precision parameter of the Dirichlet distribution, which could be naturally interpreted as the strength of confidence of the decision makers in their elicitation of preferences. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Are Retrenchment Decisions Rational? The Role of Information in Times of Budgetary Stress.

ERIC Educational Resources Information Center

Ashar, Hanna; Shapiro, Jonathan Z.

1990-01-01

Analysis of the relationship between performance data and changes in faculty size of 40 departments in a College of Arts and Sciences during a time of financial stress found that the rational choice model was applied to decision making. There was a systematic relationship between objective, evaluative data and policy decisions. (MLW)

An Analysis of Principals' Ethical Decision Making Using Rest's Four Component Model of Moral Behavior.

ERIC Educational Resources Information Center

Klinker, JoAnn Franklin; Hackmann, Donald G.

High school principals confront ethical dilemmas daily. This report describes a study that examined how MetLife/NASSP secondary principals of the year made ethical decisions conforming to three dispositions from Standard 5 of the ISLLC standards and whether they could identify processes used to reach those decisions through Rest's Four Component…

Rules for Rational Decision Making: An Experiment with 15- and 16-Year Old Students

ERIC Educational Resources Information Center

Guerra, Ana Teresa Antequera; Febles, Maria Candelaria Espinel

2012-01-01

Multicriteria analysis constitutes a way to model decision processes, which allow the decision maker to assess the possible implications each course of action may entail. A multicriteria problem is chosen from the Programme for International Student Assessment 2003 Report and then extended to include questions involving a choice of preferences and…

Energy-Water Nexus: Balancing the Tradeoffs between Two-Level Decision Makers

DOE PAGES

Zhang, Xiaodong; Vesselinov, Velimir Valentinov

2016-09-03

Energy-water nexus has substantially increased importance in the recent years. Synergistic approaches based on systems-analysis and mathematical models are critical for helping decision makers better understand the interrelationships and tradeoffs between energy and water. In energywater nexus management, various decision makers with different goals and preferences, which are often conflicting, are involved. These decision makers may have different controlling power over the management objectives and the decisions. They make decisions sequentially from the upper level to the lower level, challenging decision making in energy-water nexus. In order to address such planning issues, a bi-level decision model is developed, which improvesmore » upon the existing studies by integration of bi-level programming into energy-water nexus management. The developed model represents a methodological contribution to the challenge of sequential decisionmaking in energy-water nexus through provision of an integrated modeling framework/tool. An interactive fuzzy optimization methodology is introduced to seek a satisfactory solution to meet the overall satisfaction of the two-level decision makers. The tradeoffs between the two-level decision makers in energy-water nexus management are effectively addressed and quantified. Application of the proposed model to a synthetic example problem has demonstrated its applicability in practical energy-water nexus management. Optimal solutions for electricity generation, fuel supply, water supply including groundwater, surface water and recycled water, capacity expansion of the power plants, and GHG emission control are generated. In conclusion, these analyses are capable of helping decision makers or stakeholders adjust their tolerances to make informed decisions to achieve the overall satisfaction of energy-water nexus management where bi-level sequential decision making process is involved.« less

Energy-Water Nexus: Balancing the Tradeoffs between Two-Level Decision Makers

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

Zhang, Xiaodong; Vesselinov, Velimir Valentinov

Energy-water nexus has substantially increased importance in the recent years. Synergistic approaches based on systems-analysis and mathematical models are critical for helping decision makers better understand the interrelationships and tradeoffs between energy and water. In energywater nexus management, various decision makers with different goals and preferences, which are often conflicting, are involved. These decision makers may have different controlling power over the management objectives and the decisions. They make decisions sequentially from the upper level to the lower level, challenging decision making in energy-water nexus. In order to address such planning issues, a bi-level decision model is developed, which improvesmore » upon the existing studies by integration of bi-level programming into energy-water nexus management. The developed model represents a methodological contribution to the challenge of sequential decisionmaking in energy-water nexus through provision of an integrated modeling framework/tool. An interactive fuzzy optimization methodology is introduced to seek a satisfactory solution to meet the overall satisfaction of the two-level decision makers. The tradeoffs between the two-level decision makers in energy-water nexus management are effectively addressed and quantified. Application of the proposed model to a synthetic example problem has demonstrated its applicability in practical energy-water nexus management. Optimal solutions for electricity generation, fuel supply, water supply including groundwater, surface water and recycled water, capacity expansion of the power plants, and GHG emission control are generated. In conclusion, these analyses are capable of helping decision makers or stakeholders adjust their tolerances to make informed decisions to achieve the overall satisfaction of energy-water nexus management where bi-level sequential decision making process is involved.« less

Archetypes for Organisational Safety

NASA Technical Reports Server (NTRS)

Marais, Karen; Leveson, Nancy G.

2003-01-01

We propose a framework using system dynamics to model the dynamic behavior of organizations in accident analysis. Most current accident analysis techniques are event-based and do not adequately capture the dynamic complexity and non-linear interactions that characterize accidents in complex systems. In this paper we propose a set of system safety archetypes that model common safety culture flaws in organizations, i.e., the dynamic behaviour of organizations that often leads to accidents. As accident analysis and investigation tools, the archetypes can be used to develop dynamic models that describe the systemic and organizational factors contributing to the accident. The archetypes help clarify why safety-related decisions do not always result in the desired behavior, and how independent decisions in different parts of the organization can combine to impact safety.

Monitoring and decision making by people in man machine systems

NASA Technical Reports Server (NTRS)

Johannsen, G.

1979-01-01

The analysis of human monitoring and decision making behavior as well as its modeling are described. Classic and optimal control theoretical, monitoring models are surveyed. The relationship between attention allocation and eye movements is discussed. As an example of applications, the evaluation of predictor displays by means of the optimal control model is explained. Fault detection involving continuous signals and decision making behavior of a human operator engaged in fault diagnosis during different operation and maintenance situations are illustrated. Computer aided decision making is considered as a queueing problem. It is shown to what extent computer aids can be based on the state of human activity as measured by psychophysiological quantities. Finally, management information systems for different application areas are mentioned. The possibilities of mathematical modeling of human behavior in complex man machine systems are also critically assessed.

Improving Site-Specific Radiological Performance Assessments - 13431

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

Tauxe, John; Black, Paul; Catlett, Kate

2013-07-01

An improved approach is presented for conducting complete and defensible radiological site-specific performance assessments (PAs) to support radioactive waste disposal decisions. The basic tenets of PA were initiated some thirty years ago, focusing on geologic disposals and evaluating compliance with regulations. Some of these regulations were inherently probabilistic (i.e., addressing uncertainty in a quantitative fashion), such as the containment requirements of the U.S. Environmental Protection Agency's (EPA's) 40 CFR 191, Environmental Radiation Protection Standards for Management and Disposal of Spent Nuclear Fuel, High-Level and Transuranic Radioactive Wastes, Chap. 191.13 [1]. Methods of analysis were developed to meet those requirements, butmore » at their core early PAs used 'conservative' parameter values and modeling approaches. This limited the utility of such PAs to compliance evaluation, and did little to inform decisions about optimizing disposal, closure and long-term monitoring and maintenance, or, in general, maintaining doses 'as low as reasonably achievable' (ALARA). This basic approach to PA development in the United States was employed essentially unchanged through the end of the 20. century, principally by the U.S. Department of Energy (DOE). Performance assessments developed in support of private radioactive waste disposal operations, regulated by the U.S. Nuclear Regulatory Commission (NRC) and its agreement states, were typically not as sophisticated. Discussion of new approaches to PA is timely, since at the time of this writing, the DOE is in the midst of revising its Order 435.1, Radioactive Waste Management [2], and the NRC is revising 10 CFR 61, Licensing Requirements for Land Disposal of Radioactive Waste [3]. Over the previous decade, theoretical developments and improved computational technology have provided the foundation for integrating decision analysis (DA) concepts and objective-focused thinking, plus a Bayesian approach to probabilistic modeling and risk analysis, to guide improvements in PA. This decision-making approach, [4, 5, 6] provides a transparent formal framework for using a value- or objective-focused approach to decision-making. DA, as an analytical means to implement structured decision making, provides a context for both understanding how uncertainty affects decisions and for targeting uncertainty reduction. The proposed DA approach improves defensibility and transparency of decision-making. The DA approach is fully consistent with the need to perform realistic modeling (rather than conservative modeling), including evaluation of site-specific factors. Instead of using generic stylized scenarios for radionuclide fate and transport and for human exposures to radionuclides, site-specific scenarios better represent the advantages and disadvantages of alternative disposal sites or engineered designs, thus clarifying their differences as well as providing a sound basis for evaluation of site performance. The full DA approach to PA is described, from explicitly incorporating societal values through stakeholder involvement to model building. Model building involves scoping by considering features, events, processes, and exposure scenarios (FEPSs), development of a conceptual site model (CSM), translation into numerical models and subsequent computation, and model evaluation. These are implemented in a cycle of uncertainty analysis, sensitivity analysis and value of information analysis so that uncertainty can be reduced until sufficient confidence is gained in the decisions to be made. This includes the traditional focus on hydrogeological processes, but also places emphasis on other FEPSs such as biotically-induced transport and human exposure phenomena. The significance of human exposure scenarios is emphasized by modifying the traditional acronym 'FEPs' to include them, hence 'FEPSs'. The radioactive waste community is also recognizing that disposal sites are to be considered a national (or even global) resource. As such, there is a pressing need to optimize their utility within the constraints of protecting human health and the environment. Failing to do so will result in the need for additional sites or options for storing radioactive waste temporarily, assuming a continued need for radioactive waste disposal. Optimization should be performed using DA, including economic analysis, invoked if necessary through the ALARA process. The economic analysis must recognize the cost of implementation (disposal design, closure, maintenance, etc.), and intra- and inter-generational equity in order to ensure that the best possible radioactive waste management decisions are made for the protection of both current and future generations. In most cases this requires consideration of population or collective risk. (authors)« less

Generalisability in economic evaluation studies in healthcare: a review and case studies.

PubMed

Sculpher, M J; Pang, F S; Manca, A; Drummond, M F; Golder, S; Urdahl, H; Davies, L M; Eastwood, A

2004-12-01

To review, and to develop further, the methods used to assess and to increase the generalisability of economic evaluation studies. Electronic databases. Methodological studies relating to economic evaluation in healthcare were searched. This included electronic searches of a range of databases, including PREMEDLINE, MEDLINE, EMBASE and EconLit, and manual searches of key journals. The case studies of a decision analytic model involved highlighting specific features of previously published economic studies related to generalisability and location-related variability. The case-study involving the secondary analysis of cost-effectiveness analyses was based on the secondary analysis of three economic studies using data from randomised trials. The factor most frequently cited as generating variability in economic results between locations was the unit costs associated with particular resources. In the context of studies based on the analysis of patient-level data, regression analysis has been advocated as a means of looking at variability in economic results across locations. These methods have generally accepted that some components of resource use and outcomes are exchangeable across locations. Recent studies have also explored, in cost-effectiveness analysis, the use of tests of heterogeneity similar to those used in clinical evaluation in trials. The decision analytic model has been the main means by which cost-effectiveness has been adapted from trial to non-trial locations. Most models have focused on changes to the cost side of the analysis, but it is clear that the effectiveness side may also need to be adapted between locations. There have been weaknesses in some aspects of the reporting in applied cost-effectiveness studies. These may limit decision-makers' ability to judge the relevance of a study to their specific situations. The case study demonstrated the potential value of multilevel modelling (MLM). Where clustering exists by location (e.g. centre or country), MLM can facilitate correct estimates of the uncertainty in cost-effectiveness results, and also a means of estimating location-specific cost-effectiveness. The review of applied economic studies based on decision analytic models showed that few studies were explicit about their target decision-maker(s)/jurisdictions. The studies in the review generally made more effort to ensure that their cost inputs were specific to their target jurisdiction than their effectiveness parameters. Standard sensitivity analysis was the main way of dealing with uncertainty in the models, although few studies looked explicitly at variability between locations. The modelling case study illustrated how effectiveness and cost data can be made location-specific. In particular, on the effectiveness side, the example showed the separation of location-specific baseline events and pooled estimates of relative treatment effect, where the latter are assumed exchangeable across locations. A large number of factors are mentioned in the literature that might be expected to generate variation in the cost-effectiveness of healthcare interventions across locations. Several papers have demonstrated differences in the volume and cost of resource use between locations, but few studies have looked at variability in outcomes. In applied trial-based cost-effectiveness studies, few studies provide sufficient evidence for decision-makers to establish the relevance or to adjust the results of the study to their location of interest. Very few studies utilised statistical methods formally to assess the variability in results between locations. In applied economic studies based on decision models, most studies either stated their target decision-maker/jurisdiction or provided sufficient information from which this could be inferred. There was a greater tendency to ensure that cost inputs were specific to the target jurisdiction than clinical parameters. Methods to assess generalisability and variability in economic evaluation studies have been discussed extensively in the literature relating to both trial-based and modelling studies. Regression-based methods are likely to offer a systematic approach to quantifying variability in patient-level data. In particular, MLM has the potential to facilitate estimates of cost-effectiveness, which both reflect the variation in costs and outcomes between locations and also enable the consistency of cost-effectiveness estimates between locations to be assessed directly. Decision analytic models will retain an important role in adapting the results of cost-effectiveness studies between locations. Recommendations for further research include: the development of methods of evidence synthesis which model the exchangeability of data across locations and allow for the additional uncertainty in this process; assessment of alternative approaches to specifying multilevel models to the analysis of cost-effectiveness data alongside multilocation randomised trials; identification of a range of appropriate covariates relating to locations (e.g. hospitals) in multilevel models; and further assessment of the role of econometric methods (e.g. selection models) for cost-effectiveness analysis alongside observational datasets, and to increase the generalisability of randomised trials.

Applicability of aquifer impact models to support decisions at CO 2 sequestration sites

DOE PAGES

Keating, Elizabeth; Bacon, Diana; Carroll, Susan; ...

2016-07-25

The National Risk Assessment Partnership has developed a suite of tools to assess and manage risk at CO 2 sequestration sites. This capability includes polynomial or look-up table based reduced-order models (ROMs) that predict the impact of CO 2 and brine leaks on overlying aquifers. The development of these computationally-efficient models and the underlying reactive transport simulations they emulate has been documented elsewhere (Carroll et al., 2014a; Carroll et al., 2014b; Dai et al., 2014 ; Keating et al., 2016). Here in this paper, we seek to demonstrate applicability of ROM-based analysis by considering what types of decisions and aquifermore » types would benefit from the ROM analysis. We present four hypothetical examples where applying ROMs, in ensemble mode, could support decisions during a geologic CO 2 sequestration project. These decisions pertain to site selection, site characterization, monitoring network evaluation, and health impacts. In all cases, we consider potential brine/CO 2 leak rates at the base of the aquifer to be uncertain. We show that derived probabilities provide information relevant to the decision at hand. Although the ROMs were developed using site-specific data from two aquifers (High Plains and Edwards), the models accept aquifer characteristics as variable inputs and so they may have more broad applicability. We conclude that pH and TDS predictions are the most transferable to other aquifers based on the analysis of the nine water quality metrics (pH, TDS, 4 trace metals, 3 organic compounds). Guidelines are presented for determining the aquifer types for which the ROMs should be applicable.« less

Harnessing ecosystem models and multi-criteria decision analysis for the support of forest management.

PubMed

Wolfslehner, Bernhard; Seidl, Rupert

2010-12-01

The decision-making environment in forest management (FM) has changed drastically during the last decades. Forest management planning is facing increasing complexity due to a widening portfolio of forest goods and services, a societal demand for a rational, transparent decision process and rising uncertainties concerning future environmental conditions (e.g., climate change). Methodological responses to these challenges include an intensified use of ecosystem models to provide an enriched, quantitative information base for FM planning. Furthermore, multi-criteria methods are increasingly used to amalgamate information, preferences, expert judgments and value expressions, in support of the participatory and communicative dimensions of modern forestry. Although the potential of combining these two approaches has been demonstrated in a number of studies, methodological aspects in interfacing forest ecosystem models (FEM) and multi-criteria decision analysis (MCDA) are scarcely addressed explicitly. In this contribution we review the state of the art in FEM and MCDA in the context of FM planning and highlight some of the crucial issues when combining ecosystem and preference modeling. We discuss issues and requirements in selecting approaches suitable for supporting FM planning problems from the growing body of FEM and MCDA concepts. We furthermore identify two major challenges in a harmonized application of FEM-MCDA: (i) the design and implementation of an indicator-based analysis framework capturing ecological and social aspects and their interactions relevant for the decision process, and (ii) holistic information management that supports consistent use of different information sources, provides meta-information as well as information on uncertainties throughout the planning process.

Harnessing Ecosystem Models and Multi-Criteria Decision Analysis for the Support of Forest Management

NASA Astrophysics Data System (ADS)

Wolfslehner, Bernhard; Seidl, Rupert

2010-12-01

The decision-making environment in forest management (FM) has changed drastically during the last decades. Forest management planning is facing increasing complexity due to a widening portfolio of forest goods and services, a societal demand for a rational, transparent decision process and rising uncertainties concerning future environmental conditions (e.g., climate change). Methodological responses to these challenges include an intensified use of ecosystem models to provide an enriched, quantitative information base for FM planning. Furthermore, multi-criteria methods are increasingly used to amalgamate information, preferences, expert judgments and value expressions, in support of the participatory and communicative dimensions of modern forestry. Although the potential of combining these two approaches has been demonstrated in a number of studies, methodological aspects in interfacing forest ecosystem models (FEM) and multi-criteria decision analysis (MCDA) are scarcely addressed explicitly. In this contribution we review the state of the art in FEM and MCDA in the context of FM planning and highlight some of the crucial issues when combining ecosystem and preference modeling. We discuss issues and requirements in selecting approaches suitable for supporting FM planning problems from the growing body of FEM and MCDA concepts. We furthermore identify two major challenges in a harmonized application of FEM-MCDA: (i) the design and implementation of an indicator-based analysis framework capturing ecological and social aspects and their interactions relevant for the decision process, and (ii) holistic information management that supports consistent use of different information sources, provides meta-information as well as information on uncertainties throughout the planning process.

Ignorance- versus evidence-based decision making: a decision time analysis of the recognition heuristic.

PubMed

Hilbig, Benjamin E; Pohl, Rüdiger F

2009-09-01

According to part of the adaptive toolbox notion of decision making known as the recognition heuristic (RH), the decision process in comparative judgments-and its duration-is determined by whether recognition discriminates between objects. By contrast, some recently proposed alternative models predict that choices largely depend on the amount of evidence speaking for each of the objects and that decision times thus depend on the evidential difference between objects, or the degree of conflict between options. This article presents 3 experiments that tested predictions derived from the RH against those from alternative models. All experiments used naturally recognized objects without teaching participants any information and thus provided optimal conditions for application of the RH. However, results supported the alternative, evidence-based models and often conflicted with the RH. Recognition was not the key determinant of decision times, whereas differences between objects with respect to (both positive and negative) evidence predicted effects well. In sum, alternative models that allow for the integration of different pieces of information may well provide a better account of comparative judgments. (c) 2009 APA, all rights reserved.

Decision analysis of shoreline protection under climate change uncertainty

NASA Astrophysics Data System (ADS)

Chao, Philip T.; Hobbs, Benjamin F.

1997-04-01

If global warming occurs, it could significantly affect water resource distribution and availability. Yet it is unclear whether the prospect of such change is relevant to water resources management decisions being made today. We model a shoreline protection decision problem with a stochastic dynamic program (SDP) to determine whether consideration of the possibility of climate change would alter the decision. Three questions are addressed with the SDP: (l) How important is climate change compared to other uncertainties?, (2) What is the economic loss if climate change uncertainty is ignored?, and (3) How does belief in climate change affect the timing of the decision? In the case study, sensitivity analysis shows that uncertainty in real discount rates has a stronger effect upon the decision than belief in climate change. Nevertheless, a strong belief in climate change makes the shoreline protection project less attractive and often alters the decision to build it.

Optimal management of colorectal liver metastases in older patients: a decision analysis

PubMed Central

Yang, Simon; Alibhai, Shabbir MH; Kennedy, Erin D; El-Sedfy, Abraham; Dixon, Matthew; Coburn, Natalie; Kiss, Alex; Law, Calvin HL

2014-01-01

Background Comparative trials evaluating management strategies for colorectal cancer liver metastases (CLM) are lacking, especially for older patients. This study developed a decision-analytic model to quantify outcomes associated with treatment strategies for CLM in older patients. Methods A Markov-decision model was built to examine the effect on life expectancy (LE) and quality-adjusted life expectancy (QALE) for best supportive care (BSC), systemic chemotherapy (SC), radiofrequency ablation (RFA) and hepatic resection (HR). The baseline patient cohort assumptions included healthy 70-year-old CLM patients after a primary cancer resection. Event and transition probabilities and utilities were derived from a literature review. Deterministic and probabilistic sensitivity analyses were performed on all study parameters. Results In base case analysis, BSC, SC, RFA and HR yielded LEs of 11.9, 23.1, 34.8 and 37.0 months, and QALEs of 7.8, 13.2, 22.0 and 25.0 months, respectively. Model results were sensitive to age, comorbidity, length of model simulation and utility after HR. Probabilistic sensitivity analysis showed increasing preference for RFA over HR with increasing patient age. Conclusions HR may be optimal for healthy 70-year-old patients with CLM. In older patients with comorbidities, RFA may provide better LE and QALE. Treatment decisions in older cancer patients should account for patient age, comorbidities, local expertise and individual values. PMID:24961482

Advanced Productivity Analysis Methods for Air Traffic Control Operations

DTIC Science & Technology

1976-12-01

Routine Work ............................... 37 4.2.2. Surveillance Work .......................... 40 4.2.3. Conflict Prcessing Work ................... 41...crossing and overtake conflicts) includes potential- conflict recognition, assessment, and resolution decision making and A/N voice communications...makers to utilize £ .quantitative and dynamic analysis as a tool for decision - making. 1.1.3 Types of Simulation Models Although there are many ways to

Construction and Validation of the Career and Educational Decision Self-Efficacy Inventory for Secondary Students (CEDSIS)

ERIC Educational Resources Information Center

Ho, Esther Sui Chu; Sum, Kwok Wing

2018-01-01

This study aims to construct and validate the Career and Educational Decision Self-Efficacy Inventory for Secondary Students (CEDSIS) by using a sample of 2,631 students in Hong Kong. Principal component analysis yielded a three-factor structure, which demonstrated good model fit in confirmatory factor analysis. High reliability was found for the…

The Rank Hypothesis and Lexical Decision: A Reply to Adelman and Brown (2008)

ERIC Educational Resources Information Center

Murray, Wayne S.; Forster, Kenneth I.

2008-01-01

J. S. Adelman and G. D. A. Brown (2008) provided an extensive analysis of the form of word frequency and contextual diversity effects on lexical decision time. In this reply, the current authors suggest that their analysis provides a valuable tool for the evaluation of models of lexical access and that the results they report are broadly…

What is behind the priority heuristic? A mathematical analysis and comment on Brandstätter, Gigerenzer, and Hertwig (2006).

PubMed

Rieger, Marc Oliver; Wang, Mei

2008-01-01

Comments on the article by E. Brandstätter, G. Gigerenzer, and R. Hertwig. The authors discuss the priority heuristic, a recent model for decisions under risk. They reanalyze the experimental validity of this approach and discuss how these results compare with cumulative prospect theory, the currently most established model in behavioral economics. They also discuss how general models for decisions under risk based on a heuristic approach can be understood mathematically to gain some insight in their limitations. They finally consider whether the priority heuristic model can lead to some understanding of the decision process of individuals or whether it is better seen as an as-if model. (c) 2008 APA, all rights reserved

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management.

PubMed

Oddo, Perry C; Lee, Ben S; Garner, Gregory G; Srikrishnan, Vivek; Reed, Patrick M; Forest, Chris E; Keller, Klaus

2017-09-05

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies. © 2017 Society for Risk Analysis.

Based on a multi-agent system for multi-scale simulation and application of household's LUCC: a case study for Mengcha village, Mizhi county, Shaanxi province.

PubMed

Chen, Hai; Liang, Xiaoying; Li, Rui

2013-01-01

Multi-Agent Systems (MAS) offer a conceptual approach to include multi-actor decision making into models of land use change. Through the simulation based on the MAS, this paper tries to show the application of MAS in the micro scale LUCC, and reveal the transformation mechanism of difference scale. This paper starts with a description of the context of MAS research. Then, it adopts the Nested Spatial Choice (NSC) method to construct the multi-scale LUCC decision-making model. And a case study for Mengcha village, Mizhi County, Shaanxi Province is reported. Finally, the potentials and drawbacks of the following approach is discussed and concluded. From our design and implementation of the MAS in multi-scale model, a number of observations and conclusions can be drawn on the implementation and future research directions. (1) The use of the LUCC decision-making and multi-scale transformation framework provides, according to us, a more realistic modeling of multi-scale decision making process. (2) By using continuous function, rather than discrete function, to construct the decision-making of the households is more realistic to reflect the effect. (3) In this paper, attempts have been made to give a quantitative analysis to research the household interaction. And it provides the premise and foundation for researching the communication and learning among the households. (4) The scale transformation architecture constructed in this paper helps to accumulate theory and experience for the interaction research between the micro land use decision-making and the macro land use landscape pattern. Our future research work will focus on: (1) how to rational use risk aversion principle, and put the rule on rotation between household parcels into model. (2) Exploring the methods aiming at researching the household decision-making over a long period, it allows us to find the bridge between the long-term LUCC data and the short-term household decision-making. (3) Researching the quantitative method and model, especially the scenario analysis model which may reflect the interaction among different household types.

Neural systems analysis of decision making during goal-directed navigation.

PubMed

Penner, Marsha R; Mizumori, Sheri J Y

2012-01-01

The ability to make adaptive decisions during goal-directed navigation is a fundamental and highly evolved behavior that requires continual coordination of perceptions, learning and memory processes, and the planning of behaviors. Here, a neurobiological account for such coordination is provided by integrating current literatures on spatial context analysis and decision-making. This integration includes discussions of our current understanding of the role of the hippocampal system in experience-dependent navigation, how hippocampal information comes to impact midbrain and striatal decision making systems, and finally the role of the striatum in the implementation of behaviors based on recent decisions. These discussions extend across cellular to neural systems levels of analysis. Not only are key findings described, but also fundamental organizing principles within and across neural systems, as well as between neural systems functions and behavior, are emphasized. It is suggested that studying decision making during goal-directed navigation is a powerful model for studying interactive brain systems and their mediation of complex behaviors. Copyright © 2011. Published by Elsevier Ltd.

Decision-Oriented Health Technology Assessment: One Step Forward in Supporting the Decision-Making Process in Hospitals.

PubMed

Ritrovato, Matteo; Faggiano, Francesco C; Tedesco, Giorgia; Derrico, Pietro

2015-06-01

This article outlines the Decision-Oriented Health Technology Assessment: a new implementation of the European network for Health Technology Assessment Core Model, integrating the multicriteria decision-making analysis by using the analytic hierarchy process to introduce a standardized methodological approach as a valued and shared tool to support health care decision making within a hospital. Following the Core Model as guidance (European network for Health Technology Assessment. HTA core model for medical and surgical interventions. Available from: http://www.eunethta.eu/outputs/hta-core-model-medical-and-surgical-interventions-10r. [Accessed May 27, 2014]), it is possible to apply the analytic hierarchy process to break down a problem into its constituent parts and identify priorities (i.e., assigning a weight to each part) in a hierarchical structure. Thus, it quantitatively compares the importance of multiple criteria in assessing health technologies and how the alternative technologies perform in satisfying these criteria. The verbal ratings are translated into a quantitative form by using the Saaty scale (Saaty TL. Decision making with the analytic hierarchy process. Int J Serv Sci 2008;1:83-98). An eigenvectors analysis is used for deriving the weights' systems (i.e., local and global weights' system) that reflect the importance assigned to the criteria and the priorities related to the performance of the alternative technologies. Compared with the Core Model, this methodological approach supplies a more timely as well as contextualized evidence for a specific technology, making it possible to obtain data that are more relevant and easier to interpret, and therefore more useful for decision makers to make investment choices with greater awareness. We reached the conclusion that although there may be scope for improvement, this implementation is a step forward toward the goal of building a "solid bridge" between the scientific evidence and the final decision maker's choice. Copyright © 2015 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Benefit-cost analysis framework for evaluating inter-city transit investment.

DOT National Transportation Integrated Search

2008-10-01

This report describes the development and application of a benefit/cost analysis (BCA) model to support the evaluation of investment decisions for intercity bus services. The model recognizes two principle types of intercity bus benefits: benefits th...

Bureaucrats Versus the Ballot Box in Foreign Policy Decision Making: An Experimental Analysis of the Bureaucratic Politics Model and the Poliheuristic Theory

ERIC Educational Resources Information Center

Christensen, Eben J.; Redd, Steven B.

2004-01-01

The bureaucratic politics model and the poliheuristic theory are used to examine how political advice presented in various contexts influences choice. Organizational advisers who offer endogenous political advice are compared with situations in which the decision maker is offered advice by a separate, or exogenous, political adviser. Results show…

Career Decision Self-Efficacy Scale-Short Form: A Rasch Analysis of the Portuguese Version

ERIC Educational Resources Information Center

Miguel, Jose P.; Silva, Jose T.; Prieto, Gerardo

2013-01-01

The present study analyzes the psychometric properties of the Career Decision Self-Efficacy Scale-Short Form (CDSE-SF) in a sample of Portuguese secondary education students using the Rasch model. The results indicate that the 25 items of the CDSE-SF are well fitted to a latent unidimensional structure, as required by Rasch modeling. The response…

Hierarchical Bayes approach for subgroup analysis.

PubMed

Hsu, Yu-Yi; Zalkikar, Jyoti; Tiwari, Ram C

2017-01-01

In clinical data analysis, both treatment effect estimation and consistency assessment are important for a better understanding of the drug efficacy for the benefit of subjects in individual subgroups. The linear mixed-effects model has been used for subgroup analysis to describe treatment differences among subgroups with great flexibility. The hierarchical Bayes approach has been applied to linear mixed-effects model to derive the posterior distributions of overall and subgroup treatment effects. In this article, we discuss the prior selection for variance components in hierarchical Bayes, estimation and decision making of the overall treatment effect, as well as consistency assessment of the treatment effects across the subgroups based on the posterior predictive p-value. Decision procedures are suggested using either the posterior probability or the Bayes factor. These decision procedures and their properties are illustrated using a simulated example with normally distributed response and repeated measurements.

Acquisition and production of skilled behavior in dynamic decision-making tasks

NASA Technical Reports Server (NTRS)

Kirlik, Alex

1992-01-01

Detailed summaries of two NASA-funded research projects are provided. The first project was an ecological task analysis of the Star Cruiser model. Star Cruiser is a psychological model designed to test a subject's level of cognitive activity. Ecological task analysis is used as a framework to predict the types of cognitive activity required to achieve productive behavior and to suggest how interfaces can be manipulated to alleviate certain types of cognitive demands. The second project is presented in the form of a thesis for the Masters Degree. The thesis discusses the modeling of decision-making through the use of neural network and genetic-algorithm machine learning technologies.

Comparing decision making between cancer patients and the general population: thoughts, emotions, or social influence?

PubMed

Yang, Z Janet; McComas, Katherine A; Gay, Geri K; Leonard, John P; Dannenberg, Andrew J; Dillon, Hildy

2012-01-01

This study extends a risk information seeking and processing model to explore the relative effect of cognitive processing strategies, positive and negative emotions, and normative beliefs on individuals' decision making about potential health risks. Most previous research based on this theoretical framework has examined environmental risks. Applying this risk communication model to study health decision making presents an opportunity to explore theoretical boundaries of the model, while also bringing this research to bear on a pressing medical issue: low enrollment in clinical trials. Comparative analysis of data gathered from 2 telephone surveys of a representative national sample (n = 500) and a random sample of cancer patients (n = 411) indicated that emotions played a more substantive role in cancer patients' decisions to enroll in a potential trial, whereas cognitive processing strategies and normative beliefs had greater influences on the decisions of respondents from the national sample.

Decision-making for foot-and-mouth disease control: Objectives matter

USGS Publications Warehouse

Probert, William J. M.; Shea, Katriona; Fonnesbeck, Christopher J.; Runge, Michael C.; Carpenter, Tim E.; Durr, Salome; Garner, M. Graeme; Harvey, Neil; Stevenson, Mark A.; Webb, Colleen T.; Werkman, Marleen; Tildesley, Michael J.; Ferrari, Matthew J.

2016-01-01

Formal decision-analytic methods can be used to frame disease control problems, the first step of which is to define a clear and specific objective. We demonstrate the imperative of framing clearly-defined management objectives in finding optimal control actions for control of disease outbreaks. We illustrate an analysis that can be applied rapidly at the start of an outbreak when there are multiple stakeholders involved with potentially multiple objectives, and when there are also multiple disease models upon which to compare control actions. The output of our analysis frames subsequent discourse between policy-makers, modellers and other stakeholders, by highlighting areas of discord among different management objectives and also among different models used in the analysis. We illustrate this approach in the context of a hypothetical foot-and-mouth disease (FMD) outbreak in Cumbria, UK using outputs from five rigorously-studied simulation models of FMD spread. We present both relative rankings and relative performance of controls within each model and across a range of objectives. Results illustrate how control actions change across both the base metric used to measure management success and across the statistic used to rank control actions according to said metric. This work represents a first step towards reconciling the extensive modelling work on disease control problems with frameworks for structured decision making.

Decision aids for multiple-decision disease management as affected by weather input errors.

PubMed

Pfender, W F; Gent, D H; Mahaffee, W F; Coop, L B; Fox, A D

2011-06-01

Many disease management decision support systems (DSSs) rely, exclusively or in part, on weather inputs to calculate an indicator for disease hazard. Error in the weather inputs, typically due to forecasting, interpolation, or estimation from off-site sources, may affect model calculations and management decision recommendations. The extent to which errors in weather inputs affect the quality of the final management outcome depends on a number of aspects of the disease management context, including whether management consists of a single dichotomous decision, or of a multi-decision process extending over the cropping season(s). Decision aids for multi-decision disease management typically are based on simple or complex algorithms of weather data which may be accumulated over several days or weeks. It is difficult to quantify accuracy of multi-decision DSSs due to temporally overlapping disease events, existence of more than one solution to optimizing the outcome, opportunities to take later recourse to modify earlier decisions, and the ongoing, complex decision process in which the DSS is only one component. One approach to assessing importance of weather input errors is to conduct an error analysis in which the DSS outcome from high-quality weather data is compared with that from weather data with various levels of bias and/or variance from the original data. We illustrate this analytical approach for two types of DSS, an infection risk index for hop powdery mildew and a simulation model for grass stem rust. Further exploration of analysis methods is needed to address problems associated with assessing uncertainty in multi-decision DSSs.

What perceptions do patients have of decision making (DM)? Toward an integrative patient-centered care model. A qualitative study using focus-group interviews.

PubMed

Moreau, Alain; Carol, Laurent; Dedianne, Marie Cécile; Dupraz, Christian; Perdrix, Corinne; Lainé, Xavier; Souweine, Gilbert

2012-05-01

To understand patients' perceptions of decision making and identify relationships among decision-making models. This qualitative study was made up of four focus group interviews (elderly persons, users of health support groups, students, and rural inhabitants). Participants were asked to report their perceptions of decision making in three written clinical scenarios (hypertension, breast cancer, prostate cancer). The analysis was based on the principles of grounded theory. Most patients perceived decision making as shared decision making, a deliberative question-response interaction with the physician that allowed patients to be experts in obtaining clearer information, participating in the care process, and negotiating compromises with physician preferences. Requesting second opinions allowed patients to maintain control, even within the paternalistic model preferred by elderly persons. Facilitating factors (trust, qualitative non-verbal communication, time to think) and obstacles (serious/emergency situations, perceived inadequate scientific competence, problems making requests, fear of knowing) were also part of shared decision making. In the global concept of patient-centered care, shared decision making can be flexible and can integrate paternalistic and informative models. Physicians' expertise should be associated with biomedical and relational skills through listening to, informing, and advising patients, and by supporting patients' choices. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Multi-Criteria Decision Making For Determining A Simple Model of Supplier Selection

NASA Astrophysics Data System (ADS)

Harwati

2017-06-01

Supplier selection is a decision with many criteria. Supplier selection model usually involves more than five main criteria and more than 10 sub-criteria. In fact many model includes more than 20 criteria. Too many criteria involved in supplier selection models sometimes make it difficult to apply in many companies. This research focuses on designing supplier selection that easy and simple to be applied in the company. Analytical Hierarchy Process (AHP) is used to weighting criteria. The analysis results there are four criteria that are easy and simple can be used to select suppliers: Price (weight 0.4) shipment (weight 0.3), quality (weight 0.2) and services (weight 0.1). A real case simulation shows that simple model provides the same decision with a more complex model.

Factors predicting early postpartum glucose intolerance in Japanese women with gestational diabetes mellitus: decision-curve analysis.

PubMed

Kondo, M; Nagao, Y; Mahbub, M H; Tanabe, T; Tanizawa, Y

2018-04-29

To identify factors predicting early postpartum glucose intolerance in Japanese women with gestational diabetes mellitus, using decision-curve analysis. A retrospective cohort study was performed. The participants were 123 Japanese women with gestational diabetes who underwent 75-g oral glucose tolerance tests at 8-12 weeks after delivery. They were divided into a glucose intolerance and a normal glucose tolerance group based on postpartum oral glucose tolerance test results. Analysis of the pregnancy oral glucose tolerance test results showed predictive factors for postpartum glucose intolerance. We also evaluated the clinical usefulness of the prediction model based on decision-curve analysis. Of 123 women, 78 (63.4%) had normoglycaemia and 45 (36.6%) had glucose intolerance. Multivariable logistic regression analysis showed insulinogenic index/fasting immunoreactive insulin and summation of glucose levels, assessed during pregnancy oral glucose tolerance tests (total glucose), to be independent risk factors for postpartum glucose intolerance. Evaluating the regression models, the best discrimination (area under the curve 0.725) was obtained using the basic model (i.e. age, family history of diabetes, BMI ≥25 kg/m 2 and use of insulin during pregnancy) plus insulinogenic index/fasting immunoreactive insulin <1.1. Decision-curve analysis showed that combining insulinogenic index/fasting immunoreactive insulin <1.1 with basic clinical information resulted in superior net benefits for prediction of postpartum glucose intolerance. Insulinogenic index/fasting immunoreactive insulin calculated using oral glucose tolerance test results during pregnancy is potentially useful for predicting early postpartum glucose intolerance in Japanese women with gestational diabetes. © 2018 Diabetes UK.

Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences.

PubMed

Constantinou, Anthony Costa; Yet, Barbaros; Fenton, Norman; Neil, Martin; Marsh, William

2016-01-01

Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision. The method is based on the underlying principle of Value of Information to enhance decision analysis in interventional and counterfactual Bayesian networks. The method is applied to two real-world Bayesian network models (previously developed for decision support in forensic medical sciences) to examine the average gain in terms of both Value of Information (average relative gain ranging from 11.45% and 59.91%) and decision making (potential amendments in decision making ranging from 0% to 86.8%). We have shown how the method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from the trouble of seeking information about the particular set of risk factors. Further, we have also extended the assessment of this implication to the counterfactual case and demonstrated how answers about interventional actions are expected to change when some unknown factors become known, and how useful this becomes in forensic medical science. Copyright © 2015 Elsevier B.V. All rights reserved.

An uncertainty analysis of wildfire modeling [Chapter 13

Treesearch

Karin Riley; Matthew Thompson

2017-01-01

Before fire models can be understood, evaluated, and effectively applied to support decision making, model-based uncertainties must be analyzed. In this chapter, we identify and classify sources of uncertainty using an established analytical framework, and summarize results graphically in an uncertainty matrix. Our analysis facilitates characterization of the...

The application of data mining techniques to oral cancer prognosis.

PubMed

Tseng, Wan-Ting; Chiang, Wei-Fan; Liu, Shyun-Yeu; Roan, Jinsheng; Lin, Chun-Nan

2015-05-01

This study adopted an integrated procedure that combines the clustering and classification features of data mining technology to determine the differences between the symptoms shown in past cases where patients died from or survived oral cancer. Two data mining tools, namely decision tree and artificial neural network, were used to analyze the historical cases of oral cancer, and their performance was compared with that of logistic regression, the popular statistical analysis tool. Both decision tree and artificial neural network models showed superiority to the traditional statistical model. However, as to clinician, the trees created by the decision tree models are relatively easier to interpret compared to that of the artificial neural network models. Cluster analysis also discovers that those stage 4 patients whose also possess the following four characteristics are having an extremely low survival rate: pN is N2b, level of RLNM is level I-III, AJCC-T is T4, and cells mutate situation (G) is moderate.

Risk-Based Prioritization of Research for Aviation Security Using Logic-Evolved Decision Analysis

NASA Technical Reports Server (NTRS)

Eisenhawer, S. W.; Bott, T. F.; Sorokach, M. R.; Jones, F. P.; Foggia, J. R.

2004-01-01

The National Aeronautics and Space Administration is developing advanced technologies to reduce terrorist risk for the air transportation system. Decision support tools are needed to help allocate assets to the most promising research. An approach to rank ordering technologies (using logic-evolved decision analysis), with risk reduction as the metric, is presented. The development of a spanning set of scenarios using a logic-gate tree is described. Baseline risk for these scenarios is evaluated with an approximate reasoning model. Illustrative risk and risk reduction results are presented.

Extensions to regret-based decision curve analysis: an application to hospice referral for terminal patients.

PubMed

Tsalatsanis, Athanasios; Barnes, Laura E; Hozo, Iztok; Djulbegovic, Benjamin

2011-12-23

Despite the well documented advantages of hospice care, most terminally ill patients do not reap the maximum benefit from hospice services, with the majority of them receiving hospice care either prematurely or delayed. Decision systems to improve the hospice referral process are sorely needed. We present a novel theoretical framework that is based on well-established methodologies of prognostication and decision analysis to assist with the hospice referral process for terminally ill patients. We linked the SUPPORT statistical model, widely regarded as one of the most accurate models for prognostication of terminally ill patients, with the recently developed regret based decision curve analysis (regret DCA). We extend the regret DCA methodology to consider harms associated with the prognostication test as well as harms and effects of the management strategies. In order to enable patients and physicians in making these complex decisions in real-time, we developed an easily accessible web-based decision support system available at the point of care. The web-based decision support system facilitates the hospice referral process in three steps. First, the patient or surrogate is interviewed to elicit his/her personal preferences regarding the continuation of life-sustaining treatment vs. palliative care. Then, regret DCA is employed to identify the best strategy for the particular patient in terms of threshold probability at which he/she is indifferent between continuation of treatment and of hospice referral. Finally, if necessary, the probabilities of survival and death for the particular patient are computed based on the SUPPORT prognostication model and contrasted with the patient's threshold probability. The web-based design of the CDSS enables patients, physicians, and family members to participate in the decision process from anywhere internet access is available. We present a theoretical framework to facilitate the hospice referral process. Further rigorous clinical evaluation including testing in a prospective randomized controlled trial is required and planned.

Extensions to Regret-based Decision Curve Analysis: An application to hospice referral for terminal patients

PubMed Central

2011-01-01

Background Despite the well documented advantages of hospice care, most terminally ill patients do not reap the maximum benefit from hospice services, with the majority of them receiving hospice care either prematurely or delayed. Decision systems to improve the hospice referral process are sorely needed. Methods We present a novel theoretical framework that is based on well-established methodologies of prognostication and decision analysis to assist with the hospice referral process for terminally ill patients. We linked the SUPPORT statistical model, widely regarded as one of the most accurate models for prognostication of terminally ill patients, with the recently developed regret based decision curve analysis (regret DCA). We extend the regret DCA methodology to consider harms associated with the prognostication test as well as harms and effects of the management strategies. In order to enable patients and physicians in making these complex decisions in real-time, we developed an easily accessible web-based decision support system available at the point of care. Results The web-based decision support system facilitates the hospice referral process in three steps. First, the patient or surrogate is interviewed to elicit his/her personal preferences regarding the continuation of life-sustaining treatment vs. palliative care. Then, regret DCA is employed to identify the best strategy for the particular patient in terms of threshold probability at which he/she is indifferent between continuation of treatment and of hospice referral. Finally, if necessary, the probabilities of survival and death for the particular patient are computed based on the SUPPORT prognostication model and contrasted with the patient's threshold probability. The web-based design of the CDSS enables patients, physicians, and family members to participate in the decision process from anywhere internet access is available. Conclusions We present a theoretical framework to facilitate the hospice referral process. Further rigorous clinical evaluation including testing in a prospective randomized controlled trial is required and planned. PMID:22196308

Method Development for Clinical Comprehensive Evaluation of Pediatric Drugs Based on Multi-Criteria Decision Analysis: Application to Inhaled Corticosteroids for Children with Asthma.

PubMed

Yu, Yuncui; Jia, Lulu; Meng, Yao; Hu, Lihua; Liu, Yiwei; Nie, Xiaolu; Zhang, Meng; Zhang, Xuan; Han, Sheng; Peng, Xiaoxia; Wang, Xiaoling

2018-04-01

Establishing a comprehensive clinical evaluation system is critical in enacting national drug policy and promoting rational drug use. In China, the 'Clinical Comprehensive Evaluation System for Pediatric Drugs' (CCES-P) project, which aims to compare drugs based on clinical efficacy and cost effectiveness to help decision makers, was recently proposed; therefore, a systematic and objective method is required to guide the process. An evidence-based multi-criteria decision analysis model that involved an analytic hierarchy process (AHP) was developed, consisting of nine steps: (1) select the drugs to be reviewed; (2) establish the evaluation criterion system; (3) determine the criterion weight based on the AHP; (4) construct the evidence body for each drug under evaluation; (5) select comparative measures and calculate the original utility score; (6) place a common utility scale and calculate the standardized utility score; (7) calculate the comprehensive utility score; (8) rank the drugs; and (9) perform a sensitivity analysis. The model was applied to the evaluation of three different inhaled corticosteroids (ICSs) used for asthma management in children (a total of 16 drugs with different dosage forms and strengths or different manufacturers). By applying the drug analysis model, the 16 ICSs under review were successfully scored and evaluated. Budesonide suspension for inhalation (drug ID number: 7) ranked the highest, with comprehensive utility score of 80.23, followed by fluticasone propionate inhaled aerosol (drug ID number: 16), with a score of 79.59, and budesonide inhalation powder (drug ID number: 6), with a score of 78.98. In the sensitivity analysis, the ranking of the top five and lowest five drugs remains unchanged, suggesting this model is generally robust. An evidence-based drug evaluation model based on AHP was successfully developed. The model incorporates sufficient utility and flexibility for aiding the decision-making process, and can be a useful tool for the CCES-P.

Engineering tradeoff problems viewed as multiple objective optimizations and the VODCA methodology

NASA Astrophysics Data System (ADS)

Morgan, T. W.; Thurgood, R. L.

1984-05-01

This paper summarizes a rational model for making engineering tradeoff decisions. The model is a hybrid from the fields of social welfare economics, communications, and operations research. A solution methodology (Vector Optimization Decision Convergence Algorithm - VODCA) firmly grounded in the economic model is developed both conceptually and mathematically. The primary objective for developing the VODCA methodology was to improve the process for extracting relative value information about the objectives from the appropriate decision makers. This objective was accomplished by employing data filtering techniques to increase the consistency of the relative value information and decrease the amount of information required. VODCA is applied to a simplified hypothetical tradeoff decision problem. Possible use of multiple objective analysis concepts and the VODCA methodology in product-line development and market research are discussed.

Good modeling practice guidelines for applying multimedia models in chemical assessments.

PubMed

Buser, Andreas M; MacLeod, Matthew; Scheringer, Martin; Mackay, Don; Bonnell, Mark; Russell, Mark H; DePinto, Joseph V; Hungerbühler, Konrad

2012-10-01

Multimedia mass balance models of chemical fate in the environment have been used for over 3 decades in a regulatory context to assist decision making. As these models become more comprehensive, reliable, and accepted, there is a need to recognize and adopt principles of Good Modeling Practice (GMP) to ensure that multimedia models are applied with transparency and adherence to accepted scientific principles. We propose and discuss 6 principles of GMP for applying existing multimedia models in a decision-making context, namely 1) specification of the goals of the model assessment, 2) specification of the model used, 3) specification of the input data, 4) specification of the output data, 5) conduct of a sensitivity and possibly also uncertainty analysis, and finally 6) specification of the limitations and limits of applicability of the analysis. These principles are justified and discussed with a view to enhancing the transparency and quality of model-based assessments. Copyright © 2012 SETAC.

Considerations for Reporting Finite Element Analysis Studies in Biomechanics

PubMed Central

Erdemir, Ahmet; Guess, Trent M.; Halloran, Jason; Tadepalli, Srinivas C.; Morrison, Tina M.

2012-01-01

Simulation-based medicine and the development of complex computer models of biological structures is becoming ubiquitous for advancing biomedical engineering and clinical research. Finite element analysis (FEA) has been widely used in the last few decades to understand and predict biomechanical phenomena. Modeling and simulation approaches in biomechanics are highly interdisciplinary, involving novice and skilled developers in all areas of biomedical engineering and biology. While recent advances in model development and simulation platforms offer a wide range of tools to investigators, the decision making process during modeling and simulation has become more opaque. Hence, reliability of such models used for medical decision making and for driving multiscale analysis comes into question. Establishing guidelines for model development and dissemination is a daunting task, particularly with the complex and convoluted models used in FEA. Nonetheless, if better reporting can be established, researchers will have a better understanding of a model’s value and the potential for reusability through sharing will be bolstered. Thus, the goal of this document is to identify resources and considerate reporting parameters for FEA studies in biomechanics. These entail various levels of reporting parameters for model identification, model structure, simulation structure, verification, validation, and availability. While we recognize that it may not be possible to provide and detail all of the reporting considerations presented, it is possible to establish a level of confidence with selective use of these parameters. More detailed reporting, however, can establish an explicit outline of the decision-making process in simulation-based analysis for enhanced reproducibility, reusability, and sharing. PMID:22236526

Clinical, information and business process modeling to promote development of safe and flexible software.

PubMed

Liaw, Siaw-Teng; Deveny, Elizabeth; Morrison, Iain; Lewis, Bryn

2006-09-01

Using a factorial vignette survey and modeling methodology, we developed clinical and information models - incorporating evidence base, key concepts, relevant terms, decision-making and workflow needed to practice safely and effectively - to guide the development of an integrated rule-based knowledge module to support prescribing decisions in asthma. We identified workflows, decision-making factors, factor use, and clinician information requirements. The Unified Modeling Language (UML) and public domain software and knowledge engineering tools (e.g. Protégé) were used, with the Australian GP Data Model as the starting point for expressing information needs. A Web Services service-oriented architecture approach was adopted within which to express functional needs, and clinical processes and workflows were expressed in the Business Process Execution Language (BPEL). This formal analysis and modeling methodology to define and capture the process and logic of prescribing best practice in a reference implementation is fundamental to tackling deficiencies in prescribing decision support software.

Experimental analysis of multi-attribute decision-making based on Atanassov intuitionistic fuzzy sets: a discussion of anchor dependency and accuracy functions

NASA Astrophysics Data System (ADS)

Chen, Ting-Yu

2012-06-01

This article presents a useful method for relating anchor dependency and accuracy functions to multiple attribute decision-making (MADM) problems in the context of Atanassov intuitionistic fuzzy sets (A-IFSs). Considering anchored judgement with displaced ideals and solution precision with minimal hesitation, several auxiliary optimisation models have proposed to obtain the optimal weights of the attributes and to acquire the corresponding TOPSIS (the technique for order preference by similarity to the ideal solution) index for alternative rankings. Aside from the TOPSIS index, as a decision-maker's personal characteristics and own perception of self may also influence the direction in the axiom of choice, the evaluation of alternatives is conducted based on distances of each alternative from the positive and negative ideal alternatives, respectively. This article originates from Li's [Li, D.-F. (2005), 'Multiattribute Decision Making Models and Methods Using Intuitionistic Fuzzy Sets', Journal of Computer and System Sciences, 70, 73-85] work, which is a seminal study of intuitionistic fuzzy decision analysis using deduced auxiliary programming models, and deems it a benchmark method for comparative studies on anchor dependency and accuracy functions. The feasibility and effectiveness of the proposed methods are illustrated by a numerical example. Finally, a comparative analysis is illustrated with computational experiments on averaging accuracy functions, TOPSIS indices, separation measures from positive and negative ideal alternatives, consistency rates of ranking orders, contradiction rates of the top alternative and average Spearman correlation coefficients.

Reaching beyond the review of research evidence: a qualitative study of decision making during the development of clinical practice guidelines for disease prevention in healthcare.

PubMed

Richter Sundberg, Linda; Garvare, Rickard; Nyström, Monica Elisabeth

2017-05-11

The judgment and decision making process during guideline development is central for producing high-quality clinical practice guidelines, but the topic is relatively underexplored in the guideline research literature. We have studied the development process of national guidelines with a disease-prevention scope produced by the National board of Health and Welfare (NBHW) in Sweden. The NBHW formal guideline development model states that guideline recommendations should be based on five decision-criteria: research evidence; curative/preventive effect size, severity of the condition; cost-effectiveness; and ethical considerations. A group of health profession representatives (i.e. a prioritization group) was assigned the task of ranking condition-intervention pairs for guideline recommendations, taking into consideration the multiple decision criteria. The aim of this study was to investigate the decision making process during the two-year development of national guidelines for methods of preventing disease. A qualitative inductive longitudinal case study approach was used to investigate the decision making process. Questionnaires, non-participant observations of nine two-day group meetings, and documents provided data for the analysis. Conventional and summative qualitative content analysis was used to analyse data. The guideline development model was modified ad-hoc as the group encountered three main types of dilemmas: high quality evidence vs. low adoptability of recommendation; insufficient evidence vs. high urgency to act; and incoherence in assessment and prioritization within and between four different lifestyle areas. The formal guideline development model guided the decision-criteria used, but three new or revised criteria were added by the group: 'clinical knowledge and experience', 'potential guideline consequences' and 'needs of vulnerable groups'. The frequency of the use of various criteria in discussions varied over time. Gender, professional status, and interpersonal skills were perceived to affect individuals' relative influence on group discussions. The study shows that guideline development groups make compromises between rigour and pragmatism. The formal guideline development model incorporated multiple aspects, but offered few details on how the different criteria should be handled. The guideline development model devoted little attention to the role of the decision-model and group-related factors. Guideline development models could benefit from clarifying the role of the group-related factors and non-research evidence, such as clinical experience and ethical considerations, in decision-processes during guideline development.

A dynamic model of reasoning and memory.

PubMed

Hawkins, Guy E; Hayes, Brett K; Heit, Evan

2016-02-01

Previous models of category-based induction have neglected how the process of induction unfolds over time. We conceive of induction as a dynamic process and provide the first fine-grained examination of the distribution of response times observed in inductive reasoning. We used these data to develop and empirically test the first major quantitative modeling scheme that simultaneously accounts for inductive decisions and their time course. The model assumes that knowledge of similarity relations among novel test probes and items stored in memory drive an accumulation-to-bound sequential sampling process: Test probes with high similarity to studied exemplars are more likely to trigger a generalization response, and more rapidly, than items with low exemplar similarity. We contrast data and model predictions for inductive decisions with a recognition memory task using a common stimulus set. Hierarchical Bayesian analyses across 2 experiments demonstrated that inductive reasoning and recognition memory primarily differ in the threshold to trigger a decision: Observers required less evidence to make a property generalization judgment (induction) than an identity statement about a previously studied item (recognition). Experiment 1 and a condition emphasizing decision speed in Experiment 2 also found evidence that inductive decisions use lower quality similarity-based information than recognition. The findings suggest that induction might represent a less cautious form of recognition. We conclude that sequential sampling models grounded in exemplar-based similarity, combined with hierarchical Bayesian analysis, provide a more fine-grained and informative analysis of the processes involved in inductive reasoning than is possible solely through examination of choice data. PsycINFO Database Record (c) 2016 APA, all rights reserved.

The Value of Information in Decision-Analytic Modeling for Malaria Vector Control in East Africa.

PubMed

Kim, Dohyeong; Brown, Zachary; Anderson, Richard; Mutero, Clifford; Miranda, Marie Lynn; Wiener, Jonathan; Kramer, Randall

2017-02-01

Decision analysis tools and mathematical modeling are increasingly emphasized in malaria control programs worldwide to improve resource allocation and address ongoing challenges with sustainability. However, such tools require substantial scientific evidence, which is costly to acquire. The value of information (VOI) has been proposed as a metric for gauging the value of reduced model uncertainty. We apply this concept to an evidenced-based Malaria Decision Analysis Support Tool (MDAST) designed for application in East Africa. In developing MDAST, substantial gaps in the scientific evidence base were identified regarding insecticide resistance in malaria vector control and the effectiveness of alternative mosquito control approaches, including larviciding. We identify four entomological parameters in the model (two for insecticide resistance and two for larviciding) that involve high levels of uncertainty and to which outputs in MDAST are sensitive. We estimate and compare a VOI for combinations of these parameters in evaluating three policy alternatives relative to a status quo policy. We find having perfect information on the uncertain parameters could improve program net benefits by up to 5-21%, with the highest VOI associated with jointly eliminating uncertainty about reproductive speed of malaria-transmitting mosquitoes and initial efficacy of larviciding at reducing the emergence of new adult mosquitoes. Future research on parameter uncertainty in decision analysis of malaria control policy should investigate the VOI with respect to other aspects of malaria transmission (such as antimalarial resistance), the costs of reducing uncertainty in these parameters, and the extent to which imperfect information about these parameters can improve payoffs. © 2016 Society for Risk Analysis.

Distributed collaborative environments for predictive battlespace awareness

NASA Astrophysics Data System (ADS)

McQuay, William K.

2003-09-01

The past decade has produced significant changes in the conduct of military operations: asymmetric warfare, the reliance on dynamic coalitions, stringent rules of engagement, increased concern about collateral damage, and the need for sustained air operations. Mission commanders need to assimilate a tremendous amount of information, make quick-response decisions, and quantify the effects of those decisions in the face of uncertainty. Situational assessment is crucial in understanding the battlespace. Decision support tools in a distributed collaborative environment offer the capability of decomposing complex multitask processes and distributing them over a dynamic set of execution assets that include modeling, simulations, and analysis tools. Decision support technologies can semi-automate activities, such as analysis and planning, that have a reasonably well-defined process and provide machine-level interfaces to refine the myriad of information that the commander must fused. Collaborative environments provide the framework and integrate models, simulations, and domain specific decision support tools for the sharing and exchanging of data, information, knowledge, and actions. This paper describes ongoing AFRL research efforts in applying distributed collaborative environments to predictive battlespace awareness.

IT vendor selection model by using structural equation model & analytical hierarchy process

NASA Astrophysics Data System (ADS)

Maitra, Sarit; Dominic, P. D. D.

2012-11-01

Selecting and evaluating the right vendors is imperative for an organization's global marketplace competitiveness. Improper selection and evaluation of potential vendors can dwarf an organization's supply chain performance. Numerous studies have demonstrated that firms consider multiple criteria when selecting key vendors. This research intends to develop a new hybrid model for vendor selection process with better decision making. The new proposed model provides a suitable tool for assisting decision makers and managers to make the right decisions and select the most suitable vendor. This paper proposes a Hybrid model based on Structural Equation Model (SEM) and Analytical Hierarchy Process (AHP) for long-term strategic vendor selection problems. The five steps framework of the model has been designed after the thorough literature study. The proposed hybrid model will be applied using a real life case study to assess its effectiveness. In addition, What-if analysis technique will be used for model validation purpose.

An integrated experiment for identification of best decision styles and teamworks with respect to HSE and ergonomics program: The case of a large oil refinery.

PubMed

Azadeh, A; Mokhtari, Z; Sharahi, Z Jiryaei; Zarrin, M

2015-12-01

Decision making failure is a predominant human error in emergency situations. To demonstrate the subject model, operators of an oil refinery were asked to answer a health, safety and environment HSE-decision styles (DS) questionnaire. In order to achieve this purpose, qualitative indicators in HSE and ergonomics domain have been collected. Decision styles, related to the questions, have been selected based on Driver taxonomy of human decision making approach. Teamwork efficiency has been assessed based on different decision style combinations. The efficiency has been ranked based on HSE performance. Results revealed that efficient decision styles resulted from data envelopment analysis (DEA) optimization model is consistent with the plant's dominant styles. Therefore, improvement in system performance could be achieved using the best operator for critical posts or in team arrangements. This is the first study that identifies the best decision styles with respect to HSE and ergonomics factors. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evaluating Courses of Actions at the Strategic Planning Level

DTIC Science & Technology

2013-03-01

and statistical decision theory ( Schultz , Borrowman and Small 2011). Nowadays, it is hard to make a decision by ourselves. Modern organizations...Analysis." Lecture Slides, October 2011. Schultz , Martin T., Thomas D. Borrowman, and Mitchell J. Small. Bayesian Networks for Modeling Dredging...www.ukessays.com/essays/business/strategic-analysis-of-procter-and-gamble.php (accessed October 09, 2012). Vego, Milan . Joint Operational Warfare. Vol. Vol 1

External validation and clinical utility of a prediction model for 6-month mortality in patients undergoing hemodialysis for end-stage kidney disease.

PubMed

Forzley, Brian; Er, Lee; Chiu, Helen Hl; Djurdjev, Ognjenka; Martinusen, Dan; Carson, Rachel C; Hargrove, Gaylene; Levin, Adeera; Karim, Mohamud

2018-02-01

End-stage kidney disease is associated with poor prognosis. Health care professionals must be prepared to address end-of-life issues and identify those at high risk for dying. A 6-month mortality prediction model for patients on dialysis derived in the United States is used but has not been externally validated. We aimed to assess the external validity and clinical utility in an independent cohort in Canada. We examined the performance of the published 6-month mortality prediction model, using discrimination, calibration, and decision curve analyses. Data were derived from a cohort of 374 prevalent dialysis patients in two regions of British Columbia, Canada, which included serum albumin, age, peripheral vascular disease, dementia, and answers to the "the surprise question" ("Would I be surprised if this patient died within the next year?"). The observed mortality in the validation cohort was 11.5% at 6 months. The prediction model had reasonable discrimination (c-stat = 0.70) but poor calibration (calibration-in-the-large = -0.53 (95% confidence interval: -0.88, -0.18); calibration slope = 0.57 (95% confidence interval: 0.31, 0.83)) in our data. Decision curve analysis showed the model only has added value in guiding clinical decision in a small range of threshold probabilities: 8%-20%. Despite reasonable discrimination, the prediction model has poor calibration in this external study cohort; thus, it may have limited clinical utility in settings outside of where it was derived. Decision curve analysis clarifies limitations in clinical utility not apparent by receiver operating characteristic curve analysis. This study highlights the importance of external validation of prediction models prior to routine use in clinical practice.

The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation

NASA Astrophysics Data System (ADS)

Li, Hui; Hong, Lu-Yao; Zhou, Qing; Yu, Hai-Jie

2015-08-01

The business failure of numerous companies results in financial crises. The high social costs associated with such crises have made people to search for effective tools for business risk prediction, among which, support vector machine is very effective. Several modelling means, including single-technique modelling, hybrid modelling, and ensemble modelling, have been suggested in forecasting business risk with support vector machine. However, existing literature seldom focuses on the general modelling frame for business risk prediction, and seldom investigates performance differences among different modelling means. We reviewed researches on forecasting business risk with support vector machine, proposed the general assisted prediction modelling frame with hybridisation and ensemble (APMF-WHAE), and finally, investigated the use of principal components analysis, support vector machine, random sampling, and group decision, under the general frame in forecasting business risk. Under the APMF-WHAE frame with support vector machine as the base predictive model, four specific predictive models were produced, namely, pure support vector machine, a hybrid support vector machine involved with principal components analysis, a support vector machine ensemble involved with random sampling and group decision, and an ensemble of hybrid support vector machine using group decision to integrate various hybrid support vector machines on variables produced from principle components analysis and samples from random sampling. The experimental results indicate that hybrid support vector machine and ensemble of hybrid support vector machines were able to produce dominating performance than pure support vector machine and support vector machine ensemble.

Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model

PubMed Central

2016-01-01

The omnipresent need for optimisation requires constant improvements of companies’ business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and “what-if” scenarios. An effectual business process simulations software (BPSS) is a prerequisite for accurate analysis of an BP. Characterisation of an BPSS tool is a challenging task due to the complex selection criteria that includes quality of visual aspects, simulation capabilities, statistical facilities, quality reporting etc. Under such circumstances, making an optimal decision is challenging. Therefore, various decision support models are employed aiding the BPSS tool selection. The currently established decision support models are either proprietary or comprise only a limited subset of criteria, which affects their accuracy. Addressing this issue, this paper proposes a new hierarchical decision support model for ranking of BPSS based on their technical characteristics by employing DEX and qualitative to quantitative (QQ) methodology. Consequently, the decision expert feeds the required information in a systematic and user friendly manner. There are three significant contributions of the proposed approach. Firstly, the proposed hierarchical model is easily extendible for adding new criteria in the hierarchical structure. Secondly, a fully operational decision support system (DSS) tool that implements the proposed hierarchical model is presented. Finally, the effectiveness of the proposed hierarchical model is assessed by comparing the resulting rankings of BPSS with respect to currently available results. PMID:26871694

Game theoretic analysis of physical protection system design

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

Canion, B.; Schneider, E.; Bickel, E.

The physical protection system (PPS) of a fictional small modular reactor (SMR) facility have been modeled as a platform for a game theoretic approach to security decision analysis. To demonstrate the game theoretic approach, a rational adversary with complete knowledge of the facility has been modeled attempting a sabotage attack. The adversary adjusts his decisions in response to investments made by the defender to enhance the security measures. This can lead to a conservative physical protection system design. Since defender upgrades were limited by a budget, cost benefit analysis may be conducted upon security upgrades. One approach to cost benefitmore » analysis is the efficient frontier, which depicts the reduction in expected consequence per incremental increase in the security budget.« less

An Organizational Informatics Analysis of Colorectal, Breast, and Cervical Cancer Screening Clinical Decision Support and Information Systems within Community Health Centers

ERIC Educational Resources Information Center

Carney, Timothy Jay

2012-01-01

A study design has been developed that employs a dual modeling approach to identify factors associated with facility-level cancer screening improvement and how this is mediated by the use of clinical decision support. This dual modeling approach combines principles of (1) Health Informatics, (2) Cancer Prevention and Control, (3) Health Services…

Diffusion Modelling Reveals the Decision Making Processes Underlying Negative Judgement Bias in Rats

PubMed Central

Hales, Claire A.; Robinson, Emma S. J.; Houghton, Conor J.

2016-01-01

Human decision making is modified by emotional state. Rodents exhibit similar biases during interpretation of ambiguous cues that can be altered by affective state manipulations. In this study, the impact of negative affective state on judgement bias in rats was measured using an ambiguous-cue interpretation task. Acute treatment with an anxiogenic drug (FG7142), and chronic restraint stress and social isolation both induced a bias towards more negative interpretation of the ambiguous cue. The diffusion model was fit to behavioural data to allow further analysis of the underlying decision making processes. To uncover the way in which parameters vary together in relation to affective state manipulations, independent component analysis was conducted on rate of information accumulation and distances to decision threshold parameters for control data. Results from this analysis were applied to parameters from negative affective state manipulations. These projected components were compared to control components to reveal the changes in decision making processes that are due to affective state manipulations. Negative affective bias in rodents induced by either FG7142 or chronic stress is due to a combination of more negative interpretation of the ambiguous cue, reduced anticipation of the high reward and increased anticipation of the low reward. PMID:27023442

Cognitive mapping tools: review and risk management needs.

PubMed

Wood, Matthew D; Bostrom, Ann; Bridges, Todd; Linkov, Igor

2012-08-01

Risk managers are increasingly interested in incorporating stakeholder beliefs and other human factors into the planning process. Effective risk assessment and management requires understanding perceptions and beliefs of involved stakeholders, and how these beliefs give rise to actions that influence risk management decisions. Formal analyses of risk manager and stakeholder cognitions represent an important first step. Techniques for diagramming stakeholder mental models provide one tool for risk managers to better understand stakeholder beliefs and perceptions concerning risk, and to leverage this new understanding in developing risk management strategies. This article reviews three methodologies for assessing and diagramming stakeholder mental models--decision-analysis-based mental modeling, concept mapping, and semantic web analysis--and assesses them with regard to their ability to address risk manager needs. © 2012 Society for Risk Analysis.

Differential impairments underlying decision making in anorexia nervosa and bulimia nervosa: a cognitive modeling analysis.

PubMed

Chan, Trista Wai Sze; Ahn, Woo-Young; Bates, John E; Busemeyer, Jerome R; Guillaume, Sebastien; Redgrave, Graham W; Danner, Unna N; Courtet, Philippe

2014-03-01

This study examined the underlying processes of decision-making impairments in individuals with anorexia nervosa (AN) and bulimia nervosa (BN). We deconstructed their performance on the widely used decision task, the Iowa Gambling Task (IGT) into cognitive, motivational, and response processes using cognitive modeling analysis. We hypothesized that IGT performance would be characterized by impaired memory functions and heightened punishment sensitivity in AN, and by elevated sensitivity to reward as opposed to punishment in BN. We analyzed trial-by-trial data of IGT obtained from 224 individuals: 94 individuals with AN, 63 with BN, and 67 healthy comparison individuals (HC). The prospect valence learning model was used to assess cognitive, motivational, and response processes underlying IGT performance. Individuals with AN showed marginally impaired IGT performance compared to HC. Their performance was characterized by impairments in memory functions. Individuals with BN showed significantly impaired IGT performance compared to HC. They showed greater relative sensitivity to gains as opposed to losses than HC. Memory functions in AN were positively correlated with body mass index. This study identified differential impairments underlying IGT performance in AN and BN. Findings suggest that impaired decision making in AN might involve impaired memory functions. Impaired decision making in BN might involve altered reward and punishment sensitivity. Copyright © 2013 Wiley Periodicals, Inc.

Decision Making Configurations: An Alternative to the Centralization/Decentralization Conceptualization.

ERIC Educational Resources Information Center

Cullen, John B.; Perrewe, Pamela L.

1981-01-01

Used factors identified in the literature as predictors of centralization/decentralization as potential discriminating variables among several decision making configurations in university affiliated professional schools. The model developed from multiple discriminant analysis had reasonable success in classifying correctly only the decentralized…

Demand analysis of flood insurance by using logistic regression model and genetic algorithm

NASA Astrophysics Data System (ADS)

Sidi, P.; Mamat, M. B.; Sukono; Supian, S.; Putra, A. S.

2018-03-01

Citarum River floods in the area of South Bandung Indonesia, often resulting damage to some buildings belonging to the people living in the vicinity. One effort to alleviate the risk of building damage is to have flood insurance. The main obstacle is not all people in the Citarum basin decide to buy flood insurance. In this paper, we intend to analyse the decision to buy flood insurance. It is assumed that there are eight variables that influence the decision of purchasing flood assurance, include: income level, education level, house distance with river, building election with road, flood frequency experience, flood prediction, perception on insurance company, and perception towards government effort in handling flood. The analysis was done by using logistic regression model, and to estimate model parameters, it is done with genetic algorithm. The results of the analysis shows that eight variables analysed significantly influence the demand of flood insurance. These results are expected to be considered for insurance companies, to influence the decision of the community to be willing to buy flood insurance.

Monitoring of human brain functions in risk decision-making task by diffuse optical tomography using voxel-wise general linear model

NASA Astrophysics Data System (ADS)

Lin, Zi-Jing; Li, Lin; Cazzell, Marry; Liu, Hanli

2013-03-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique which measures the hemodynamic changes that reflect the brain activity. Diffuse optical tomography (DOT), a variant of fNIRS with multi-channel NIRS measurements, has demonstrated capability of three dimensional (3D) reconstructions of hemodynamic changes due to the brain activity. Conventional method of DOT image analysis to define the brain activation is based upon the paired t-test between two different states, such as resting-state versus task-state. However, it has limitation because the selection of activation and post-activation period is relatively subjective. General linear model (GLM) based analysis can overcome this limitation. In this study, we combine the 3D DOT image reconstruction with GLM-based analysis (i.e., voxel-wise GLM analysis) to investigate the brain activity that is associated with the risk-decision making process. Risk decision-making is an important cognitive process and thus is an essential topic in the field of neuroscience. The balloon analogue risk task (BART) is a valid experimental model and has been commonly used in behavioral measures to assess human risk taking action and tendency while facing risks. We have utilized the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making. Voxel-wise GLM analysis was performed on 18human participants (10 males and 8females).In this work, we wish to demonstrate the feasibility of using voxel-wise GLM analysis to image and study cognitive functions in response to risk decision making by DOT. Results have shown significant changes in the dorsal lateral prefrontal cortex (DLPFC) during the active choice mode and a different hemodynamic pattern between genders, which are in good agreements with published literatures in functional magnetic resonance imaging (fMRI) and fNIRS studies.

Increasing Effectiveness in Teaching Ethics to Undergraduate Business Students.

ERIC Educational Resources Information Center

Lampe, Marc

1997-01-01

Traditional approaches to teaching business ethics (philosophical analysis, moral quandaries, executive cases) may not be effective in persuading undergraduates of the importance of ethical behavior. Better techniques include values education, ethical decision-making models, analysis of ethical conflicts, and role modeling. (SK)

Deciding to Come Out to Parents: Toward a Model of Sexual Orientation Disclosure Decisions.

PubMed

Grafsky, Erika L

2017-08-16

The purpose of this study was to conduct research to understand nonheterosexual youths' decision to disclose their sexual orientation information to their parents. The sample for this study includes 22 youth between the ages of 14 and 21. Constructivist grounded theory guided the qualitative methodology and data analysis. The findings from this study posit an emerging model of sexual orientation disclosure decisions comprised of four interrelated factors that influence the decision to disclose or not disclose, as well as a description of the mechanism through which disclosure either does or does not occur. Clinical implications and recommendations for further research are provided. © 2017 Family Process Institute.

[Analyzing consumer preference by using the latest semantic model for verbal protocol].

PubMed

Tamari, Yuki; Takemura, Kazuhisa

2012-02-01

This paper examines consumers' preferences for competing brands by using a preference model of verbal protocols. Participants were 150 university students, who reported their opinions and feelings about McDonalds and Mos Burger (competing hamburger restaurants in Japan). Their verbal protocols were analyzed by using the singular value decomposition method, and the latent decision frames were estimated. The verbal protocols having a large value in the decision frames could be interpreted as showing attributes that consumers emphasize. Based on the estimated decision frames, we predicted consumers' preferences using the logistic regression analysis method. The results indicate that the decision frames projected from the verbal protocol data explained consumers' preferences effectively.

Decision analysis for conservation breeding: Maximizing production for reintroduction of whooping cranes

USGS Publications Warehouse

Smith, Des H.V.; Converse, Sarah J.; Gibson, Keith; Moehrenschlager, Axel; Link, William A.; Olsen, Glenn H.; Maguire, Kelly

2011-01-01

Captive breeding is key to management of severely endangered species, but maximizing captive production can be challenging because of poor knowledge of species breeding biology and the complexity of evaluating different management options. In the face of uncertainty and complexity, decision-analytic approaches can be used to identify optimal management options for maximizing captive production. Building decision-analytic models requires iterations of model conception, data analysis, model building and evaluation, identification of remaining uncertainty, further research and monitoring to reduce uncertainty, and integration of new data into the model. We initiated such a process to maximize captive production of the whooping crane (Grus americana), the world's most endangered crane, which is managed through captive breeding and reintroduction. We collected 15 years of captive breeding data from 3 institutions and used Bayesian analysis and model selection to identify predictors of whooping crane hatching success. The strongest predictor, and that with clear management relevance, was incubation environment. The incubation period of whooping crane eggs is split across two environments: crane nests and artificial incubators. Although artificial incubators are useful for allowing breeding pairs to produce multiple clutches, our results indicate that crane incubation is most effective at promoting hatching success. Hatching probability increased the longer an egg spent in a crane nest, from 40% hatching probability for eggs receiving 1 day of crane incubation to 95% for those receiving 30 days (time incubated in each environment varied independently of total incubation period). Because birds will lay fewer eggs when they are incubating longer, a tradeoff exists between the number of clutches produced and egg hatching probability. We developed a decision-analytic model that estimated 16 to be the optimal number of days of crane incubation needed to maximize the number of offspring produced. These results show that using decision-analytic tools to account for uncertainty in captive breeding can improve the rate at which such programs contribute to wildlife reintroductions. 

A Search for the tt¯H (H → bb) Large Hadron Collider with the atlas detector using a matrix element method

NASA Astrophysics Data System (ADS)

Basye, Austin T.

A matrix element method analysis of the Standard Model Higgs boson, produced in association with two top quarks decaying to the lepton-plus-jets channel is presented. Based on 20.3 fb--1 of s=8 TeV data, produced at the Large Hadron Collider and collected by the ATLAS detector, this analysis utilizes multiple advanced techniques to search for ttH signatures with a 125 GeV Higgs boson decaying to two b -quarks. After categorizing selected events based on their jet and b-tag multiplicities, signal rich regions are analyzed using the matrix element method. Resulting variables are then propagated to two parallel multivariate analyses utilizing Neural Networks and Boosted Decision Trees respectively. As no significant excess is found, an observed (expected) limit of 3.4 (2.2) times the Standard Model cross-section is determined at 95% confidence, using the CLs method, for the Neural Network analysis. For the Boosted Decision Tree analysis, an observed (expected) limit of 5.2 (2.7) times the Standard Model cross-section is determined at 95% confidence, using the CLs method. Corresponding unconstrained fits of the Higgs boson signal strength to the observed data result in the measured signal cross-section to Standard Model cross-section prediction of mu = 1.2 +/- 1.3(total) +/- 0.7(stat.) for the Neural Network analysis, and mu = 2.9 +/- 1.4(total) +/- 0.8(stat.) for the Boosted Decision Tree analysis.

Use of multiattribute utility theory for formulary management in a health system.

PubMed

Chung, Seonyoung; Kim, Sooyon; Kim, Jeongmee; Sohn, Kieho

2010-01-15

The application, utility, and flexibility of the multiattribute utility theory (MAUT) when used as a formulary decision methodology in a Korean medical center were evaluated. A drug analysis model using MAUT consisting of 10 steps was designed for two drug classes of dihydropyridine calcium channel blockers (CCBs) and angiotensin II receptor blockers (ARBs). These two drug classes contain the most diverse agents among cardiovascular drugs on Samsung Medical Center's drug formulary. The attributes identified for inclusion in the drug analysis model were effectiveness, safety, patient convenience, and cost, with relative weights of 50%, 30%, 10%, and 10%, respectively. The factors were incorporated into the model to quantify the contribution of each attribute. For each factor, a utility scale of 0-100 was established, and the total utility score for each alternative was calculated. An attempt was made to make the model adaptable to changing health care and regulatory circumstances. The analysis revealed amlodipine besylate to be an alternative agent, with the highest total utility score among the dihydropyridine CCBs, while barnidipine hydrochloride had the lowest score. For ARBs, losartan potassium had the greatest total utility score, while olmesartan medoxomil had the lowest. A drug analysis model based on the MAUT was successfully developed and used in making formulary decisions for dihydropyridine CCBs and ARBs for a Korean health system. The model incorporates sufficient utility and flexibility of a drug's attributes and can be used as an alternative decision-making tool for formulary management in health systems.

Impacts of land-use management on ecosystem services and biodiversity: an agent-based modelling approach

PubMed Central

Heckbert, Scott; Wilson, Jeffrey J.; Vandenbroeck, Andrew J. K.; Cranston, Jerome; Farr, Daniel R.

2016-01-01

The science of ecosystem service (ES) mapping has become increasingly sophisticated over the past 20 years, and examples of successfully integrating ES into management decisions at national and sub-national scales have begun to emerge. However, increasing model sophistication and accuracy—and therefore complexity—may trade-off with ease of use and applicability to real-world decision-making contexts, so it is vital to incorporate the lessons learned from implementation efforts into new model development. Using successful implementation efforts for guidance, we developed an integrated ES modelling system to quantify several ecosystem services: forest timber production and carbon storage, water purification, pollination, and biodiversity. The system is designed to facilitate uptake of ES information into land-use decisions through three principal considerations: (1) using relatively straightforward models that can be readily deployed and interpreted without specialized expertise; (2) using an agent-based modelling framework to enable the incorporation of human decision-making directly within the model; and (3) integration among all ES models to simultaneously demonstrate the effects of a single land-use decision on multiple ES. We present an implementation of the model for a major watershed in Alberta, Canada, and highlight the system’s capabilities to assess a suite of ES under future management decisions, including forestry activities under two alternative timber harvest strategies, and through a scenario modelling analysis exploring different intensities of hypothetical agricultural expansion. By using a modular approach, the modelling system can be readily expanded to evaluate additional ecosystem services or management questions of interest in order to guide land-use decisions to achieve socioeconomic and environmental objectives. PMID:28028479

Water Quality Analysis Simulation Program (WASP)

EPA Pesticide Factsheets

The Water Quality Analysis Simulation Program (WASP) model helps users interpret and predict water quality responses to natural phenomena and manmade pollution for various pollution management decisions.

Motivational antecedents to contraceptive method change following a pregnancy scare: a couple analysis.

PubMed

Miller, W B; Pasta, D J

2001-01-01

In this study we develop and then test a couple model of contraceptive method choice decision-making following a pregnancy scare. The central constructs in our model are satisfaction with one's current method and confidence in the use of it. Downstream in the decision sequence, satisfaction and confidence predict desires and intentions to change methods. Upstream they are predicted by childbearing motivations, contraceptive attitudes, and the residual effects of the couples' previous method decisions. We collected data from 175 mostly unmarried and racially/ethnically diverse couples who were seeking pregnancy tests. We used LISREL and its latent variable capacity to estimate a structural equation model of the couple decision-making sequence leading to a change (or not) in contraceptive method. Results confirm most elements in our model and demonstrate a number of important cross-partner effects. Almost one-half of the sample had positive pregnancy tests and the base model fitted to this subsample indicates less accuracy in partner perception and greater influence of the female partner on method change decision-making. The introduction of some hypothesis-generating exogenous variables to our base couple model, together with some unexpected findings for the contraceptive attitude variables, suggest interesting questions that require further exploration.

Exploring model based engineering for large telescopes: getting started with descriptive models

NASA Astrophysics Data System (ADS)

Karban, R.; Zamparelli, M.; Bauvir, B.; Koehler, B.; Noethe, L.; Balestra, A.

2008-07-01

Large telescopes pose a continuous challenge to systems engineering due to their complexity in terms of requirements, operational modes, long duty lifetime, interfaces and number of components. A multitude of decisions must be taken throughout the life cycle of a new system, and a prime means of coping with complexity and uncertainty is using models as one decision aid. The potential of descriptive models based on the OMG Systems Modeling Language (OMG SysMLTM) is examined in different areas: building a comprehensive model serves as the basis for subsequent activities of soliciting and review for requirements, analysis and design alike. Furthermore a model is an effective communication instrument against misinterpretation pitfalls which are typical of cross disciplinary activities when using natural language only or free-format diagrams. Modeling the essential characteristics of the system, like interfaces, system structure and its behavior, are important system level issues which are addressed. Also shown is how to use a model as an analysis tool to describe the relationships among disturbances, opto-mechanical effects and control decisions and to refine the control use cases. Considerations on the scalability of the model structure and organization, its impact on the development process, the relation to document-centric structures, style and usage guidelines and the required tool chain are presented.

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

Providing guidance for genomics-based cancer treatment decisions: insights from stakeholder engagement for post-prostatectomy radiation therapy.

PubMed

Abe, James; Lobo, Jennifer M; Trifiletti, Daniel M; Showalter, Timothy N

2017-08-24

Despite the emergence of genomics-based risk prediction tools in oncology, there is not yet an established framework for communication of test results to cancer patients to support shared decision-making. We report findings from a stakeholder engagement program that aimed to develop a framework for using Markov models with individualized model inputs, including genomics-based estimates of cancer recurrence probability, to generate personalized decision aids for prostate cancer patients faced with radiation therapy treatment decisions after prostatectomy. We engaged a total of 22 stakeholders, including: prostate cancer patients, urological surgeons, radiation oncologists, genomic testing industry representatives, and biomedical informatics faculty. Slides were at each meeting to provide background information regarding the analytical framework. Participants were invited to provide feedback during the meeting, including revising the overall project aims. Stakeholder meeting content was reviewed and summarized by stakeholder group and by theme. The majority of stakeholder suggestions focused on aspects of decision aid design and formatting. Stakeholders were enthusiastic about the potential value of using decision analysis modeling with personalized model inputs for cancer recurrence risk, as well as competing risks from age and comorbidities, to generate a patient-centered tool to assist decision-making. Stakeholders did not view privacy considerations as a major barrier to the proposed decision aid program. A common theme was that decision aids should be portable across multiple platforms (electronic and paper), should allow for interaction by the user to adjust model inputs iteratively, and available to patients both before and during consult appointments. Emphasis was placed on the challenge of explaining the model's composite result of quality-adjusted life years. A range of stakeholders provided valuable insights regarding the design of a personalized decision aid program, based upon Markov modeling with individualized model inputs, to provide a patient-centered framework to support for genomic-based treatment decisions for cancer patients. The guidance provided by our stakeholders may be broadly applicable to the communication of genomic test results to patients in a patient-centered fashion that supports effective shared decision-making that represents a spectrum of personal factors such as age, medical comorbidities, and individual priorities and values.

A model of human decision making in multiple process monitoring situations

NASA Technical Reports Server (NTRS)

Greenstein, J. S.; Rouse, W. B.

1982-01-01

Human decision making in multiple process monitoring situations is considered. It is proposed that human decision making in many multiple process monitoring situations can be modeled in terms of the human's detection of process related events and his allocation of attention among processes once he feels event have occurred. A mathematical model of human event detection and attention allocation performance in multiple process monitoring situations is developed. An assumption made in developing the model is that, in attempting to detect events, the human generates estimates of the probabilities that events have occurred. An elementary pattern recognition technique, discriminant analysis, is used to model the human's generation of these probability estimates. The performance of the model is compared to that of four subjects in a multiple process monitoring situation requiring allocation of attention among processes.

An analysis of wildfire prevention

NASA Technical Reports Server (NTRS)

Heineke, J. M.; Weissenberger, S.

1974-01-01

A model of the production of wildfire ignitions and damages is developed and used to determine wildland activity-regulation decisions, which minimize total expected cost-plus-loss due to wildfires. In this context, the implications of various policy decisions are considered. The resulting decision rules take a form that makes it possible for existing wildfire management agencies to readily adopt them upon collection of the required data.

Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis.

PubMed

Hostettler, Isabel Charlotte; Muroi, Carl; Richter, Johannes Konstantin; Schmid, Josef; Neidert, Marian Christoph; Seule, Martin; Boss, Oliver; Pangalu, Athina; Germans, Menno Robbert; Keller, Emanuela

2018-01-19

OBJECTIVE The aim of this study was to create prediction models for outcome parameters by decision tree analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH). METHODS The database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7. RESULTS The overall mortality was 18.4%. The accuracy of the decision tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of < 5%. Prediction accuracy for survival on day 1 was 75.2%. The most important differentiating factor was the interleukin-6 (IL-6) level on day 1. Favorable functional outcome, defined as Glasgow Outcome Scale scores of 4 and 5, was observed in 68.6% of patients. Favorable functional outcome at all time points had a prediction accuracy of 71.1% in the training data set, with procalcitonin on day 1 being the most important differentiating factor at all time points. A total of 148 patients (27%) developed VP shunt dependency. The most important differentiating factor was hyperglycemia on admission. CONCLUSIONS The multiple variable analysis capability of decision trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The decision tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.

Pricing, Carbon Emission Reduction, Low-Carbon Promotion and Returning Decision in a Closed-Loop Supply Chain under Vertical and Horizontal Cooperation

PubMed Central

Li, Hui; Wang, Chuanxu; Shang, Meng; Ou, Wei

2017-01-01

In this paper, we examine the influences of vertical and horizontal cooperation models on the optimal decisions and performance of a low-carbon closed-loop supply chain (CLSC) with a manufacturer and two retailers, and study optimal operation in the competitive pricing, competitive the low-carbon promotion, the carbon emission reduction, the used-products collection and the profits. We consider the completely decentralized model, M-R vertical cooperation model, R-R horizontal cooperation model, M-R-R vertical and horizontal cooperation model and completely centralized model, and also identify the optimal decision results and profits. It can be observed from a systematic comparison and numerical analysis that the completely centralized model is best in all optimal decision results among all models. In semi-cooperation, the M-R vertical cooperation model is positive, the R-R horizontal cooperation model is passive, and the positivity of the M-R-R vertical and horizontal cooperation model decreases with competitive intensity increasing in the used-products returning, carbon emissions reduction level, low-carbon promotion effort and the profits of the manufacturer and the entire supply chain. PMID:29104268

Pricing, Carbon Emission Reduction, Low-Carbon Promotion and Returning Decision in a Closed-Loop Supply Chain under Vertical and Horizontal Cooperation.

PubMed

Li, Hui; Wang, Chuanxu; Shang, Meng; Ou, Wei

2017-11-01

In this paper, we examine the influences of vertical and horizontal cooperation models on the optimal decisions and performance of a low-carbon closed-loop supply chain (CLSC) with a manufacturer and two retailers, and study optimal operation in the competitive pricing, competitive the low-carbon promotion, the carbon emission reduction, the used-products collection and the profits. We consider the completely decentralized model, M-R vertical cooperation model, R-R horizontal cooperation model, M-R-R vertical and horizontal cooperation model and completely centralized model, and also identify the optimal decision results and profits. It can be observed from a systematic comparison and numerical analysis that the completely centralized model is best in all optimal decision results among all models. In semi-cooperation, the M-R vertical cooperation model is positive, the R-R horizontal cooperation model is passive, and the positivity of the M-R-R vertical and horizontal cooperation model decreases with competitive intensity increasing in the used-products returning, carbon emissions reduction level, low-carbon promotion effort and the profits of the manufacturer and the entire supply chain.

Information Presentation in Decision and Risk Analysis: Answered, Partly Answered, and Unanswered Questions.

PubMed

Keller, L Robin; Wang, Yitong

2017-06-01

For the last 30 years, researchers in risk analysis, decision analysis, and economics have consistently proven that decisionmakers employ different processes for evaluating and combining anticipated and actual losses, gains, delays, and surprises. Although rational models generally prescribe a consistent response, people's heuristic processes will sometimes lead them to be inconsistent in the way they respond to information presented in theoretically equivalent ways. We point out several promising future research directions by listing and detailing a series of answered, partly answered, and unanswered questions. © 2016 Society for Risk Analysis.

Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk.

PubMed

Trepel, Christopher; Fox, Craig R; Poldrack, Russell A

2005-04-01

Most decisions must be made without advance knowledge of their consequences. Economists and psychologists have devoted much attention to modeling decisions made under conditions of risk in which options can be characterized by a known probability distribution over possible outcomes. The descriptive shortcomings of classical economic models motivated the development of prospect theory (D. Kahneman, A. Tversky, Prospect theory: An analysis of decision under risk. Econometrica, 4 (1979) 263-291; A. Tversky, D. Kahneman, Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5 (4) (1992) 297-323) the most successful behavioral model of decision under risk. In the prospect theory, subjective value is modeled by a value function that is concave for gains, convex for losses, and steeper for losses than for gains; the impact of probabilities are characterized by a weighting function that overweights low probabilities and underweights moderate to high probabilities. We outline the possible neural bases of the components of prospect theory, surveying evidence from human imaging, lesion, and neuropharmacology studies as well as animal neurophysiology studies. These results provide preliminary suggestions concerning the neural bases of prospect theory that include a broad set of brain regions and neuromodulatory systems. These data suggest that focused studies of decision making in the context of quantitative models may provide substantial leverage towards a fuller understanding of the cognitive neuroscience of decision making.

Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making.

PubMed

Owen, Rhiannon K; Cooper, Nicola J; Quinn, Terence J; Lees, Rosalind; Sutton, Alex J

2018-07-01

Network meta-analyses (NMA) have extensively been used to compare the effectiveness of multiple interventions for health care policy and decision-making. However, methods for evaluating the performance of multiple diagnostic tests are less established. In a decision-making context, we are often interested in comparing and ranking the performance of multiple diagnostic tests, at varying levels of test thresholds, in one simultaneous analysis. Motivated by an example of cognitive impairment diagnosis following stroke, we synthesized data from 13 studies assessing the efficiency of two diagnostic tests: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), at two test thresholds: MMSE <25/30 and <27/30, and MoCA <22/30 and <26/30. Using Markov chain Monte Carlo (MCMC) methods, we fitted a bivariate network meta-analysis model incorporating constraints on increasing test threshold, and accounting for the correlations between multiple test accuracy measures from the same study. We developed and successfully fitted a model comparing multiple tests/threshold combinations while imposing threshold constraints. Using this model, we found that MoCA at threshold <26/30 appeared to have the best true positive rate, whereas MMSE at threshold <25/30 appeared to have the best true negative rate. The combined analysis of multiple tests at multiple thresholds allowed for more rigorous comparisons between competing diagnostics tests for decision making. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Nuclear emergency management procedures in Europe

NASA Astrophysics Data System (ADS)

Carter, Emma

The Chernobyl accident brought to the fore the need for decision-making in nuclear emergency management to be transparent and consistent across Europe. A range of systems to support decision-making in future emergencies have since been developed, but, by and large, with little consultation with potential decision makers and limited understanding of the emergency management procedures across Europe and how they differ. In nuclear emergency management, coordination, communication and information sharing are of paramount importance. There are many key players with their own technical expertise, and several key activities occur in parallel, across different locations. Business process modelling can facilitate understanding through the representation of processes, aid transparency and structure the analysis, comparison and improvement of processes. This work has been conducted as part of a European Fifth Framework Programme project EVATECH, whose aim was to improve decision support methods, models and processes taking into account stakeholder expectations and concerns. It has involved the application of process modelling to document and compare the emergency management processes in four European countries. It has also involved a multidisciplinary approach taking a socio-technical perspective. The use of process modelling did indeed facilitate understanding and provided a common platform, which was not previously available, to consider emergency management processes. This thesis illustrates the structured analysis approach that process modelling enables. Firstly, through an individual analysis for the United Kingdom (UK) model that illustrated the potential benefits for a country. These are for training purposes, to build reflexive shared mental models, to aid coordination and for process improvement. Secondly, through a comparison of the processes in Belgium, Germany, Slovak Republic and the UK. In this comparison of the four processes we observed that the four process models are substantially different in their organisational structure and identified differences in the management of advice, where decisions are made and the communication network style. Another key aspect of this work is that through the structured analysis conducted we were able to develop a framework for the evaluation of DSS from the perspective of process. This work concludes reflecting on the challenges, which the European off-site nuclear emergency community face and suggest direction for future work, with particular reference to a recent conference on the capabilities and challenges of offsite nuclear emergency management, the Salzburg Symposium 2003.

Task Inhibition and Response Inhibition in Older vs. Younger Adults: A Diffusion Model Analysis

PubMed Central

Schuch, Stefanie

2016-01-01

Differences in inhibitory ability between older (64–79 years, N = 24) and younger adults (18–26 years, N = 24) were investigated using a diffusion model analysis. Participants performed a task-switching paradigm that allows assessing n−2 task repetition costs, reflecting inhibitory control on the level of tasks, as well as n−1 response-repetition costs, reflecting inhibitory control on the level of responses. N−2 task repetition costs were of similar size in both age groups. Diffusion model analysis revealed that for both younger and older adults, drift rate parameters were smaller in the inhibition condition relative to the control condition, consistent with the idea that persisting task inhibition slows down response selection. Moreover, there was preliminary evidence for task inhibition effects in threshold separation and non-decision time in the older, but not the younger adults, suggesting that older adults might apply different strategies when dealing with persisting task inhibition. N−1 response-repetition costs in mean RT were larger in older than younger adults, but in mean error rates tended to be larger in younger than older adults. Diffusion-model analysis revealed longer non-decision times in response repetitions than response switches in both age groups, consistent with the idea that motor processes take longer in response repetitions than response switches due to persisting response inhibition of a previously executed response. The data also revealed age-related differences in overall performance: Older adults responded more slowly and more accurately than young adults, which was reflected by a higher threshold separation parameter in diffusion model analysis. Moreover, older adults showed larger non-decision times and higher variability in non-decision time than young adults, possibly reflecting slower and more variable motor processes. In contrast, overall drift rate did not differ between older and younger adults. Taken together, diffusion model analysis revealed differences in overall performance between the age groups, as well as preliminary evidence for age differences in dealing with task inhibition, but no evidence for an inhibitory deficit in older age. PMID:27895599

Naturalistic Decision Making for Power System Operators

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

Greitzer, Frank L.; Podmore, Robin; Robinson, Marck

2010-02-01

Motivation – Investigations of large-scale outages in the North American interconnected electric system often attribute the causes to three T’s: Trees, Training and Tools. To document and understand the mental processes used by expert operators when making critical decisions, a naturalistic decision making (NDM) model was developed. Transcripts of conversations were analyzed to reveal and assess NDM-based performance criteria. Findings/Design – An item analysis indicated that the operators’ Situation Awareness Levels, mental models, and mental simulations can be mapped at different points in the training scenario. This may identify improved training methods or analytical/ visualization tools. Originality/Value – This studymore » applies for the first time, the concepts of Recognition Primed Decision Making, Situation Awareness Levels and Cognitive Task Analysis to training of electric power system operators. Take away message – The NDM approach provides a viable framework for systematic training management to accelerate learning in simulator-based training scenarios for power system operators and teams.« less

Directional Slack-Based Measure for the Inverse Data Envelopment Analysis

PubMed Central

Abu Bakar, Mohd Rizam; Lee, Lai Soon; Jaafar, Azmi B.; Heydar, Maryam

2014-01-01

A novel technique has been introduced in this research which lends its basis to the Directional Slack-Based Measure for the inverse Data Envelopment Analysis. In practice, the current research endeavors to elucidate the inverse directional slack-based measure model within a new production possibility set. On one occasion, there is a modification imposed on the output (input) quantities of an efficient decision making unit. In detail, the efficient decision making unit in this method was omitted from the present production possibility set but substituted by the considered efficient decision making unit while its input and output quantities were subsequently modified. The efficiency score of the entire DMUs will be retained in this approach. Also, there would be an improvement in the efficiency score. The proposed approach was investigated in this study with reference to a resource allocation problem. It is possible to simultaneously consider any upsurges (declines) of certain outputs associated with the efficient decision making unit. The significance of the represented model is accentuated by presenting numerical examples. PMID:24883350

The application of the heuristic-systematic processing model to treatment decision making about prostate cancer.

PubMed

Steginga, Suzanne K; Occhipinti, Stefano

2004-01-01

The study investigated the utility of the Heuristic-Systematic Processing Model as a framework for the investigation of patient decision making. A total of 111 men recently diagnosed with localized prostate cancer were assessed using Verbal Protocol Analysis and self-report measures. Study variables included men's use of nonsystematic and systematic information processing, desire for involvement in decision making, and the individual differences of health locus of control, tolerance of ambiguity, and decision-related uncertainty. Most men (68%) preferred that decision making be shared equally between them and their doctor. Men's use of the expert opinion heuristic was related to men's verbal reports of decisional uncertainty and having a positive orientation to their doctor and medical care; a desire for greater involvement in decision making was predicted by a high internal locus of health control. Trends were observed for systematic information processing to increase when the heuristic strategy used was negatively affect laden and when men were uncertain about the probabilities for cure and side effects. There was a trend for decreased systematic processing when the expert opinion heuristic was used. Findings were consistent with the Heuristic-Systematic Processing Model and suggest that this model has utility for future research in applied decision making about health.

Making the right decisions about new technologies: a perspective on criteria and preferences in hospitals.

PubMed

Gurtner, Sebastian

2014-01-01

Decision makers in hospitals are regularly faced with choices about the adoption of new technologies. Wrong decisions lead to a waste of resources and can have serious effects on the patients' and hospital's well-being. The goal of this research was to contribute to the understanding of decision making in hospitals. This study produced insights regarding relevant decision criteria and explored their specific relevance. An initial empirical survey was used to collect the relevant criteria for technological decision making in hospitals. In total, 220 experts in the field of health technology assessment from 34 countries participated in the survey. As a second step, the abovementioned criteria were used to form the basis of an analytic hierarchy process model. A group of 115 physicians, medical technical assistants, and other staff, all of whom worked in the field of radiooncology, prioritized the criteria. An analysis of variance was performed to explore differences among groups in terms of institutional and personal categorization variables. The first part of the research revealed seven key criteria for technological decision making in hospitals. The analytic hierarchy process model revealed that organizational impact was the most important criterion, followed by budget impact. The analysis of variance indicated that there were differences in the perceptions of the importance of the identified criteria. This exploration of the criteria for technological decision making in hospitals will help decision makers consider all of the relevant aspects, leading to more structured and rational decisions. For the optimal resource allocation, all of the relevant stakeholder perspectives and local issues must be considered appropriately.

Accelerating policy decisions to adopt haemophilus influenzae type B vaccine: a global, multivariable analysis.

PubMed

Shearer, Jessica C; Stack, Meghan L; Richmond, Marcie R; Bear, Allyson P; Hajjeh, Rana A; Bishai, David M

2010-03-16

Adoption of new and underutilized vaccines by national immunization programs is an essential step towards reducing child mortality. Policy decisions to adopt new vaccines in high mortality countries often lag behind decisions in high-income countries. Using the case of Haemophilus influenzae type b (Hib) vaccine, this paper endeavors to explain these delays through the analysis of country-level economic, epidemiological, programmatic and policy-related factors, as well as the role of the Global Alliance for Vaccines and Immunisation (GAVI Alliance). Data for 147 countries from 1990 to 2007 were analyzed in accelerated failure time models to identify factors that are associated with the time to decision to adopt Hib vaccine. In multivariable models that control for Gross National Income, region, and burden of Hib disease, the receipt of GAVI support speeded the time to decision by a factor of 0.37 (95% CI 0.18-0.76), or 63%. The presence of two or more neighboring country adopters accelerated decisions to adopt by a factor of 0.50 (95% CI 0.33-0.75). For each 1% increase in vaccine price, decisions to adopt are delayed by a factor of 1.02 (95% CI 1.00-1.04). Global recommendations and local studies were not associated with time to decision. This study substantiates previous findings related to vaccine price and presents new evidence to suggest that GAVI eligibility is associated with accelerated decisions to adopt Hib vaccine. The influence of neighboring country decisions was also highly significant, suggesting that approaches to support the adoption of new vaccines should consider supply- and demand-side factors.

Multicriteria decision model for retrofitting existing buildings

NASA Astrophysics Data System (ADS)

Bostenaru Dan, B.

2003-04-01

In this paper a model to decide which buildings from an urban area should be retrofitted is presented. The model has been cast into existing ones by choosing the decision rule, criterion weighting and decision support system types most suitable for the spatial problem of reducing earthquake risk in urban areas, considering existing spatial multiatributive and multiobjective decision methods and especially collaborative issues. Due to the participative character of the group decision problem "retrofitting existing buildings" the decision making model is based on interactivity. Buildings have been modeled following the criteria of spatial decision support systems. This includes identifying the corresponding spatial elements of buildings according to the information needs of actors from different sphaeres like architects, construction engineers and economists. The decision model aims to facilitate collaboration between this actors. The way of setting priorities interactivelly will be shown, by detailing the two phases: judgemental and computational, in this case site analysis, collection and evaluation of the unmodified data and converting survey data to information with computational methods using additional expert support. Buildings have been divided into spatial elements which are characteristic for the survey, present typical damages in case of an earthquake and are decisive for a better seismic behaviour in case of retrofitting. The paper describes the architectural and engineering characteristics as well as the structural damage for constuctions of different building ages on the example of building types in Bucharest, Romania in compressible and interdependent charts, based on field observation, reports from the 1977 earthquake and detailed studies made by the author together with a local engineer for the EERI Web Housing Encyclopedia. On this base criteria for setting priorities flow into the expert information contained in the system.

Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: An example from a vertigo phase III study with longitudinal count data as primary endpoint

PubMed Central

2012-01-01

Background A statistical analysis plan (SAP) is a critical link between how a clinical trial is conducted and the clinical study report. To secure objective study results, regulatory bodies expect that the SAP will meet requirements in pre-specifying inferential analyses and other important statistical techniques. To write a good SAP for model-based sensitivity and ancillary analyses involves non-trivial decisions on and justification of many aspects of the chosen setting. In particular, trials with longitudinal count data as primary endpoints pose challenges for model choice and model validation. In the random effects setting, frequentist strategies for model assessment and model diagnosis are complex and not easily implemented and have several limitations. Therefore, it is of interest to explore Bayesian alternatives which provide the needed decision support to finalize a SAP. Methods We focus on generalized linear mixed models (GLMMs) for the analysis of longitudinal count data. A series of distributions with over- and under-dispersion is considered. Additionally, the structure of the variance components is modified. We perform a simulation study to investigate the discriminatory power of Bayesian tools for model criticism in different scenarios derived from the model setting. We apply the findings to the data from an open clinical trial on vertigo attacks. These data are seen as pilot data for an ongoing phase III trial. To fit GLMMs we use a novel Bayesian computational approach based on integrated nested Laplace approximations (INLAs). The INLA methodology enables the direct computation of leave-one-out predictive distributions. These distributions are crucial for Bayesian model assessment. We evaluate competing GLMMs for longitudinal count data according to the deviance information criterion (DIC) or probability integral transform (PIT), and by using proper scoring rules (e.g. the logarithmic score). Results The instruments under study provide excellent tools for preparing decisions within the SAP in a transparent way when structuring the primary analysis, sensitivity or ancillary analyses, and specific analyses for secondary endpoints. The mean logarithmic score and DIC discriminate well between different model scenarios. It becomes obvious that the naive choice of a conventional random effects Poisson model is often inappropriate for real-life count data. The findings are used to specify an appropriate mixed model employed in the sensitivity analyses of an ongoing phase III trial. Conclusions The proposed Bayesian methods are not only appealing for inference but notably provide a sophisticated insight into different aspects of model performance, such as forecast verification or calibration checks, and can be applied within the model selection process. The mean of the logarithmic score is a robust tool for model ranking and is not sensitive to sample size. Therefore, these Bayesian model selection techniques offer helpful decision support for shaping sensitivity and ancillary analyses in a statistical analysis plan of a clinical trial with longitudinal count data as the primary endpoint. PMID:22962944

Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: an example from a vertigo phase III study with longitudinal count data as primary endpoint.

PubMed

Adrion, Christine; Mansmann, Ulrich

2012-09-10

A statistical analysis plan (SAP) is a critical link between how a clinical trial is conducted and the clinical study report. To secure objective study results, regulatory bodies expect that the SAP will meet requirements in pre-specifying inferential analyses and other important statistical techniques. To write a good SAP for model-based sensitivity and ancillary analyses involves non-trivial decisions on and justification of many aspects of the chosen setting. In particular, trials with longitudinal count data as primary endpoints pose challenges for model choice and model validation. In the random effects setting, frequentist strategies for model assessment and model diagnosis are complex and not easily implemented and have several limitations. Therefore, it is of interest to explore Bayesian alternatives which provide the needed decision support to finalize a SAP. We focus on generalized linear mixed models (GLMMs) for the analysis of longitudinal count data. A series of distributions with over- and under-dispersion is considered. Additionally, the structure of the variance components is modified. We perform a simulation study to investigate the discriminatory power of Bayesian tools for model criticism in different scenarios derived from the model setting. We apply the findings to the data from an open clinical trial on vertigo attacks. These data are seen as pilot data for an ongoing phase III trial. To fit GLMMs we use a novel Bayesian computational approach based on integrated nested Laplace approximations (INLAs). The INLA methodology enables the direct computation of leave-one-out predictive distributions. These distributions are crucial for Bayesian model assessment. We evaluate competing GLMMs for longitudinal count data according to the deviance information criterion (DIC) or probability integral transform (PIT), and by using proper scoring rules (e.g. the logarithmic score). The instruments under study provide excellent tools for preparing decisions within the SAP in a transparent way when structuring the primary analysis, sensitivity or ancillary analyses, and specific analyses for secondary endpoints. The mean logarithmic score and DIC discriminate well between different model scenarios. It becomes obvious that the naive choice of a conventional random effects Poisson model is often inappropriate for real-life count data. The findings are used to specify an appropriate mixed model employed in the sensitivity analyses of an ongoing phase III trial. The proposed Bayesian methods are not only appealing for inference but notably provide a sophisticated insight into different aspects of model performance, such as forecast verification or calibration checks, and can be applied within the model selection process. The mean of the logarithmic score is a robust tool for model ranking and is not sensitive to sample size. Therefore, these Bayesian model selection techniques offer helpful decision support for shaping sensitivity and ancillary analyses in a statistical analysis plan of a clinical trial with longitudinal count data as the primary endpoint.

A Chaotic Ordered Hierarchies Consistency Analysis Performance Evaluation Model

NASA Astrophysics Data System (ADS)

Yeh, Wei-Chang

2013-02-01

The Hierarchies Consistency Analysis (HCA) is proposed by Guh in-cooperated along with some case study on a Resort to reinforce the weakness of Analytical Hierarchy Process (AHP). Although the results obtained enabled aid for the Decision Maker to make more reasonable and rational verdicts, the HCA itself is flawed. In this paper, our objective is to indicate the problems of HCA, and then propose a revised method called chaotic ordered HCA (COH in short) which can avoid problems. Since the COH is based upon Guh's method, the Decision Maker establishes decisions in a way similar to that of the original method.

Application of a stochastic snowmelt model for probabilistic decisionmaking

NASA Technical Reports Server (NTRS)

Mccuen, R. H.

1983-01-01

A stochastic form of the snowmelt runoff model that can be used for probabilistic decision-making was developed. The use of probabilistic streamflow predictions instead of single valued deterministic predictions leads to greater accuracy in decisions. While the accuracy of the output function is important in decisionmaking, it is also important to understand the relative importance of the coefficients. Therefore, a sensitivity analysis was made for each of the coefficients.

Class Evolution Tree: A Graphical Tool to Support Decisions on the Number of Classes in Exploratory Categorical Latent Variable Modeling for Rehabilitation Research

ERIC Educational Resources Information Center

Kriston, Levente; Melchior, Hanne; Hergert, Anika; Bergelt, Corinna; Watzke, Birgit; Schulz, Holger; von Wolff, Alessa

2011-01-01

The aim of our study was to develop a graphical tool that can be used in addition to standard statistical criteria to support decisions on the number of classes in explorative categorical latent variable modeling for rehabilitation research. Data from two rehabilitation research projects were used. In the first study, a latent profile analysis was…

Attitudes towards poverty, organizations, ethics and morals: Israeli social workers' shared decision making.

PubMed

Levin, Lia; Schwartz-Tayri, Talia

2017-06-01

Partnerships between service users and social workers are complex in nature and can be driven by both personal and contextual circumstances. This study sought to explore the relationship between social workers' involvement in shared decision making with service users, their attitudes towards service users in poverty, moral standards and health and social care organizations' policies towards shared decision making. Based on the responses of 225 licensed social workers from health and social care agencies in the public, private and third sectors in Israel, path analysis was used to test a hypothesized model. Structural attributions for poverty contributed to attitudes towards people who live in poverty, which led to shared decision making. Also, organizational support in shared decision making, and professional moral identity, contributed to ethical behaviour which led to shared decision making. The results of this analysis revealed that shared decision making may be a scion of branched roots planted in the relationship between ethics, organizations and Stigma. © 2016 The Authors. Health Expectations Published by John Wiley & Sons Ltd.

Faults Discovery By Using Mined Data

NASA Technical Reports Server (NTRS)

Lee, Charles

2005-01-01

Fault discovery in the complex systems consist of model based reasoning, fault tree analysis, rule based inference methods, and other approaches. Model based reasoning builds models for the systems either by mathematic formulations or by experiment model. Fault Tree Analysis shows the possible causes of a system malfunction by enumerating the suspect components and their respective failure modes that may have induced the problem. The rule based inference build the model based on the expert knowledge. Those models and methods have one thing in common; they have presumed some prior-conditions. Complex systems often use fault trees to analyze the faults. Fault diagnosis, when error occurs, is performed by engineers and analysts performing extensive examination of all data gathered during the mission. International Space Station (ISS) control center operates on the data feedback from the system and decisions are made based on threshold values by using fault trees. Since those decision-making tasks are safety critical and must be done promptly, the engineers who manually analyze the data are facing time challenge. To automate this process, this paper present an approach that uses decision trees to discover fault from data in real-time and capture the contents of fault trees as the initial state of the trees.

Beyond utilitarianism: a method for analyzing competing ethical principles in a decision analysis of liver transplantation.

PubMed

Volk, Michael L; Lok, Anna S F; Ubel, Peter A; Vijan, Sandeep

2008-01-01

The utilitarian foundation of decision analysis limits its usefulness for many social policy decisions. In this study, the authors examine a method to incorporate competing ethical principles in a decision analysis of liver transplantation for a patient with acute liver failure (ALF). A Markov model was constructed to compare the benefit of transplantation for a patient with ALF versus the harm caused to other patients on the waiting list and to determine the lowest acceptable 5-y posttransplant survival for the ALF patient. The weighting of the ALF patient and other patients was then adjusted using a multiattribute variable incorporating utilitarianism, urgency, and other principles such as fair chances. In the base-case analysis, the strategy of transplanting the ALF patient resulted in a 0.8% increase in the risk of death and a utility loss of 7.8 quality-adjusted days of life for each of the other patients on the waiting list. These harms cumulatively outweighed the benefit of transplantation for an ALF patient having a posttransplant survival of less than 48% at 5 y. However, the threshold for an acceptable posttransplant survival for the ALF patient ranged from 25% to 56% at 5 y, depending on the ethical principles involved. The results of the decision analysis vary depending on the ethical perspective. This study demonstrates how competing ethical principles can be numerically incorporated in a decision analysis.

Devaluation and sequential decisions: linking goal-directed and model-based behavior

PubMed Central

Friedel, Eva; Koch, Stefan P.; Wendt, Jean; Heinz, Andreas; Deserno, Lorenz; Schlagenhauf, Florian

2014-01-01

In experimental psychology different experiments have been developed to assess goal–directed as compared to habitual control over instrumental decisions. Similar to animal studies selective devaluation procedures have been used. More recently sequential decision-making tasks have been designed to assess the degree of goal-directed vs. habitual choice behavior in terms of an influential computational theory of model-based compared to model-free behavioral control. As recently suggested, different measurements are thought to reflect the same construct. Yet, there has been no attempt to directly assess the construct validity of these different measurements. In the present study, we used a devaluation paradigm and a sequential decision-making task to address this question of construct validity in a sample of 18 healthy male human participants. Correlational analysis revealed a positive association between model-based choices during sequential decisions and goal-directed behavior after devaluation suggesting a single framework underlying both operationalizations and speaking in favor of construct validity of both measurement approaches. Up to now, this has been merely assumed but never been directly tested in humans. PMID:25136310

Groundwater Remediation using Bayesian Information-Gap Decision Theory

NASA Astrophysics Data System (ADS)

O'Malley, D.; Vesselinov, V. V.

2016-12-01

Probabilistic analyses of groundwater remediation scenarios frequently fail because the probability of an adverse, unanticipated event occurring is often high. In general, models of flow and transport in contaminated aquifers are always simpler than reality. Further, when a probabilistic analysis is performed, probability distributions are usually chosen more for convenience than correctness. The Bayesian Information-Gap Decision Theory (BIGDT) was designed to mitigate the shortcomings of the models and probabilistic decision analyses by leveraging a non-probabilistic decision theory - information-gap decision theory. BIGDT considers possible models that have not been explicitly enumerated and does not require us to commit to a particular probability distribution for model and remediation-design parameters. Both the set of possible models and the set of possible probability distributions grow as the degree of uncertainty increases. The fundamental question that BIGDT asks is "How large can these sets be before a particular decision results in an undesirable outcome?". The decision that allows these sets to be the largest is considered to be the best option. In this way, BIGDT enables robust decision-support for groundwater remediation problems. Here we apply BIGDT to in a representative groundwater remediation scenario where different options for hydraulic containment and pump & treat are being considered. BIGDT requires many model runs and for complex models high-performance computing resources are needed. These analyses are carried out on synthetic problems, but are applicable to real-world problems such as LANL site contaminations. BIGDT is implemented in Julia (a high-level, high-performance dynamic programming language for technical computing) and is part of the MADS framework (http://mads.lanl.gov/ and https://github.com/madsjulia/Mads.jl).

Next generation terminology infrastructure to support interprofessional care planning.

PubMed

Collins, Sarah; Klinkenberg-Ramirez, Stephanie; Tsivkin, Kira; Mar, Perry L; Iskhakova, Dina; Nandigam, Hari; Samal, Lipika; Rocha, Roberto A

2017-11-01

Develop a prototype of an interprofessional terminology and information model infrastructure that can enable care planning applications to facilitate patient-centered care, learn care plan linkages and associations, provide decision support, and enable automated, prospective analytics. The study steps included a 3 step approach: (1) Process model and clinical scenario development, and (2) Requirements analysis, and (3) Development and validation of information and terminology models. Components of the terminology model include: Health Concerns, Goals, Decisions, Interventions, Assessments, and Evaluations. A terminology infrastructure should: (A) Include discrete care plan concepts; (B) Include sets of profession-specific concerns, decisions, and interventions; (C) Communicate rationales, anticipatory guidance, and guidelines that inform decisions among the care team; (D) Define semantic linkages across clinical events and professions; (E) Define sets of shared patient goals and sub-goals, including patient stated goals; (F) Capture evaluation toward achievement of goals. These requirements were mapped to AHRQ Care Coordination Measures Framework. This study used a constrained set of clinician-validated clinical scenarios. Terminology models for goals and decisions are unavailable in SNOMED CT, limiting the ability to evaluate these aspects of the proposed infrastructure. Defining and linking subsets of care planning concepts appears to be feasible, but also essential to model interprofessional care planning for common co-occurring conditions and chronic diseases. We recommend the creation of goal dynamics and decision concepts in SNOMED CT to further enable the necessary models. Systems with flexible terminology management infrastructure may enable intelligent decision support to identify conflicting and aligned concerns, goals, decisions, and interventions in shared care plans, ultimately decreasing documentation effort and cognitive burden for clinicians and patients. Copyright © 2017 Elsevier Inc. All rights reserved.

Modelling the risk-benefit impact of H1N1 influenza vaccines.

PubMed

Phillips, Lawrence D; Fasolo, Barbara; Zafiropoulous, Nikolaos; Eichler, Hans-Georg; Ehmann, Falk; Jekerle, Veronika; Kramarz, Piotr; Nicoll, Angus; Lönngren, Thomas

2013-08-01

Shortly after the H1N1 influenza virus reached pandemic status in June 2009, the benefit-risk project team at the European Medicines Agency recognized this presented a research opportunity for testing the usefulness of a decision analysis model in deliberations about approving vaccines soon based on limited data or waiting for more data. Undertaken purely as a research exercise, the model was not connected to the ongoing assessment by the European Medicines Agency, which approved the H1N1 vaccines on 25 September 2009. A decision tree model constructed initially on 1 September 2009, and slightly revised subsequently as new data were obtained, represented an end-of-September or end-of-October approval of vaccines. The model showed combinations of uncertain events, the severity of the disease and the vaccines' efficacy and safety, leading to estimates of numbers of deaths and serious disabilities. The group based their probability assessments on available information and background knowledge about vaccines and similar pandemics in the past. Weighting the numbers by their joint probabilities for all paths through the decision tree gave a weighted average for a September decision of 216 500 deaths and serious disabilities, and for a decision delayed to October of 291 547, showing that an early decision was preferable. The process of constructing the model facilitated communications among the group's members and led to new insights for several participants, while its robustness built confidence in the decision. These findings suggest that models might be helpful to regulators, as they form their preferences during the process of deliberation and debate, and more generally, for public health issues when decision makers face considerable uncertainty.

E-DECIDER Decision Support Gateway For Earthquake Disaster Response

NASA Astrophysics Data System (ADS)

Glasscoe, M. T.; Stough, T. M.; Parker, J. W.; Burl, M. C.; Donnellan, A.; Blom, R. G.; Pierce, M. E.; Wang, J.; Ma, Y.; Rundle, J. B.; Yoder, M. R.

2013-12-01

Earthquake Data Enhanced Cyber-Infrastructure for Disaster Evaluation and Response (E-DECIDER) is a NASA-funded project developing capabilities for decision-making utilizing remote sensing data and modeling software in order to provide decision support for earthquake disaster management and response. E-DECIDER incorporates earthquake forecasting methodology and geophysical modeling tools developed through NASA's QuakeSim project in order to produce standards-compliant map data products to aid in decision-making following an earthquake. Remote sensing and geodetic data, in conjunction with modeling and forecasting tools, help provide both long-term planning information for disaster management decision makers as well as short-term information following earthquake events (i.e. identifying areas where the greatest deformation and damage has occurred and emergency services may need to be focused). E-DECIDER utilizes a service-based GIS model for its cyber-infrastructure in order to produce standards-compliant products for different user types with multiple service protocols (such as KML, WMS, WFS, and WCS). The goal is to make complex GIS processing and domain-specific analysis tools more accessible to general users through software services as well as provide system sustainability through infrastructure services. The system comprises several components, which include: a GeoServer for thematic mapping and data distribution, a geospatial database for storage and spatial analysis, web service APIs, including simple-to-use REST APIs for complex GIS functionalities, and geoprocessing tools including python scripts to produce standards-compliant data products. These are then served to the E-DECIDER decision support gateway (http://e-decider.org), the E-DECIDER mobile interface, and to the Department of Homeland Security decision support middleware UICDS (Unified Incident Command and Decision Support). The E-DECIDER decision support gateway features a web interface that delivers map data products including deformation modeling results (slope change and strain magnitude) and aftershock forecasts, with remote sensing change detection results under development. These products are event triggered (from the USGS earthquake feed) and will be posted to event feeds on the E-DECIDER webpage and accessible via the mobile interface and UICDS. E-DECIDER also features a KML service that provides infrastructure information from the FEMA HAZUS database through UICDS and the mobile interface. The back-end GIS service architecture and front-end gateway components form a decision support system that is designed for ease-of-use and extensibility for end-users.

Estimation of Survival Probabilities for Use in Cost-effectiveness Analyses: A Comparison of a Multi-state Modeling Survival Analysis Approach with Partitioned Survival and Markov Decision-Analytic Modeling

PubMed Central

Williams, Claire; Lewsey, James D.; Mackay, Daniel F.; Briggs, Andrew H.

2016-01-01

Modeling of clinical-effectiveness in a cost-effectiveness analysis typically involves some form of partitioned survival or Markov decision-analytic modeling. The health states progression-free, progression and death and the transitions between them are frequently of interest. With partitioned survival, progression is not modeled directly as a state; instead, time in that state is derived from the difference in area between the overall survival and the progression-free survival curves. With Markov decision-analytic modeling, a priori assumptions are often made with regard to the transitions rather than using the individual patient data directly to model them. This article compares a multi-state modeling survival regression approach to these two common methods. As a case study, we use a trial comparing rituximab in combination with fludarabine and cyclophosphamide v. fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia. We calculated mean Life Years and QALYs that involved extrapolation of survival outcomes in the trial. We adapted an existing multi-state modeling approach to incorporate parametric distributions for transition hazards, to allow extrapolation. The comparison showed that, due to the different assumptions used in the different approaches, a discrepancy in results was evident. The partitioned survival and Markov decision-analytic modeling deemed the treatment cost-effective with ICERs of just over £16,000 and £13,000, respectively. However, the results with the multi-state modeling were less conclusive, with an ICER of just over £29,000. This work has illustrated that it is imperative to check whether assumptions are realistic, as different model choices can influence clinical and cost-effectiveness results. PMID:27698003

Estimation of Survival Probabilities for Use in Cost-effectiveness Analyses: A Comparison of a Multi-state Modeling Survival Analysis Approach with Partitioned Survival and Markov Decision-Analytic Modeling.

PubMed

Williams, Claire; Lewsey, James D; Mackay, Daniel F; Briggs, Andrew H

2017-05-01

Modeling of clinical-effectiveness in a cost-effectiveness analysis typically involves some form of partitioned survival or Markov decision-analytic modeling. The health states progression-free, progression and death and the transitions between them are frequently of interest. With partitioned survival, progression is not modeled directly as a state; instead, time in that state is derived from the difference in area between the overall survival and the progression-free survival curves. With Markov decision-analytic modeling, a priori assumptions are often made with regard to the transitions rather than using the individual patient data directly to model them. This article compares a multi-state modeling survival regression approach to these two common methods. As a case study, we use a trial comparing rituximab in combination with fludarabine and cyclophosphamide v. fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia. We calculated mean Life Years and QALYs that involved extrapolation of survival outcomes in the trial. We adapted an existing multi-state modeling approach to incorporate parametric distributions for transition hazards, to allow extrapolation. The comparison showed that, due to the different assumptions used in the different approaches, a discrepancy in results was evident. The partitioned survival and Markov decision-analytic modeling deemed the treatment cost-effective with ICERs of just over £16,000 and £13,000, respectively. However, the results with the multi-state modeling were less conclusive, with an ICER of just over £29,000. This work has illustrated that it is imperative to check whether assumptions are realistic, as different model choices can influence clinical and cost-effectiveness results.

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.

The dynamics of decision making in risky choice: an eye-tracking analysis.

PubMed

Fiedler, Susann; Glöckner, Andreas

2012-01-01

In the last years, research on risky choice has moved beyond analyzing choices only. Models have been suggested that aim to describe the underlying cognitive processes and some studies have tested process predictions of these models. Prominent approaches are evidence accumulation models such as decision field theory (DFT), simple serial heuristic models such as the adaptive toolbox, and connectionist approaches such as the parallel constraint satisfaction (PCS) model. In two studies involving measures of attention and pupil dilation, we investigate hypotheses derived from these models in choices between two gambles with two outcomes each. We show that attention to an outcome of a gamble increases with its probability and its value and that attention shifts toward the subsequently favored gamble after about two thirds of the decision process, indicating a gaze-cascade effect. Information search occurs mostly within-gambles, and the direction of search does not change over the course of decision making. Pupil dilation, which reflects both cognitive effort and arousal, increases during the decision process and increases with mean expected value. Overall, the results support aspects of automatic integration models for risky choice such as DFT and PCS, but in their current specification none of them can account for the full pattern of results.

Releases of whooping cranes to the Florida nonmigratory flock: a structured decision-making approach: report to the International Whooping Crane Recovery Team, September 22, 2008

USGS Publications Warehouse

Moore, Clinton T.; Converse, Sarah J.; Folk, Martin J.; Boughton, Robin; Brooks, Bill; French, John B.; O'Meara, Timothy; Putnam, Michael; Rodgers, James; Spalding, Marilyn

2008-01-01

We used a structured decision-making approach to inform the decision of whether the Florida Fish and Wildlife Conservation Commission should request of the International Whooping Crane Recovery Team that additional whooping crane chicks be released into the Florida Non-Migratory Population (FNMP). Structured decision-making is an application of decision science that strives to produce transparent, replicable, and defensible decisions that recognize the appropriate roles of management policy and science in decision-making. We present a multi-objective decision framework, where management objectives include successful establishment of a whooping crane population in Florida, minimization of costs, positive public relations, information gain, and providing a supply of captive-reared birds to alternative crane release projects, such as the Eastern Migratory Population. We developed models to predict the outcome relative to each of these objectives under 29 different scenarios of the release methodology used from 1993 to 2004, including options of no further releases and variable numbers of releases per year over the next 5-30 years. In particular, we developed a detailed set of population projection models, which make substantially different predictions about the probability of successful establishment of the FNMP. We used expert elicitation to develop prior model weights (measures of confidence in population model predictions); the results of the population model weighting and modelaveraging exercise indicated that the probability of successful establishment of the FNMP ranged from 9% if no additional releases are made, to as high as 41% with additional releases. We also used expert elicitation to develop weights (relative values) on the set of identified objectives, and we then used a formal optimization technique for identifying the optimal decision, which considers the tradeoffs between objectives. The optimal decision was identified as release of 3 cohorts (24 birds) per year over the next 10 years. However, any decision that involved release of 1-3 cohorts (8-24 birds) per year over the next 5 to 20 years, as well as decisions that involve skipping releases in every other year, performed better in our analysis than the alternative of no further releases. These results were driven by the relatively high objective weights that experts placed on the population objective (i.e., successful establishment of the FNMP) and the information gain objective (where releases are expected to accelerate learning on what was identified as a primary uncertainty: the demographic performance of wild-hatched birds). Additional considerations that were not formally integrated into the analysis are also discussed.

Mental model mapping as a new tool to analyse the use of information in decision-making in integrated water management

NASA Astrophysics Data System (ADS)

Kolkman, M. J.; Kok, M.; van der Veen, A.

The solution of complex, unstructured problems is faced with policy controversy and dispute, unused and misused knowledge, project delay and failure, and decline of public trust in governmental decisions. Mental model mapping (also called concept mapping) is a technique to analyse these difficulties on a fundamental cognitive level, which can reveal experiences, perceptions, assumptions, knowledge and subjective beliefs of stakeholders, experts and other actors, and can stimulate communication and learning. This article presents the theoretical framework from which the use of mental model mapping techniques to analyse this type of problems emerges as a promising technique. The framework consists of the problem solving or policy design cycle, the knowledge production or modelling cycle, and the (computer) model as interface between the cycles. Literature attributes difficulties in the decision-making process to communication gaps between decision makers, stakeholders and scientists, and to the construction of knowledge within different paradigm groups that leads to different interpretation of the problem situation. Analysis of the decision-making process literature indicates that choices, which are made in all steps of the problem solving cycle, are based on an individual decision maker’s frame of perception. This frame, in turn, depends on the mental model residing in the mind of the individual. Thus we identify three levels of awareness on which the decision process can be analysed. This research focuses on the third level. Mental models can be elicited using mapping techniques. In this way, analysing an individual’s mental model can shed light on decision-making problems. The steps of the knowledge production cycle are, in the same manner, ultimately driven by the mental models of the scientist in a specific discipline. Remnants of this mental model can be found in the resulting computer model. The characteristics of unstructured problems (complexity, uncertainty and disagreement) can be positioned in the framework, as can the communities of knowledge construction and valuation involved in the solution of these problems (core science, applied science, and professional consultancy, and “post-normal” science). Mental model maps, this research hypothesises, are suitable to analyse the above aspects of the problem. This hypothesis is tested for the case of the Zwolle storm surch barrier. Analysis can aid integration between disciplines, participation of public stakeholders, and can stimulate learning processes. Mental model mapping is recommended to visualise the use of knowledge, to analyse difficulties in problem solving process, and to aid information transfer and communication. Mental model mapping help scientists to shape their new, post-normal responsibilities in a manner that complies with integrity when dealing with unstructured problems in complex, multifunctional systems.

Present-value analysis: A systems approach to public decisionmaking for cost effectiveness

NASA Technical Reports Server (NTRS)

Herbert, T. T.

1971-01-01

Decision makers within Governmental agencies and Congress must evaluate competing (and sometimes conflicting) proposals which seek funding and implementation. Present value analysis can be an effective decision making tool by enabling the formal evaluation of the effects of competing proposals on efficient national resource utilization. A project's costs are not only its direct disbursements, but its social costs as well. How much does it cost to have those funds diverted from their use and economic benefit by the private sector to the public project? Comparisons of competing projects' social costs allow decision makers to expand their decision bases by quantifying the projects' impacts upon the economy and the efficient utilization of the country's limited national resources. A conceptual model is established for the choosing of the appropriate discount rate to be used in evaluation decisions through the technique.

An Investment Behavior Analysis using by Brain Computer Interface

NASA Astrophysics Data System (ADS)

Suzuki, Kyoko; Kinoshita, Kanta; Miyagawa, Kazuhiro; Shiomi, Shinichi; Misawa, Tadanobu; Shimokawa, Tetsuya

In this paper, we will construct a new Brain Computer Interface (BCI), for the purpose of analyzing human's investment decision makings. The BCI is made up of three functional parts which take roles of, measuring brain information, determining market price in an artificial market, and specifying investment decision model, respectively. When subjects make decisions, their brain information is conveyed to the part of specifying investment decision model through the part of measuring brain information, whereas, their decisions of investment order are sent to the part of artificial market to form market prices. Both the support vector machine and the 3 layered perceptron are used to assess the investment decision model. In order to evaluate our BCI, we conduct an experiment in which subjects and a computer trader agent trade shares of stock in the artificial market and test how the computer trader agent can forecast market price formation and investment decision makings from the brain information of subjects. The result of the experiment shows that the brain information can improve the accuracy of forecasts, and so the computer trader agent can supply market liquidity to stabilize market volatility without his loss.

Desktop microsimulation: a tool to improve efficiency in the medical office practice.

PubMed

Montgomery, James B; Linville, Beth A; Slonim, Anthony D

2013-01-01

Because the economic crisis in the United States continues to have an impact on healthcare organizations, industry leaders must optimize their decision making. Discrete-event computer simulation is a quality tool with a demonstrated track record of improving the precision of analysis for process redesign. However, the use of simulation to consolidate practices and design efficiencies into an unfinished medical office building was a unique task. A discrete-event computer simulation package was used to model the operations and forecast future results for four orthopedic surgery practices. The scenarios were created to allow an evaluation of the impact of process change on the output variables of exam room utilization, patient queue size, and staff utilization. The model helped with decisions regarding space allocation and efficient exam room use by demonstrating the impact of process changes in patient queues at check-in/out, x-ray, and cast room locations when compared to the status quo model. The analysis impacted decisions on facility layout, patient flow, and staff functions in this newly consolidated practice. Simulation was found to be a useful tool for process redesign and decision making even prior to building occupancy. © 2011 National Association for Healthcare Quality.

Measuring sustainable development using a multi-criteria model: a case study.

PubMed

Boggia, Antonio; Cortina, Carla

2010-11-01

This paper shows how Multi-criteria Decision Analysis (MCDA) can help in a complex process such as the assessment of the level of sustainability of a certain area. The paper presents the results of a study in which a model for measuring sustainability was implemented to better aid public policy decisions regarding sustainability. In order to assess sustainability in specific areas, a methodological approach based on multi-criteria analysis has been developed. The aim is to rank areas in order to understand the specific technical and/or financial support that they need to develop sustainable growth. The case study presented is an assessment of the level of sustainability in different areas of an Italian Region using the MCDA approach. Our results show that MCDA is a proper approach for sustainability assessment. The results are easy to understand and the evaluation path is clear and transparent. This is what decision makers need for having support to their decisions. The multi-criteria model for evaluation has been developed respecting the sustainable development economic theory, so that final results can have a clear meaning in terms of sustainability. Copyright 2010 Elsevier Ltd. All rights reserved.

A leader-follower-interactive method for regional water resources management with considering multiple water demands and eco-environmental constraints

NASA Astrophysics Data System (ADS)

Chen, Yizhong; Lu, Hongwei; Li, Jing; Ren, Lixia; He, Li

2017-05-01

This study presents the mathematical formulation and implementations of a synergistic optimization framework based on an understanding of water availability and reliability together with the characteristics of multiple water demands. This framework simultaneously integrates a set of leader-followers-interactive objectives established by different decision makers during the synergistic optimization. The upper-level model (leader's one) determines the optimal pollutants discharge to satisfy the environmental target. The lower-level model (follower's one) accepts the dispatch requirement from the upper-level one and dominates the optimal water-allocation strategy to maximize economic benefits representing the regional authority. The complicated bi-level model significantly improves upon the conventional programming methods through the mutual influence and restriction between the upper- and lower-level decision processes, particularly when limited water resources are available for multiple completing users. To solve the problem, a bi-level interactive solution algorithm based on satisfactory degree is introduced into the decision-making process for measuring to what extent the constraints are met and the objective reaches its optima. The capabilities of the proposed model are illustrated through a real-world case study of water resources management system in the district of Fengtai located in Beijing, China. Feasible decisions in association with water resources allocation, wastewater emission and pollutants discharge would be sequentially generated for balancing the objectives subject to the given water-related constraints, which can enable Stakeholders to grasp the inherent conflicts and trade-offs between the environmental and economic interests. The performance of the developed bi-level model is enhanced by comparing with single-level models. Moreover, in consideration of the uncertainty in water demand and availability, sensitivity analysis and policy analysis are employed for identifying their impacts on the final decisions and improving the practical applications.

Climate Induced Spillover and Implications for U.S. Security.

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

Tidwell, Vincent C.; Naugle, Asmeret Bier; Backus, George A.

Developing nations incur a greater risk to climate change than the developed world due to poorly managed human/natural resources, unreliable infrastructure and brittle governing/economic institutions. These vulnerabilities often give rise to a climate induced “domino effect” of reduced natural resource production-leading to economic hardship, social unrest, and humanitarian crises. Integral to this cascading set of events is increased human migration, leading to the “spillover” of impacts to adjoining areas with even broader impact on global markets and security. Given the complexity of factors influencing human migration and the resultant spill-over effect, quantitative tools are needed to aid policy analysis. Towardmore » this need, a series of migration models were developed along with a system dynamics model of the spillover effect. The migration decision models were structured according to two interacting paths, one that captured long-term “chronic” impacts related to protracted deteriorating quality of life and a second focused on short-term “acute” impacts of disaster and/or conflict. Chronic migration dynamics were modeled for two different cases; one that looked only at emigration but at a national level for the entire world; and a second that looked at both emigration and immigration but focused on a single nation. Model parameterization for each of the migration models was accomplished through regression analysis using decadal data spanning the period 1960-2010. A similar approach was taken with acute migration dynamics except regression analysis utilized annual data sets limited to a shorter time horizon (2001-2013). The system dynamics spillover model was organized around two broad modules, one simulating the decision dynamics of migration and a second module that treats the changing environmental conditions that influence the migration decision. The environmental module informs the migration decision, endogenously simulating interactions/changes in the economy, labor, population, conflict, water, and food. A regional model focused on Mali in western Africa was used as a test case to demonstrate the efficacy of the model.« less

A Cognitive Model of College Mathematics Placement

DTIC Science & Technology

1989-08-01

study focused on the precalculus -- calculus placement decision. The Cognitive model uses novel, or analysis level, placement test items in an attempt to...relative to the requirements of a precalculus course. Placement test scores may be partitioned to give analysis and non-analysis subtest scores which can...67 5.1.1 1989 Intercorrelations ....................................................................... 67 5.1.2 1989 Precalculus -Calculus

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.

Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

USGS Publications Warehouse

Zajac, Zuzanna; Stith, Bradley M.; Bowling, Andrea C.; Langtimm, Catherine A.; Swain, Eric D.

2015-01-01

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low-quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision-making framework will result in better-informed, more robust decisions.

Decision-making analysis for allergen immunotherapy versus nasal steroids in the treatment of nasal steroid-responsive allergic rhinitis.

PubMed

Kennedy, Joshua L; Robinson, Derek; Christophel, Jared; Borish, Larry; Payne, Spencer

2014-01-01

The purpose of the study was to determine the age at which initiation of specific subcutaneous immunotherapy (SCIT) becomes more cost-effective than continued lifetime intranasal steroid (NS) therapy in the treatment of allergic rhinitis, with the use of a decision analysis model. A Markov decision analysis model was created for this study. Economic analyses were performed to identify "break-even" points in the treatment of allergic rhinitis with the use of SCIT and NS. Efficacy rates for therapy and cost data were collected from the published literature. Models in which there was only incomplete improvement while receiving SCIT were also evaluated for economic break-even points. The primary perspective of the study was societal. Multiple break-even point curves were obtained corresponding to various clinical scenarios. For patients with seasonal allergic rhinitis requiring NS (i.e., fluticasone) 6 months per year, the age at which initiation of SCIT provides long-term direct cost advantage is less than 41 years. For patients with perennial rhinitis symptoms requiring year-round NS, the cut-off age for SCIT cost-effectiveness increases to 60 years. Hypothetical subjects who require continued NS treatment (50% reduction of previous dosage) while receiving SCIT also display break-even points, whereby it is economically advantageous to consider allergy referral and SCIT, dependent on the cost of the NS prescribed. The age at which SCIT provides economic advantages over NS in the treatment of allergic rhinitis depends on multiple clinical factors. Decision analysis models can assist the physician in accounting for these factors and customize patient counseling with regard to treatment options.

Evidence used in model-based economic evaluations for evaluating pharmacogenetic and pharmacogenomic tests: a systematic review protocol

PubMed Central

Peters, Jaime L; Cooper, Chris; Buchanan, James

2015-01-01

Introduction Decision models can be used to conduct economic evaluations of new pharmacogenetic and pharmacogenomic tests to ensure they offer value for money to healthcare systems. These models require a great deal of evidence, yet research suggests the evidence used is diverse and of uncertain quality. By conducting a systematic review, we aim to investigate the test-related evidence used to inform decision models developed for the economic evaluation of genetic tests. Methods and analysis We will search electronic databases including MEDLINE, EMBASE and NHS EEDs to identify model-based economic evaluations of pharmacogenetic and pharmacogenomic tests. The search will not be limited by language or date. Title and abstract screening will be conducted independently by 2 reviewers, with screening of full texts and data extraction conducted by 1 reviewer, and checked by another. Characteristics of the decision problem, the decision model and the test evidence used to inform the model will be extracted. Specifically, we will identify the reported evidence sources for the test-related evidence used, describe the study design and how the evidence was identified. A checklist developed specifically for decision analytic models will be used to critically appraise the models described in these studies. Variations in the test evidence used in the decision models will be explored across the included studies, and we will identify gaps in the evidence in terms of both quantity and quality. Dissemination The findings of this work will be disseminated via a peer-reviewed journal publication and at national and international conferences. PMID:26560056

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.

Economic analyses to support decisions about HPV vaccination in low- and middle-income countries: a consensus report and guide for analysts.

PubMed

Jit, Mark; Levin, Carol; Brisson, Marc; Levin, Ann; Resch, Stephen; Berkhof, Johannes; Kim, Jane; Hutubessy, Raymond

2013-01-30

Low- and middle-income countries need to consider economic issues such as cost-effectiveness, affordability and sustainability before introducing a program for human papillomavirus (HPV) vaccination. However, many such countries lack the technical capacity and data to conduct their own analyses. Analysts informing policy decisions should address the following questions: 1) Is an economic analysis needed? 2) Should analyses address costs, epidemiological outcomes, or both? 3) If costs are considered, what sort of analysis is needed? 4) If outcomes are considered, what sort of model should be used? 5) How complex should the analysis be? 6) How should uncertainty be captured? 7) How should model results be communicated? Selecting the appropriate analysis is essential to ensure that all the important features of the decision problem are correctly represented, but that the analyses are not more complex than necessary. This report describes the consensus of an expert group convened by the World Health Organization, prioritizing key issues to be addressed when considering economic analyses to support HPV vaccine introduction in these countries.

Semiparametric Thurstonian Models for Recurrent Choices: A Bayesian Analysis

ERIC Educational Resources Information Center

Ansari, Asim; Iyengar, Raghuram

2006-01-01

We develop semiparametric Bayesian Thurstonian models for analyzing repeated choice decisions involving multinomial, multivariate binary or multivariate ordinal data. Our modeling framework has multiple components that together yield considerable flexibility in modeling preference utilities, cross-sectional heterogeneity and parameter-driven…

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

Nash Equilibria in Theory of Reasoned Action

NASA Astrophysics Data System (ADS)

Almeida, Leando; Cruz, José; Ferreira, Helena; Pinto, Alberto Adrego

2009-08-01

Game theory and Decision Theory have been applied to many different areas such as Physics, Economics, Biology, etc. In its application to Psychology, we introduce, in the literature, a Game Theoretical Model of Planned Behavior or Reasoned Action by establishing an analogy between two specific theories. In this study we take in account that individual decision-making is an outcome of a process where group decisions can determine individual probabilistic behavior. Using Game Theory concepts, we describe how intentions can be transformed in behavior and according to the Nash Equilibrium, this process will correspond to the best individual decision/response taking in account the collective response. This analysis can be extended to several examples based in the Game Theoretical Model of Planned Behavior or Reasoned Action.

Using Decision Structures for Policy Analysis in Software Product-line Evolution - A Case Study

NASA Astrophysics Data System (ADS)

Sarang, Nita; Sanglikar, Mukund A.

Project management decisions are the primary basis for project success (or failure). Mostly, such decisions are based on an intuitive understanding of the underlying software engineering and management process and have a likelihood of being misjudged. Our problem domain is product-line evolution. We model the dynamics of the process by incorporating feedback loops appropriate to two decision structures: staffing policy, and the forces of growth associated with long-term software evolution. The model is executable and supports project managers to assess the long-term effects of possible actions. Our work also corroborates results from earlier studies of E-type systems, in particular the FEAST project and the rules for software evolution, planning and management.

Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed

USGS Publications Warehouse

Balk, Benjamin; Elder, Kelly

2000-01-01

We model the spatial distribution of snow across a mountain basin using an approach that combines binary decision tree and geostatistical techniques. In April 1997 and 1998, intensive snow surveys were conducted in the 6.9‐km2 Loch Vale watershed (LVWS), Rocky Mountain National Park, Colorado. Binary decision trees were used to model the large‐scale variations in snow depth, while the small‐scale variations were modeled through kriging interpolation methods. Binary decision trees related depth to the physically based independent variables of net solar radiation, elevation, slope, and vegetation cover type. These decision tree models explained 54–65% of the observed variance in the depth measurements. The tree‐based modeled depths were then subtracted from the measured depths, and the resulting residuals were spatially distributed across LVWS through kriging techniques. The kriged estimates of the residuals were added to the tree‐based modeled depths to produce a combined depth model. The combined depth estimates explained 60–85% of the variance in the measured depths. Snow densities were mapped across LVWS using regression analysis. Snow‐covered area was determined from high‐resolution aerial photographs. Combining the modeled depths and densities with a snow cover map produced estimates of the spatial distribution of snow water equivalence (SWE). This modeling approach offers improvement over previous methods of estimating SWE distribution in mountain basins.

Impact of model-based risk analysis for liver surgery planning.

PubMed

Hansen, C; Zidowitz, S; Preim, B; Stavrou, G; Oldhafer, K J; Hahn, H K

2014-05-01

A model-based risk analysis for oncologic liver surgery was described in previous work (Preim et al. in Proceedings of international symposium on computer assisted radiology and surgery (CARS), Elsevier, Amsterdam, pp. 353–358, 2002; Hansen et al. Int I Comput Assist Radiol Surg 4(5):469–474, 2009). In this paper, we present an evaluation of this method. To prove whether and how the risk analysis facilitates the process of liver surgery planning, an explorative user study with 10 liver experts was conducted. The purpose was to compare and analyze their decision-making. The results of the study show that model-based risk analysis enhances the awareness of surgical risk in the planning stage. Participants preferred smaller resection volumes and agreed more on the safety margins’ width in case the risk analysis was available. In addition, time to complete the planning task and confidence of participants were not increased when using the risk analysis. This work shows that the applied model-based risk analysis may influence important planning decisions in liver surgery. It lays a basis for further clinical evaluations and points out important fields for future research.

The Role of Dysfunctional Myths in a Decision-Making Process under Bounded Rationality: A Complex Dynamical Systems Perspective.

PubMed

Stamovlasis, Dimitrios; Vaiopoulou, Julie

2017-07-01

The present study examines the factors influencing a decision-making process, with specific focus on the role of dysfunctional myths (DM). DM are thoughts or beliefs that are rather irrational, however influential to people's decisions. In this paper a decision-making process regarding the career choice of university students majoring in natural sciences and education (N=496) is examined by analyzing survey data taken via Career Decision Making Difficulties Questionnaire (CDDQ). The difficulty of making the choice and the certainty about one's decision were the state variables, while the independent variables were factors related to the lack of information or knowledge needed, which actually reflect a bounded rationality. Cusp catastrophe analysis, based on both least squares and maximum likelihood procedures, showed that the nonlinear models predicting the two state variables were superior to linear alternatives. Factors related to lack of knowledge about the steps involved in the process of career decision-making, lack of information about the various occupations, lack of information about self and lack of motivation acted as asymmetry, while dysfunctional myths acted as bifurcation factor for both state variables. The catastrophe model, grounded in empirical data, revealed a unique role for DM and a better interpretation within the context of complexity and the notion of bounded rationality. The analysis opens the nonlinear dynamical systems (NDS) perspective in studying decision-making processes. Theoretical and practical implications are discussed.

Enabling Real-time Water Decision Support Services Using Model as a Service

NASA Astrophysics Data System (ADS)

Zhao, T.; Minsker, B. S.; Lee, J. S.; Salas, F. R.; Maidment, D. R.; David, C. H.

2014-12-01

Through application of computational methods and an integrated information system, data and river modeling services can help researchers and decision makers more rapidly understand river conditions under alternative scenarios. To enable this capability, workflows (i.e., analysis and model steps) are created and published as Web services delivered through an internet browser, including model inputs, a published workflow service, and visualized outputs. The RAPID model, which is a river routing model developed at University of Texas Austin for parallel computation of river discharge, has been implemented as a workflow and published as a Web application. This allows non-technical users to remotely execute the model and visualize results as a service through a simple Web interface. The model service and Web application has been prototyped in the San Antonio and Guadalupe River Basin in Texas, with input from university and agency partners. In the future, optimization model workflows will be developed to link with the RAPID model workflow to provide real-time water allocation decision support services.

Multisensor satellite data for water quality analysis and water pollution risk assessment: decision making under deep uncertainty with fuzzy algorithm in framework of multimodel approach

NASA Astrophysics Data System (ADS)

Kostyuchenko, Yuriy V.; Sztoyka, Yulia; Kopachevsky, Ivan; Artemenko, Igor; Yuschenko, Maxim

2017-10-01

Multi-model approach for remote sensing data processing and interpretation is described. The problem of satellite data utilization in multi-modeling approach for socio-ecological risks assessment is formally defined. Observation, measurement and modeling data utilization method in the framework of multi-model approach is described. Methodology and models of risk assessment in framework of decision support approach are defined and described. Method of water quality assessment using satellite observation data is described. Method is based on analysis of spectral reflectance of aquifers. Spectral signatures of freshwater bodies and offshores are analyzed. Correlations between spectral reflectance, pollutions and selected water quality parameters are analyzed and quantified. Data of MODIS, MISR, AIRS and Landsat sensors received in 2002-2014 have been utilized verified by in-field spectrometry and lab measurements. Fuzzy logic based approach for decision support in field of water quality degradation risk is discussed. Decision on water quality category is making based on fuzzy algorithm using limited set of uncertain parameters. Data from satellite observations, field measurements and modeling is utilizing in the framework of the approach proposed. It is shown that this algorithm allows estimate water quality degradation rate and pollution risks. Problems of construction of spatial and temporal distribution of calculated parameters, as well as a problem of data regularization are discussed. Using proposed approach, maps of surface water pollution risk from point and diffuse sources are calculated and discussed.

QTest: Quantitative Testing of Theories of Binary Choice.

PubMed

Regenwetter, Michel; Davis-Stober, Clintin P; Lim, Shiau Hong; Guo, Ying; Popova, Anna; Zwilling, Chris; Cha, Yun-Shil; Messner, William

2014-01-01

The goal of this paper is to make modeling and quantitative testing accessible to behavioral decision researchers interested in substantive questions. We provide a novel, rigorous, yet very general, quantitative diagnostic framework for testing theories of binary choice. This permits the nontechnical scholar to proceed far beyond traditionally rather superficial methods of analysis, and it permits the quantitatively savvy scholar to triage theoretical proposals before investing effort into complex and specialized quantitative analyses. Our theoretical framework links static algebraic decision theory with observed variability in behavioral binary choice data. The paper is supplemented with a custom-designed public-domain statistical analysis package, the QTest software. We illustrate our approach with a quantitative analysis using published laboratory data, including tests of novel versions of "Random Cumulative Prospect Theory." A major asset of the approach is the potential to distinguish decision makers who have a fixed preference and commit errors in observed choices from decision makers who waver in their preferences.

Model application niche analysis: Assessing the transferability and generalizability of ecological models

EPA Science Inventory

The use of models by ecologist and environmental managers, to inform environmental management and decision-making, has grown exponentially in the past 50 years. Due to logistical, economical and theoretical benefits, model users are frequently transferring preexisting models to n...

Development of a Shared Decision Making coding system for analysis of patient-healthcare provider encounters

PubMed Central

Clayman, Marla L.; Makoul, Gregory; Harper, Maya M.; Koby, Danielle G.; Williams, Adam R.

2012-01-01

Objectives Describe the development and refinement of a scheme, Detail of Essential Elements and Participants in Shared Decision Making (DEEP-SDM), for coding Shared Decision Making (SDM) while reporting on the characteristics of decisions in a sample of patients with metastatic breast cancer. Methods The Evidence-Based Patient Choice instrument was modified to reflect Makoul and Clayman’s Integrative Model of SDM. Coding was conducted on video recordings of 20 women at the first visit with their medical oncologists after suspicion of disease progression. Noldus Observer XT v.8, a video coding software platform, was used for coding. Results The sample contained 80 decisions (range: 1-11), divided into 150 decision making segments. Most decisions were physician-led, although patients and physicians initiated similar numbers of decision-making conversations. Conclusion DEEP-SDM facilitates content analysis of encounters between women with metastatic breast cancer and their medical oncologists. Despite the fractured nature of decision making, it is possible to identify decision points and to code each of the Essential Elements of Shared Decision Making. Further work should include application of DEEP-SDM to non-cancer encounters. Practice Implications: A better understanding of how decisions unfold in the medical encounter can help inform the relationship of SDM to patient-reported outcomes. PMID:22784391

A Structured Decision Approach for Integrating and Analyzing Community Perspectives in Re-Use Planning of Vacant Properties in Cleveland, Ohio

EPA Science Inventory

An integrated GIS-based, multi-attribute decision model deployed in a web-based platform is presented enabling an iterative, spatially explicit and collaborative analysis of relevant and available information for repurposing vacant land. The process incorporated traditional and ...

Psychological Peculiarities of Judge Professional Activity and Decision-Making

ERIC Educational Resources Information Center

Uspanov, Zholdybai T.; Turabayeva, Dana S.

2016-01-01

The article considers the psychological peculiarities of judge professional activity and decision-making, judge's mental set and requirements to ethical and moral requirements and quality. Moreover, this work offers original job analysis and competency model of judge professional activity. The authors have studied the problems concerning the…

REGIONAL VULNERABILITY ASSESSMENT (REVA) IMPROVING ENVIRONMENTAL DECISION MAKING THROUGH CLIENT PARTNERSHIPS

EPA Science Inventory

The Regional Vulnerability Assessment (ReV A) Program is an applied research program t,1at is focusing on using spatial information and model results to support environmental decision-making at regional- down to local-scales. Re VA has developed analysis and assessment methods to...

Modeling Hospital Discharge and Placement Decision Making: Whither the Elderly.

ERIC Educational Resources Information Center

Clark, William F.; Pelham, Anabel O.

This paper examines the hospital discharge decision making process for elderly patients, based on observations of the operations of a long term care agency, the California Multipurpose Senior Services Project. The analysis is divided into four components: actors, factors, processes, and strategy critique. The first section discusses the major…

Formulary evaluation of third-generation cephalosporins using decision analysis.

PubMed

Cano, S B; Fujita, N K

1988-03-01

A structured, objective approach to formulary review of third-generation cephalosporins using the decision-analysis model is described. The pharmacy and therapeutics (P&T) committee approved the evaluation criteria for this drug class and assigned priority weights (as percentages of 100) to those drug characteristics deemed most important. Clinical data (spectrum of activity, pharmacokinetics, adverse effects, and stability) and financial data (cost of acquisition and cost of therapy per day) were used to determine ranking scores for each drug. Total scores were determined by multiplying ranking scores by the assigned priority weights for the criteria. The two highest-scoring drugs were selected for inclusion in the formulary. By this decision-analysis process, the P&T committee recommended that all current third-generation cephalosporins (cefotaxime, cefoperazone, and moxalactam) be removed from the institutions's formulary and be replaced with ceftazidime and ceftriaxone. P&T committees at other institutions may structure their criteria differently, and different recommendations may result. Using decision analysis for formulary review may promote rational drug therapy and achieve cost savings.

Command Process Modeling & Risk Analysis

NASA Technical Reports Server (NTRS)

Meshkat, Leila

2011-01-01

Commanding Errors may be caused by a variety of root causes. It's important to understand the relative significance of each of these causes for making institutional investment decisions. One of these causes is the lack of standardized processes and procedures for command and control. We mitigate this problem by building periodic tables and models corresponding to key functions within it. These models include simulation analysis and probabilistic risk assessment models.

Systems analysis in land-use planning... a conceptual development

Treesearch

Ronald A. Oliveira

1973-01-01

A planning model in which social, economic, and environmental constraints are specified--especially in mathematical form--can be helpful in decisionmaking. The general structure of a land-use decision model approached through systems analysis is described. The proposed procedures emphasize the quantification of interrelationships between uses and the specification of...

Robustness analysis of a green chemistry-based model for the classification of silver nanoparticles synthesis processes

EPA Science Inventory

This paper proposes a robustness analysis based on Multiple Criteria Decision Aiding (MCDA). The ensuing model was used to assess the implementation of green chemistry principles in the synthesis of silver nanoparticles. Its recommendations were also compared to an earlier develo...

Assessment of flood susceptible areas using spatially explicit, probabilistic multi-criteria decision analysis

NASA Astrophysics Data System (ADS)

Tang, Zhongqian; Zhang, Hua; Yi, Shanzhen; Xiao, Yangfan

2018-03-01

GIS-based multi-criteria decision analysis (MCDA) is increasingly used to support flood risk assessment. However, conventional GIS-MCDA methods fail to adequately represent spatial variability and are accompanied with considerable uncertainty. It is, thus, important to incorporate spatial variability and uncertainty into GIS-based decision analysis procedures. This research develops a spatially explicit, probabilistic GIS-MCDA approach for the delineation of potentially flood susceptible areas. The approach integrates the probabilistic and the local ordered weighted averaging (OWA) methods via Monte Carlo simulation, to take into account the uncertainty related to criteria weights, spatial heterogeneity of preferences and the risk attitude of the analyst. The approach is applied to a pilot study for the Gucheng County, central China, heavily affected by the hazardous 2012 flood. A GIS database of six geomorphological and hydrometeorological factors for the evaluation of susceptibility was created. Moreover, uncertainty and sensitivity analysis were performed to investigate the robustness of the model. The results indicate that the ensemble method improves the robustness of the model outcomes with respect to variation in criteria weights and identifies which criteria weights are most responsible for the variability of model outcomes. Therefore, the proposed approach is an improvement over the conventional deterministic method and can provides a more rational, objective and unbiased tool for flood susceptibility evaluation.

Applications of the International Space Station Probabilistic Risk Assessment Model

NASA Technical Reports Server (NTRS)

Grant, Warren; Lutomski, Michael G.

2011-01-01

Recently the International Space Station (ISS) has incorporated more Probabilistic Risk Assessments (PRAs) in the decision making process for significant issues. Future PRAs will have major impact to ISS and future spacecraft development and operations. These PRAs will have their foundation in the current complete ISS PRA model and the current PRA trade studies that are being analyzed as requested by ISS Program stakeholders. ISS PRAs have recently helped in the decision making process for determining reliability requirements for future NASA spacecraft and commercial spacecraft, making crew rescue decisions, as well as making operational requirements for ISS orbital orientation, planning Extravehicular activities (EVAs) and robotic operations. This paper will describe some applications of the ISS PRA model and how they impacted the final decision. This paper will discuss future analysis topics such as life extension, requirements of new commercial vehicles visiting ISS.

Multi-criteria analysis for PM10 planning

NASA Astrophysics Data System (ADS)

Pisoni, Enrico; Carnevale, Claudio; Volta, Marialuisa

To implement sound air quality policies, Regulatory Agencies require tools to evaluate outcomes and costs associated to different emission reduction strategies. These tools are even more useful when considering atmospheric PM10 concentrations due to the complex nonlinear processes that affect production and accumulation of the secondary fraction of this pollutant. The approaches presented in the literature (Integrated Assessment Modeling) are mainly cost-benefit and cost-effective analysis. In this work, the formulation of a multi-objective problem to control particulate matter is proposed. The methodology defines: (a) the control objectives (the air quality indicator and the emission reduction cost functions); (b) the decision variables (precursor emission reductions); (c) the problem constraints (maximum feasible technology reductions). The cause-effect relations between air quality indicators and decision variables are identified tuning nonlinear source-receptor models. The multi-objective problem solution provides to the decision maker a set of not-dominated scenarios representing the efficient trade-off between the air quality benefit and the internal costs (emission reduction technology costs). The methodology has been implemented for Northern Italy, often affected by high long-term exposure to PM10. The source-receptor models used in the multi-objective analysis are identified processing long-term simulations of GAMES multiphase modeling system, performed in the framework of CAFE-Citydelta project.

Science-based HRA: experimental comparison of operator performance to IDAC (Information-Decision-Action Crew) simulations

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

Shirley, Rachel; Smidts, Carol; Boring, Ronald

Information-Decision-Action Crew (IDAC) operator model simulations of a Steam Generator Tube Rupture are compared to student operator performance in studies conducted in the Ohio State University’s Nuclear Power Plant Simulator Facility. This study is presented as a prototype for conducting simulator studies to validate key aspects of Human Reliability Analysis (HRA) methods. Seven student operator crews are compared to simulation results for crews designed to demonstrate three different decision-making strategies. The IDAC model used in the simulations is modified slightly to capture novice behavior rather that expert operators. Operator actions and scenario pacing are compared. A preliminary review of availablemore » performance shaping factors (PSFs) is presented. After the scenario in the NPP Simulator Facility, student operators review a video of the scenario and evaluate six PSFs at pre-determined points in the scenario. This provides a dynamic record of the PSFs experienced by the OSU student operators. In this preliminary analysis, Time Constraint Load (TCL) calculated in the IDAC simulations is compared to TCL reported by student operators. We identify potential modifications to the IDAC model to develop an “IDAC Student Operator Model.” This analysis provides insights into how similar experiments could be conducted using expert operators to improve the fidelity of IDAC simulations.« less

The art of maturity modeling. Part 2. Alternative models and sensitivity analysis

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

Waples, D.W.; Suizu, Masahiro; Kamata, Hiromi

1992-01-01

The sensitivity of exploration decisions to variations in several input parameters for maturity modeling was examined for the MITI Rumoi well, Hokkaido, Japan. Decisions were almost completely insensitive to uncertainties about formation age and erosional removal across some unconformities, but were more sensitive to changes in removal during unconformities which occurred near maximum paleotemperatures. Exploration decisions were not very sensitive to the choice of a particular kinetic model for hydrocarbon generation. Uncertainties in kerogen type and the kinetics of different kerogen types are more serious than differences among the various kinetic models. Results of modeling using the TTI method weremore » unsatisfactory. Thermal history and timing and amount of hydrocarbon generation estimated or calculated using the TTI method were greatly different from those obtained using a purely kinetic model. The authors strongly recommend use of the kinetic R{sub o} method instead of the TTI method. If they had lacked measured R{sub o} data, subsurface temperature data, or both, their confidence in the modeling results would have been sharply reduced. Conceptual models for predicting heat flow and thermal conductivity are simply too weak at present to allow one to carry out highly meaningful modeling unless the input is constrained by measured data. Maturity modeling therefore requires the use of more, not fewer, measured temperature and maturity data. The use of sensitivity analysis in maturity modeling is very important for understanding the geologic system, for knowing what level of confidence to place on the results, and for determining what new types of data would be most necessary to improve confidence. Sensitivity analysis can be carried out easily using a rapid, interactive maturity-modeling program.« less

Stimulating household flood risk mitigation investments through insurance and subsidies: an Agent-Based Modelling approach

NASA Astrophysics Data System (ADS)

Haer, Toon; Botzen, Wouter; de Moel, Hans; Aerts, Jeroen

2015-04-01

In the period 1998-2009, floods triggered roughly 52 billion euro in insured economic losses making floods the most costly natural hazard in Europe. Climate change and socio/economic trends are expected to further aggrevate floods losses in many regions. Research shows that flood risk can be significantly reduced if households install protective measures, and that the implementation of such measures can be stimulated through flood insurance schemes and subsidies. However, the effectiveness of such incentives to stimulate implementation of loss-reducing measures greatly depends on the decision process of individuals and is hardly studied. In our study, we developed an Agent-Based Model that integrates flood damage models, insurance mechanisms, subsidies, and household behaviour models to assess the effectiveness of different economic tools on stimulating households to invest in loss-reducing measures. Since the effectiveness depends on the decision making process of individuals, the study compares different household decision models ranging from standard economic models, to economic models for decision making under risk, to more complex decision models integrating economic models and risk perceptions, opinion dynamics, and the influence of flood experience. The results show the effectiveness of incentives to stimulate investment in loss-reducing measures for different household behavior types, while assuming climate change scenarios. It shows how complex decision models can better reproduce observed real-world behaviour compared to traditional economic models. Furthermore, since flood events are included in the simulations, the results provide an analysis of the dynamics in insured and uninsured losses for households, the costs of reducing risk by implementing loss-reducing measures, the capacity of the insurance market, and the cost of government subsidies under different scenarios. The model has been applied to the City of Rotterdam in The Netherlands.

Water Quality Analysis Simulation

EPA Pesticide Factsheets

The Water Quality analysis simulation Program, an enhancement of the original WASP. This model helps users interpret and predict water quality responses to natural phenomena and man-made pollution for variious pollution management decisions.

Decision analysis framing study; in-valley drainage management strategies for the western San Joaquin Valley, California

USGS Publications Warehouse

Presser, Theresa S.; Jenni, Karen E.; Nieman, Timothy; Coleman, James

2010-01-01

Constraints on drainage management in the western San Joaquin Valley and implications of proposed approaches to management were recently evaluated by the U.S. Geological Survey (USGS). The USGS found that a significant amount of data for relevant technical issues was available and that a structured, analytical decision support tool could help optimize combinations of specific in-valley drainage management strategies, address uncertainties, and document underlying data analysis for future use. To follow-up on USGS's technical analysis and to help define a scientific basis for decisionmaking in implementing in-valley drainage management strategies, this report describes the first step (that is, a framing study) in a Decision Analysis process. In general, a Decision Analysis process includes four steps: (1) problem framing to establish the scope of the decision problem(s) and a set of fundamental objectives to evaluate potential solutions, (2) generation of strategies to address identified decision problem(s), (3) identification of uncertainties and their relationships, and (4) construction of a decision support model. Participation in such a systematic approach can help to promote consensus and to build a record of qualified supporting data for planning and implementation. In December 2008, a Decision Analysis framing study was initiated with a series of meetings designed to obtain preliminary input from key stakeholder groups on the scope of decisions relevant to drainage management that were of interest to them, and on the fundamental objectives each group considered relevant to those decisions. Two key findings of this framing study are: (1) participating stakeholders have many drainage management objectives in common; and (2) understanding the links between drainage management and water management is necessary both for sound science-based decisionmaking and for resolving stakeholder differences about the value of proposed drainage management solutions. Citing ongoing legal processes associated with drainage management in the western San Joaquin Valley, the U.S. Bureau of Reclamation (USBR) withdrew from the Decision Analysis process early in the proceedings. Without the involvement of the USBR, the USGS discontinued further development of this study.

A modeling framework for optimal long-term care insurance purchase decisions in retirement planning.

PubMed

Gupta, Aparna; Li, Lepeng

2004-05-01

The level of need and costs of obtaining long-term care (LTC) during retired life require that planning for it is an integral part of retirement planning. In this paper, we divide retirement planning into two phases, pre-retirement and post-retirement. On the basis of four interrelated models for health evolution, wealth evolution, LTC insurance premium and coverage, and LTC cost structure, a framework for optimal LTC insurance purchase decisions in the pre-retirement phase is developed. Optimal decisions are obtained by developing a trade-off between post-retirement LTC costs and LTC insurance premiums and coverage. Two-way branching models are used to model stochastic health events and asset returns. The resulting optimization problem is formulated as a dynamic programming problem. We compare the optimal decision under two insurance purchase scenarios: one assumes that insurance is purchased for good and other assumes it may be purchased, relinquished and re-purchased. Sensitivity analysis is performed for the retirement age.

Treatment strategies for pelvic organ prolapse: a cost-effectiveness analysis.

PubMed

Hullfish, Kathie L; Trowbridge, Elisa R; Stukenborg, George J

2011-05-01

To compare the relative cost effectiveness of treatment decision alternatives for post-hysterectomy pelvic organ prolapse (POP). A Markov decision analysis model was used to assess and compare the relative cost effectiveness of expectant management, use of a pessary, and surgery for obtaining months of quality-adjusted life over 1 year. Sensitivity analysis was conducted to determine whether the results depended on specific estimates of patient utilities for pessary use, probabilities for complications and other events, and estimated costs. Only two treatment alternatives were found to be efficient choices: initial pessary use and vaginal reconstructive surgery (VRS). Pessary use (including patients that eventually transitioned to surgery) achieved 10.4 quality-adjusted months, at a cost of $10,000 per patient, while VRS obtained 11.4 quality-adjusted months, at $15,000 per patient. Sensitivity analysis demonstrated that these baseline results depended on several key estimates in the model. This analysis indicates that pessary use and VRS are the most cost-effective treatment alternatives for treating post-hysterectomy vaginal prolapse. Additional research is needed to standardize POP outcomes and complications, so that healthcare providers can best utilize cost information in balancing the risks and benefits of their treatment decisions.

Performance evaluation of automated manufacturing systems using generalized stochastic Petri Nets. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Al-Jaar, Robert Y.; Desrochers, Alan A.

1989-01-01

The main objective of this research is to develop a generic modeling methodology with a flexible and modular framework to aid in the design and performance evaluation of integrated manufacturing systems using a unified model. After a thorough examination of the available modeling methods, the Petri Net approach was adopted. The concurrent and asynchronous nature of manufacturing systems are easily captured by Petri Net models. Three basic modules were developed: machine, buffer, and Decision Making Unit. The machine and buffer modules are used for modeling transfer lines and production networks. The Decision Making Unit models the functions of a computer node in a complex Decision Making Unit Architecture. The underlying model is a Generalized Stochastic Petri Net (GSPN) that can be used for performance evaluation and structural analysis. GSPN's were chosen because they help manage the complexity of modeling large manufacturing systems. There is no need to enumerate all the possible states of the Markov Chain since they are automatically generated from the GSPN model.

Development of prognostic model for predicting survival after retrograde placement of ureteral stent in advanced gastrointestinal cancer patients and its evaluation by decision curve analysis.

PubMed

Kawano, Shingo; Komai, Yoshinobu; Ishioka, Junichiro; Sakai, Yasuyuki; Fuse, Nozomu; Ito, Masaaki; Kihara, Kazunori; Saito, Norio

2016-10-01

The aim of this study was to determine risk factors for survival after retrograde placement of ureteral stents and develop a prognostic model for advanced gastrointestinal tract (GIT: esophagus, stomach, colon and rectum) cancer patients. We examined the clinical records of 122 patients who underwent retrograde placement of a ureteral stent against malignant extrinsic ureteral obstruction. A prediction model for survival after stenting was developed. We compared its clinical usefulness with our previous model based on the results from nephrostomy cases by decision curve analysis. Median follow-up period was 201 days (8-1490) and 97 deaths occurred. The 1-year survival rate in this cohort was 29%. Based on multivariate analysis, primary site of colon origin, absence of retroperitoneal lymph node metastasis and serum albumin >3g/dL were significantly associated with a prolonged survival time. To develop a prognostic model, we divided the patients into 3 risk groups of favorable: 0-1 factors (N.=53), intermediate: 2 risk factors (N.=54), and poor: 3 risk factors (N.=15). There were significant differences in the survival profiles of these 3 risk groups (P<0.0001). Decision curve analyses revealed that the current model has a superior net benefit than our previous model for most of the examined probabilities. We have developed a novel prognostic model for GIT cancer patients who were treated with retrograde placement of a ureteral stent. The current model should help urologists and medical oncologists to predict survival in cases of malignant extrinsic ureteral obstruction.

Development of a model-based flood emergency management system in Yujiang River Basin, South China

NASA Astrophysics Data System (ADS)

Zeng, Yong; Cai, Yanpeng; Jia, Peng; Mao, Jiansu

2014-06-01

Flooding is the most frequent disaster in China. It affects people's lives and properties, causing considerable economic loss. Flood forecast and operation of reservoirs are important in flood emergency management. Although great progress has been achieved in flood forecast and reservoir operation through using computer, network technology, and geographic information system technology in China, the prediction accuracy of models are not satisfactory due to the unavailability of real-time monitoring data. Also, real-time flood control scenario analysis is not effective in many regions and can seldom provide online decision support function. In this research, a decision support system for real-time flood forecasting in Yujiang River Basin, South China (DSS-YRB) is introduced in this paper. This system is based on hydrological and hydraulic mathematical models. The conceptual framework and detailed components of the proposed DSS-YRB is illustrated, which employs real-time rainfall data conversion, model-driven hydrologic forecasting, model calibration, data assimilation methods, and reservoir operational scenario analysis. Multi-tiered architecture offers great flexibility, portability, reusability, and reliability. The applied case study results show the development and application of a decision support system for real-time flood forecasting and operation is beneficial for flood control.

Investigating the application of Rasch theory in measuring change in middle school student performance in physical science

NASA Astrophysics Data System (ADS)

Cunningham, Jessica D.

Newton's Universe (NU), an innovative teacher training program, strives to obtain measures from rural, middle school science teachers and their students to determine the impact of its distance learning course on understanding of temperature. No consensus exists on the most appropriate and useful method of analysis to measure change in psychological constructs over time. Several item response theory (IRT) models have been deemed useful in measuring change, which makes the choice of an IRT model not obvious. The appropriateness and utility of each model, including a comparison to a traditional analysis of variance approach, was investigated using middle school science student performance on an assessment over an instructional period. Predetermined criteria were outlined to guide model selection based on several factors including research questions, data properties, and meaningful interpretations to determine the most appropriate model for this study. All methods employed in this study reiterated one common interpretation of the data -- specifically, that the students of teachers with any NU course experience had significantly greater gains in performance over the instructional period. However, clear distinctions were made between an analysis of variance and the racked and stacked analysis using the Rasch model. Although limited research exists examining the usefulness of the Rasch model in measuring change in understanding over time, this study applied these methods and detailed plausible implications for data-driven decisions based upon results for NU and others. Being mindful of the advantages and usefulness of each method of analysis may help others make informed decisions about choosing an appropriate model to depict changes to evaluate other programs. Results may encourage other researchers to consider the meaningfulness of using IRT for this purpose. Results have implications for data-driven decisions for future professional development courses, in science education and other disciplines. KEYWORDS: Item Response Theory, Rasch Model, Racking and Stacking, Measuring Change in Student Performance, Newton's Universe teacher training

A systematic review of health economic models of opioid agonist therapies in maintenance treatment of non-prescription opioid dependence.

PubMed

Chetty, Mersha; Kenworthy, James J; Langham, Sue; Walker, Andrew; Dunlop, William C N

2017-02-24

Opioid dependence is a chronic condition with substantial health, economic and social costs. The study objective was to conduct a systematic review of published health-economic models of opioid agonist therapy for non-prescription opioid dependence, to review the different modelling approaches identified, and to inform future modelling studies. Literature searches were conducted in March 2015 in eight electronic databases, supplemented by hand-searching reference lists and searches on six National Health Technology Assessment Agency websites. Studies were included if they: investigated populations that were dependent on non-prescription opioids and were receiving opioid agonist or maintenance therapy; compared any pharmacological maintenance intervention with any other maintenance regimen (including placebo or no treatment); and were health-economic models of any type. A total of 18 unique models were included. These used a range of modelling approaches, including Markov models (n = 4), decision tree with Monte Carlo simulations (n = 3), decision analysis (n = 3), dynamic transmission models (n = 3), decision tree (n = 1), cohort simulation (n = 1), Bayesian (n = 1), and Monte Carlo simulations (n = 2). Time horizons ranged from 6 months to lifetime. The most common evaluation was cost-utility analysis reporting cost per quality-adjusted life-year (n = 11), followed by cost-effectiveness analysis (n = 4), budget-impact analysis/cost comparison (n = 2) and cost-benefit analysis (n = 1). Most studies took the healthcare provider's perspective. Only a few models included some wider societal costs, such as productivity loss or costs of drug-related crime, disorder and antisocial behaviour. Costs to individuals and impacts on family and social networks were not included in any model. A relatively small number of studies of varying quality were found. Strengths and weaknesses relating to model structure, inputs and approach were identified across all the studies. There was no indication of a single standard emerging as a preferred approach. Most studies omitted societal costs, an important issue since the implications of drug abuse extend widely beyond healthcare services. Nevertheless, elements from previous models could together form a framework for future economic evaluations in opioid agonist therapy including all relevant costs and outcomes. This could more adequately support decision-making and policy development for treatment of non-prescription opioid dependence.

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

From complex questionnaire and interviewing data to intelligent Bayesian Network models for medical decision support

PubMed Central

Constantinou, Anthony Costa; Fenton, Norman; Marsh, William; Radlinski, Lukasz

2016-01-01

Objectives 1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; 2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; 3) To ensure the BN model can be used for interventional analysis; 4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. Method The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. Results When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. Conclusions This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way. PMID:26830286

From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support.

PubMed

Constantinou, Anthony Costa; Fenton, Norman; Marsh, William; Radlinski, Lukasz

2016-02-01

(1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; (2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; (3) To ensure the BN model can be used for interventional analysis; (4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way. Copyright © 2016 Elsevier B.V. All rights reserved.

Lessons Learned from the Frenchman Flat Flow and Transport Modeling External Peer Review

NASA Astrophysics Data System (ADS)

Becker, N. M.; Crowe, B. M.; Ruskauff, G.; Kwicklis, E. M.; Wilborn, B.

2011-12-01

The objective of the U.S. Department of Energy's Underground Test Area Program program is to forecast, using computer modeling, the contaminant boundary of radionuclide transport in groundwater at the Nevada National Security Site that exceeds the Safe Drinking Water Act after 1000 yrs. This objective is defined within the Federal Facilities Agreement and Consent Order between the Department of Energy, Department of Defense and State of Nevada Division of Environmental Protection . At one of the Corrective Action Units, Frenchman Flat, a Phase I flow and transport model underwent peer review in 1999 to determine if the model approach, assumptions and results adequate to be used as a decision tool as a basis to negotiate a compliance boundary with Nevada Division of Environmental Protection. The external peer review decision was that the model was not fully tested under a full suite of possible conceptual models, including boundary conditions, flow mechanisms, other transport processes, hydrological framework models, sensitivity and uncertainty analysis, etc. The program went back to collect more data, expand modeling to consider other alternatives that were not adequately tested, and conduct sensitivity and uncertainty analysis. A second external peer review was held in August 2010. Their conclusion that the new Frenchman Flat flow and transport modeling analysis were adequate as a decision tool and that the model was ready to advance to the next step in the Federal Facilities Agreement and Consent Order strategy. We will discuss the processes to changing the modeling that occurred between the first and second peer reviews, and then present the second peer review general comments. Finally, we present the lessons learned from the total model acceptance process required for federal regulatory compliance.

Real options analysis for land use management: Methods, application, and implications for policy.

PubMed

Regan, Courtney M; Bryan, Brett A; Connor, Jeffery D; Meyer, Wayne S; Ostendorf, Bertram; Zhu, Zili; Bao, Chenming

2015-09-15

Discounted cash flow analysis, including net present value is an established way to value land use and management investments which accounts for the time-value of money. However, it provides a static view and assumes passive commitment to an investment strategy when real world land use and management investment decisions are characterised by uncertainty, irreversibility, change, and adaptation. Real options analysis has been proposed as a better valuation method under uncertainty and where the opportunity exists to delay investment decisions, pending more information. We briefly review the use of discounted cash flow methods in land use and management and discuss their benefits and limitations. We then provide an overview of real options analysis, describe the main analytical methods, and summarize its application to land use investment decisions. Real options analysis is largely underutilized in evaluating land use decisions, despite uncertainty in policy and economic drivers, the irreversibility and sunk costs involved. New simulation methods offer the potential for overcoming current technical challenges to implementation as demonstrated with a real options simulation model used to evaluate an agricultural land use decision in South Australia. We conclude that considering option values in future policy design will provide a more realistic assessment of landholder investment decision making and provide insights for improved policy performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predictive models for subtypes of autism spectrum disorder based on single-nucleotide polymorphisms and magnetic resonance imaging.

PubMed

Jiao, Y; Chen, R; Ke, X; Cheng, L; Chu, K; Lu, Z; Herskovits, E H

2011-01-01

Autism spectrum disorder (ASD) is a neurodevelopmental disorder, of which Asperger syndrome and high-functioning autism are subtypes. Our goal is: 1) to determine whether a diagnostic model based on single-nucleotide polymorphisms (SNPs), brain regional thickness measurements, or brain regional volume measurements can distinguish Asperger syndrome from high-functioning autism; and 2) to compare the SNP, thickness, and volume-based diagnostic models. Our study included 18 children with ASD: 13 subjects with high-functioning autism and 5 subjects with Asperger syndrome. For each child, we obtained 25 SNPs for 8 ASD-related genes; we also computed regional cortical thicknesses and volumes for 66 brain structures, based on structural magnetic resonance (MR) examination. To generate diagnostic models, we employed five machine-learning techniques: decision stump, alternating decision trees, multi-class alternating decision trees, logistic model trees, and support vector machines. For SNP-based classification, three decision-tree-based models performed better than the other two machine-learning models. The performance metrics for three decision-tree-based models were similar: decision stump was modestly better than the other two methods, with accuracy = 90%, sensitivity = 0.95 and specificity = 0.75. All thickness and volume-based diagnostic models performed poorly. The SNP-based diagnostic models were superior to those based on thickness and volume. For SNP-based classification, rs878960 in GABRB3 (gamma-aminobutyric acid A receptor, beta 3) was selected by all tree-based models. Our analysis demonstrated that SNP-based classification was more accurate than morphometry-based classification in ASD subtype classification. Also, we found that one SNP--rs878960 in GABRB3--distinguishes Asperger syndrome from high-functioning autism.

Moral dilemmas and abortion decision-making: Lessons learnt from abortion research in England and Wales.

PubMed

Hoggart, Lesley

2018-05-21

This paper scrutinises the concepts of moral reasoning and personal reasoning, problematising the binary model by looking at young women's pregnancy decision-making. Data from two UK empirical studies are subjected to theoretically driven qualitative secondary analysis, and illustrative cases show how complex decision-making is characterised by an intertwining of the personal and the moral, and is thus best understood by drawing on moral relativism.

Balancing Information Analysis and Decision Value: A Model to Exploit the Decision Process

DTIC Science & Technology

2011-12-01

technical intelli- gence e.g. signals and sensors (SIGINT and MASINT), imagery (!MINT), as well and human and open source intelligence (HUMINT and OSINT ...Clark 2006). The ability to capture large amounts of da- ta and the plenitude of modem intelligence information sources provides a rich cache of...many tech- niques for managing information collected and derived from these sources , the exploitation of intelligence assets for decision-making

Handling a Small Dataset Problem in Prediction Model by employ Artificial Data Generation Approach: A Review

NASA Astrophysics Data System (ADS)

Lateh, Masitah Abdul; Kamilah Muda, Azah; Yusof, Zeratul Izzah Mohd; Azilah Muda, Noor; Sanusi Azmi, Mohd

2017-09-01

The emerging era of big data for past few years has led to large and complex data which needed faster and better decision making. However, the small dataset problems still arise in a certain area which causes analysis and decision are hard to make. In order to build a prediction model, a large sample is required as a training sample of the model. Small dataset is insufficient to produce an accurate prediction model. This paper will review an artificial data generation approach as one of the solution to solve the small dataset problem.

The information value of early career productivity in mathematics: a ROC analysis of prediction errors in bibliometricly informed decision making.

PubMed

Lindahl, Jonas; Danell, Rickard

The aim of this study was to provide a framework to evaluate bibliometric indicators as decision support tools from a decision making perspective and to examine the information value of early career publication rate as a predictor of future productivity. We used ROC analysis to evaluate a bibliometric indicator as a tool for binary decision making. The dataset consisted of 451 early career researchers in the mathematical sub-field of number theory. We investigated the effect of three different definitions of top performance groups-top 10, top 25, and top 50 %; the consequences of using different thresholds in the prediction models; and the added prediction value of information on early career research collaboration and publications in prestige journals. We conclude that early career performance productivity has an information value in all tested decision scenarios, but future performance is more predictable if the definition of a high performance group is more exclusive. Estimated optimal decision thresholds using the Youden index indicated that the top 10 % decision scenario should use 7 articles, the top 25 % scenario should use 7 articles, and the top 50 % should use 5 articles to minimize prediction errors. A comparative analysis between the decision thresholds provided by the Youden index which take consequences into consideration and a method commonly used in evaluative bibliometrics which do not take consequences into consideration when determining decision thresholds, indicated that differences are trivial for the top 25 and the 50 % groups. However, a statistically significant difference between the methods was found for the top 10 % group. Information on early career collaboration and publication strategies did not add any prediction value to the bibliometric indicator publication rate in any of the models. The key contributions of this research is the focus on consequences in terms of prediction errors and the notion of transforming uncertainty into risk when we are choosing decision thresholds in bibliometricly informed decision making. The significance of our results are discussed from the point of view of a science policy and management.

Developing a conceptual model for the application of patient and public involvement in the healthcare system in Iran.

PubMed

Azmal, Mohammad; Sari, Ali Akbari; Foroushani, Abbas Rahimi; Ahmadi, Batoul

2016-06-01

Patient and public involvement is engaging patients, providers, community representatives, and the public in healthcare planning and decision-making. The purpose of this study was to develop a model for the application of patient and public involvement in decision making in the Iranian healthcare system. A mixed qualitative-quantitative approach was used to develop a conceptual model. Thirty three key informants were purposely recruited in the qualitative stage, and 420 people (patients and their companions) were included in a protocol study that was implemented in five steps: 1) Identifying antecedents, consequences, and variables associated with the patient and the publics' involvement in healthcare decision making through a comprehensive literature review; 2) Determining the main variables in the context of Iran's health system using conceptual framework analysis; 3) Prioritizing and weighting variables by Shannon entropy; 4) designing and validating a tool for patient and public involvement in healthcare decision making; and 5) Providing a conceptual model of patient and the public involvement in planning and developing healthcare using structural equation modeling. We used various software programs, including SPSS (17), Max QDA (10), EXCEL, and LISREL. Content analysis, Shannon entropy, and descriptive and analytic statistics were used to analyze the data. In this study, seven antecedents variable, five dimensions of involvement, and six consequences were identified. These variables were used to design a valid tool. A logical model was derived that explained the logical relationships between antecedent and consequent variables and the dimensions of patient and public involvement as well. Given the specific context of the political, social, and innovative environments in Iran, it was necessary to design a model that would be compatible with these features. It can improve the quality of care and promote the patient and the public satisfaction with healthcare and legitimate the representative of people they served for. This model can provide a practical guide for managers and policy makers to involve people in making the decisions that influence their lives.

"Know What to Do If You Encounter a Flash Flood": Mental Models Analysis for Improving Flash Flood Risk Communication and Public Decision Making.

PubMed

Lazrus, Heather; Morss, Rebecca E; Demuth, Julie L; Lazo, Jeffrey K; Bostrom, Ann

2016-02-01

Understanding how people view flash flood risks can help improve risk communication, ultimately improving outcomes. This article analyzes data from 26 mental models interviews about flash floods with members of the public in Boulder, Colorado, to understand their perspectives on flash flood risks and mitigation. The analysis includes a comparison between public and professional perspectives by referencing a companion mental models study of Boulder-area professionals. A mental models approach can help to diagnose what people already know about flash flood risks and responses, as well as any critical gaps in their knowledge that might be addressed through improved risk communication. A few public interviewees mentioned most of the key concepts discussed by professionals as important for flash flood warning decision making. However, most interviewees exhibited some incomplete understandings and misconceptions about aspects of flash flood development and exposure, effects, or mitigation that may lead to ineffective warning decisions when a flash flood threatens. These include important misunderstandings about the rapid evolution of flash floods, the speed of water in flash floods, the locations and times that pose the greatest flash flood risk in Boulder, the value of situational awareness and environmental cues, and the most appropriate responses when a flash flood threatens. The findings point to recommendations for ways to improve risk communication, over the long term and when an event threatens, to help people quickly recognize and understand threats, obtain needed information, and make informed decisions in complex, rapidly evolving extreme weather events such as flash floods. © 2015 Society for Risk Analysis.

Dynamic decision making for dam-break emergency management - Part 1: Theoretical framework

NASA Astrophysics Data System (ADS)

Peng, M.; Zhang, L. M.

2013-02-01

An evacuation decision for dam breaks is a very serious issue. A late decision may lead to loss of lives and properties, but a very early evacuation will incur unnecessary expenses. This paper presents a risk-based framework of dynamic decision making for dam-break emergency management (DYDEM). The dam-break emergency management in both time scale and space scale is introduced first to define the dynamic decision problem. The probability of dam failure is taken as a stochastic process and estimated using a time-series analysis method. The flood consequences are taken as functions of warning time and evaluated with a human risk analysis model (HURAM) based on Bayesian networks. A decision criterion is suggested to decide whether to evacuate the population at risk (PAR) or to delay the decision. The optimum time for evacuating the PAR is obtained by minimizing the expected total loss, which integrates the time-related probabilities and flood consequences. When a delayed decision is chosen, the decision making can be updated with available new information. A specific dam-break case study is presented in a companion paper to illustrate the application of this framework to complex dam-breaching problems.

Comparing multi-criteria decision analysis and integrated assessment to support long-term water supply planning

PubMed Central

Maurer, Max; Lienert, Judit

2017-01-01

We compare the use of multi-criteria decision analysis (MCDA)–or more precisely, models used in multi-attribute value theory (MAVT)–to integrated assessment (IA) models for supporting long-term water supply planning in a small town case study in Switzerland. They are used to evaluate thirteen system scale water supply alternatives in four future scenarios regarding forty-four objectives, covering technical, social, environmental, and economic aspects. The alternatives encompass both conventional and unconventional solutions and differ regarding technical, spatial and organizational characteristics. This paper focuses on the impact assessment and final evaluation step of the structured MCDA decision support process. We analyze the performance of the alternatives for ten stakeholders. We demonstrate the implications of model assumptions by comparing two IA and three MAVT evaluation model layouts of different complexity. For this comparison, we focus on the validity (ranking stability), desirability (value), and distinguishability (value range) of the alternatives given the five model layouts. These layouts exclude or include stakeholder preferences and uncertainties. Even though all five led us to identify the same best alternatives, they did not produce identical rankings. We found that the MAVT-type models provide higher distinguishability and a more robust basis for discussion than the IA-type models. The needed complexity of the model, however, should be determined based on the intended use of the model within the decision support process. The best-performing alternatives had consistently strong performance for all stakeholders and future scenarios, whereas the current water supply system was outperformed in all evaluation layouts. The best-performing alternatives comprise proactive pipe rehabilitation, adapted firefighting provisions, and decentralized water storage and/or treatment. We present recommendations for possible ways of improving water supply planning in the case study and beyond. PMID:28481881

Large-Scale Transport Model Uncertainty and Sensitivity Analysis: Distributed Sources in Complex Hydrogeologic Systems

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

Sig Drellack, Lance Prothro

2007-12-01

The Underground Test Area (UGTA) Project of the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office is in the process of assessing and developing regulatory decision options based on modeling predictions of contaminant transport from underground testing of nuclear weapons at the Nevada Test Site (NTS). The UGTA Project is attempting to develop an effective modeling strategy that addresses and quantifies multiple components of uncertainty including natural variability, parameter uncertainty, conceptual/model uncertainty, and decision uncertainty in translating model results into regulatory requirements. The modeling task presents multiple unique challenges to the hydrological sciences as a result ofmore » the complex fractured and faulted hydrostratigraphy, the distributed locations of sources, the suite of reactive and non-reactive radionuclides, and uncertainty in conceptual models. Characterization of the hydrogeologic system is difficult and expensive because of deep groundwater in the arid desert setting and the large spatial setting of the NTS. Therefore, conceptual model uncertainty is partially addressed through the development of multiple alternative conceptual models of the hydrostratigraphic framework and multiple alternative models of recharge and discharge. Uncertainty in boundary conditions is assessed through development of alternative groundwater fluxes through multiple simulations using the regional groundwater flow model. Calibration of alternative models to heads and measured or inferred fluxes has not proven to provide clear measures of model quality. Therefore, model screening by comparison to independently-derived natural geochemical mixing targets through cluster analysis has also been invoked to evaluate differences between alternative conceptual models. Advancing multiple alternative flow models, sensitivity of transport predictions to parameter uncertainty is assessed through Monte Carlo simulations. The simulations are challenged by the distributed sources in each of the Corrective Action Units, by complex mass transfer processes, and by the size and complexity of the field-scale flow models. An efficient methodology utilizing particle tracking results and convolution integrals provides in situ concentrations appropriate for Monte Carlo analysis. Uncertainty in source releases and transport parameters including effective porosity, fracture apertures and spacing, matrix diffusion coefficients, sorption coefficients, and colloid load and mobility are considered. With the distributions of input uncertainties and output plume volumes, global analysis methods including stepwise regression, contingency table analysis, and classification tree analysis are used to develop sensitivity rankings of parameter uncertainties for each model considered, thus assisting a variety of decisions.« less

[Application of decision curve on evaluation of MRI predictive model for early assessing pathological complete response to neoadjuvant therapy in breast cancer].

PubMed

He, Y J; Li, X T; Fan, Z Q; Li, Y L; Cao, K; Sun, Y S; Ouyang, T

2018-01-23

Objective: To construct a dynamic enhanced MR based predictive model for early assessing pathological complete response (pCR) to neoadjuvant therapy in breast cancer, and to evaluate the clinical benefit of the model by using decision curve. Methods: From December 2005 to December 2007, 170 patients with breast cancer treated with neoadjuvant therapy were identified and their MR images before neoadjuvant therapy and at the end of the first cycle of neoadjuvant therapy were collected. Logistic regression model was used to detect independent factors for predicting pCR and construct the predictive model accordingly, then receiver operating characteristic (ROC) curve and decision curve were used to evaluate the predictive model. Results: ΔArea(max) and Δslope(max) were independent predictive factors for pCR, OR =0.942 (95% CI : 0.918-0.967) and 0.961 (95% CI : 0.940-0.987), respectively. The area under ROC curve (AUC) for the constructed model was 0.886 (95% CI : 0.820-0.951). Decision curve showed that in the range of the threshold probability above 0.4, the predictive model presented increased net benefit as the threshold probability increased. Conclusions: The constructed predictive model for pCR is of potential clinical value, with an AUC>0.85. Meanwhile, decision curve analysis indicates the constructed predictive model has net benefit from 3 to 8 percent in the likely range of probability threshold from 80% to 90%.

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

Decision support system in an international-voice-services business company

NASA Astrophysics Data System (ADS)

Hadianti, R.; Uttunggadewa, S.; Syamsuddin, M.; Soewono, E.

2017-01-01

We consider a problem facing by an international telecommunication services company in maximizing its profit. From voice services by controlling cost and business partnership. The competitiveness in this industry is very high, so that any efficiency from controlling cost and business partnership can help the company to survive in the very high competitiveness situation. The company trades voice traffic with a large number of business partners. There are four trading schemes that can be chosen by this company, namely, flat rate, class tiering, volume commitment, and revenue capped. Each scheme has a specific characteristic on the rate and volume deal, where the last three schemes are regarded as strategic schemes to be offered to business partner to ensure incoming traffic volume for both parties. This company and each business partner need to choose an optimal agreement in a certain period of time that can maximize the company’s profit. In this agreement, both parties agree to use a certain trading scheme, rate and rate/volume/revenue deal. A decision support system is then needed in order to give a comprehensive information to the sales officers to deal with the business partners. This paper discusses the mathematical model of the optimal decision for incoming traffic volume control, which is a part of the analysis needed to build the decision support system. The mathematical model is built by first performing data analysis to see how elastic the incoming traffic volume is. As the level of elasticity is obtained, we then derive a mathematical modelling that can simulate the impact of any decision on trading to the revenue of the company. The optimal decision can be obtained from these simulations results. To evaluate the performance of the proposed method we implement our decision model to the historical data. A software tool incorporating our methodology is currently in construction.

A qualitative analysis of parental decision making for childhood immunisation.

PubMed

Marshall, S; Swerissen, H

1999-10-01

Achieving high rates of childhood immunisation is an important public health aim. Currently, however, immunisation uptake in Australia is disappointing. This qualitative study investigated the factors that influence parental decision making for childhood immunisation, and whether parents' experiences were better conceptualised in terms of static subjective expected utility models or in terms of a more dynamic process. Semi-structured in-depth interviews were conducted with 20 predominantly middle-class mothers--17 immunizers and three non-immunizers, in Melbourne, Victoria, in 1997. The data were then examined using thematic analysis. The results suggested that for these participants the decision regarding childhood immunization was better conceptualized as a dynamic process. The decision required initial consideration, implementation then maintenance. If a better understanding of immunization decision making is to be achieved, future studies must look beyond static frameworks. Clearer insight into the dynamic nature of immunization decision making should assist in the identification of more effective methods of promoting childhood immunization to groups at risk of non-compliance.

Dynamics of Entropy in Quantum-like Model of Decision Making

NASA Astrophysics Data System (ADS)

Basieva, Irina; Khrennikov, Andrei; Asano, Masanari; Ohya, Masanori; Tanaka, Yoshiharu

2011-03-01

We present a quantum-like model of decision making in games of the Prisoner's Dilemma type. By this model the brain processes information by using representation of mental states in complex Hilbert space. Driven by the master equation the mental state of a player, say Alice, approaches an equilibrium point in the space of density matrices. By using this equilibrium point Alice determines her mixed (i.e., probabilistic) strategy with respect to Bob. Thus our model is a model of thinking through decoherence of initially pure mental state. Decoherence is induced by interaction with memory and external environment. In this paper we study (numerically) dynamics of quantum entropy of Alice's state in the process of decision making. Our analysis demonstrates that this dynamics depends nontrivially on the initial state of Alice's mind on her own actions and her prediction state (for possible actions of Bob.)

Note on Professor Sizer's Paper.

ERIC Educational Resources Information Center

Balderston, Frederick E.

1979-01-01

Issues suggested by John Sizer's paper, an overview of the assessment of institutional performance, include: the efficient-frontier approach, multiple-criterion decision-making models, performance analysis approached as path analysis, and assessment of academic quality. (JMD)

Risk Decision Making Model for Reservoir Floodwater resources Utilization

NASA Astrophysics Data System (ADS)

Huang, X.

2017-12-01

Floodwater resources utilization(FRU) can alleviate the shortage of water resources, but there are risks. In order to safely and efficiently utilize the floodwater resources, it is necessary to study the risk of reservoir FRU. In this paper, the risk rate of exceeding the design flood water level and the risk rate of exceeding safety discharge are estimated. Based on the principle of the minimum risk and the maximum benefit of FRU, a multi-objective risk decision making model for FRU is constructed. Probability theory and mathematical statistics method is selected to calculate the risk rate; C-D production function method and emergy analysis method is selected to calculate the risk benefit; the risk loss is related to flood inundation area and unit area loss; the multi-objective decision making problem of the model is solved by the constraint method. Taking the Shilianghe reservoir in Jiangsu Province as an example, the optimal equilibrium solution of FRU of the Shilianghe reservoir is found by using the risk decision making model, and the validity and applicability of the model are verified.

Preference, resistance to change, and the cumulative decision model.

PubMed

Grace, Randolph C

2018-01-01

According to behavioral momentum theory (Nevin & Grace, 2000a), preference in concurrent chains and resistance to change in multiple schedules are independent measures of a common construct representing reinforcement history. Here I review the original studies on preference and resistance to change in which reinforcement variables were manipulated parametrically, conducted by Nevin, Grace and colleagues between 1997 and 2002, as well as more recent research. The cumulative decision model proposed by Grace and colleagues for concurrent chains is shown to provide a good account of both preference and resistance to change, and is able to predict the increased sensitivity to reinforcer rate and magnitude observed with constant-duration components. Residuals from fits of the cumulative decision model to preference and resistance to change data were positively correlated, supporting the prediction of behavioral momentum theory. Although some questions remain, the learning process assumed by the cumulative decision model, in which outcomes are compared against a criterion that represents the average outcome value in the current context, may provide a plausible model for the acquisition of differential resistance to change. © 2018 Society for the Experimental Analysis of Behavior.

Efficient Workflows for Curation of Heterogeneous Data Supporting Modeling of U-Nb Alloy Aging

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

Ward, Logan Timothy; Hackenberg, Robert Errol

These are slides from a presentation summarizing a graduate research associate's summer project. The following topics are covered in these slides: data challenges in materials, aging in U-Nb Alloys, Building an Aging Model, Different Phase Trans. in U-Nb, the Challenge, Storing Materials Data, Example Data Source, Organizing Data: What is a Schema?, What does a "XML Schema" look like?, Our Data Schema: Nice and Simple, Storing Data: Materials Data Curation System (MDCS), Problem with MDCS: Slow Data Entry, Getting Literature into MDCS, Staging Data in Excel Document, Final Result: MDCS Records, Analyzing Image Data, Process for Making TTT Diagram, Bottleneckmore » Number 1: Image Analysis, Fitting a TTP Boundary, Fitting a TTP Curve: Comparable Results, How Does it Compare to Our Data?, Image Analysis Workflow, Curating Hardness Records, Hardness Data: Two Key Decisions, Before Peak Age? - Automation, Interactive Viz, Which Transformation?, Microstructure-Informed Model, Tracking the Entire Process, General Problem with Property Models, Pinyon: Toolkit for Managing Model Creation, Tracking Individual Decisions, Jupyter: Docs and Code in One File, Hardness Analysis Workflow, Workflow for Aging Models, and conclusions.« less

The management of patients with T1 adenocarcinoma of the low rectum: a decision analysis.

PubMed

Johnston, Calvin F; Tomlinson, George; Temple, Larissa K; Baxter, Nancy N

2013-04-01

Decision making for patients with T1 adenocarcinoma of the low rectum, when treatment options are limited to a transanal local excision or abdominoperineal resection, is challenging. The aim of this study was to develop a contemporary decision analysis to assist patients and clinicians in balancing the goals of maximizing life expectancy and quality of life in this situation. We constructed a Markov-type microsimulation in open-source software. Recurrence rates and quality-of-life parameters were elicited by systematic literature reviews. Sensitivity analyses were performed on key model parameters. Our base case for analysis was a 65-year-old man with low-lying T1N0 rectal cancer. We determined the sensitivity of our model for sex, age up to 80, and T stage. The main outcome measured was quality-adjusted life-years. In the base case, selecting transanal local excision over abdominoperineal resection resulted in a loss of 0.53 years of life expectancy but a gain of 0.97 quality-adjusted life-years. One-way sensitivity analysis demonstrated a health state utility value threshold for permanent colostomy of 0.93. This value ranged from 0.88 to 1.0 based on tumor recurrence risk. There were no other model sensitivities. Some model parameter estimates were based on weak data. In our model, transanal local excision was found to be the preferable approach for most patients. An abdominoperineal resection has a 3.5% longer life expectancy, but this advantage is lost when the quality-of-life reduction reported by stoma patients is weighed in. The minority group in whom abdominoperineal resection is preferred are those who are unwilling to sacrifice 7% of their life expectancy to avoid a permanent stoma. This is estimated to be approximately 25% of all patients. The threshold increases to 12% of life expectancy in high-risk tumors. No other factors are found to be relevant to the decision.

Overrepresentation of extreme events in decision making reflects rational use of cognitive resources.

PubMed

Lieder, Falk; Griffiths, Thomas L; Hsu, Ming

2018-01-01

People's decisions and judgments are disproportionately swayed by improbable but extreme eventualities, such as terrorism, that come to mind easily. This article explores whether such availability biases can be reconciled with rational information processing by taking into account the fact that decision makers value their time and have limited cognitive resources. Our analysis suggests that to make optimal use of their finite time decision makers should overrepresent the most important potential consequences relative to less important, put potentially more probable, outcomes. To evaluate this account, we derive and test a model we call utility-weighted sampling. Utility-weighted sampling estimates the expected utility of potential actions by simulating their outcomes. Critically, outcomes with more extreme utilities have a higher probability of being simulated. We demonstrate that this model can explain not only people's availability bias in judging the frequency of extreme events but also a wide range of cognitive biases in decisions from experience, decisions from description, and memory recall. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

GIS-Based Suitability Model for Assessment of Forest Biomass Energy Potential in a Region of Portugal

NASA Astrophysics Data System (ADS)

Quinta-Nova, Luis; Fernandez, Paulo; Pedro, Nuno

2017-12-01

This work focuses on developed a decision support system based on multicriteria spatial analysis to assess the potential for generation of biomass residues from forestry sources in a region of Portugal (Beira Baixa). A set of environmental, economic and social criteria was defined, evaluated and weighted in the context of Saaty’s analytic hierarchies. The best alternatives were obtained after applying Analytic Hierarchy Process (AHP). The model was applied to the central region of Portugal where forest and agriculture are the most representative land uses. Finally, sensitivity analysis of the set of factors and their associated weights was performed to test the robustness of the model. The proposed evaluation model provides a valuable reference for decision makers in establishing a standardized means of selecting the optimal location for new biomass plants.

Sensor fusion IV: Control paradigms and data structures; Proceedings of the Meeting, Boston, MA, Nov. 12-15, 1991

NASA Technical Reports Server (NTRS)

Schenker, Paul S. (Editor)

1992-01-01

Various papers on control paradigms and data structures in sensor fusion are presented. The general topics addressed include: decision models and computational methods, sensor modeling and data representation, active sensing strategies, geometric planning and visualization, task-driven sensing, motion analysis, models motivated biology and psychology, decentralized detection and distributed decision, data fusion architectures, robust estimation of shapes and features, application and implementation. Some of the individual subjects considered are: the Firefly experiment on neural networks for distributed sensor data fusion, manifold traversing as a model for learning control of autonomous robots, choice of coordinate systems for multiple sensor fusion, continuous motion using task-directed stereo vision, interactive and cooperative sensing and control for advanced teleoperation, knowledge-based imaging for terrain analysis, physical and digital simulations for IVA robotics.

Exploring alternate specifications to explain agency-level effects in placement decisions regarding Aboriginal children: Further analysis of the Canadian Incidence Study of Reported Child Abuse and Neglect Part C.

PubMed

Fallon, Barbara; Chabot, Martin; Fluke, John; Blackstock, Cindy; Sinha, Vandna; Allan, Kate; MacLaurin, Bruce

2015-11-01

A series of papers using data from the Canadian Incidence Study of Reported Child Abuse and Neglect (CIS) explored the influence of clinical and organizational characteristics on the decision to place Aboriginal children in out-of-home placements at the conclusion of child maltreatment investigations. The purpose of this paper is to further explore a consistent finding of the previous analyses: the proportion of investigations involving Aboriginal children at a child welfare agency is associated with placement for all children in that agency. CIS-2008 data were used in the analysis, which allowed for inclusion of previously unavailable organizational and contextual variables. Multi-level statistical models were developed to analyze the influence of clinical and organizational variables on the placement decision. Final models revealed that the proportion of investigations conducted by the child welfare agency involving Aboriginal children was again a key agency-level predictor of the placement decision for any child served by the agency. Specifically, the higher the proportion of investigations of Aboriginal children, the more likely placement was to occur for any child. Further, this analysis demonstrated that structure of governance, an organizational-level variable not available in previous cycles of the CIS, is an important agency-level predictor of out-of-home placement. Further analysis is needed to fully understand individual and organizational level variables that may influence decisions regarding placement of Aboriginal children. Copyright © 2015 Elsevier Ltd. All rights reserved.

A decision analysis approach for risk management of near-earth objects

NASA Astrophysics Data System (ADS)

Lee, Robert C.; Jones, Thomas D.; Chapman, Clark R.

2014-10-01

Risk management of near-Earth objects (NEOs; e.g., asteroids and comets) that can potentially impact Earth is an important issue that took on added urgency with the Chelyabinsk event of February 2013. Thousands of NEOs large enough to cause substantial damage are known to exist, although only a small fraction of these have the potential to impact Earth in the next few centuries. The probability and location of a NEO impact are subject to complex physics and great uncertainty, and consequences can range from minimal to devastating, depending upon the size of the NEO and location of impact. Deflecting a potential NEO impactor would be complex and expensive, and inter-agency and international cooperation would be necessary. Such deflection campaigns may be risky in themselves, and mission failure may result in unintended consequences. The benefits, risks, and costs of different potential NEO risk management strategies have not been compared in a systematic fashion. We present a decision analysis framework addressing this hazard. Decision analysis is the science of informing difficult decisions. It is inherently multi-disciplinary, especially with regard to managing catastrophic risks. Note that risk analysis clarifies the nature and magnitude of risks, whereas decision analysis guides rational risk management. Decision analysis can be used to inform strategic, policy, or resource allocation decisions. First, a problem is defined, including the decision situation and context. Second, objectives are defined, based upon what the different decision-makers and stakeholders (i.e., participants in the decision) value as important. Third, quantitative measures or scales for the objectives are determined. Fourth, alternative choices or strategies are defined. Fifth, the problem is then quantitatively modeled, including probabilistic risk analysis, and the alternatives are ranked in terms of how well they satisfy the objectives. Sixth, sensitivity analyses are performed in order to examine the impact of uncertainties. Finally, the need for further analysis, data collection, or refinement is determined. The first steps of defining the problem and the objectives are critical to constructing an informative decision analysis. Such steps must be undertaken with participation from experts, decision-makers, and stakeholders (defined here as "decision participants"). The basic problem here can be framed as: “What is the best strategy to manage risk associated with NEOs?” Some high-level objectives might be to minimize: mortality and injuries, damage to critical infrastructure (e.g., power, communications and food distribution), ecosystem damage, property damage, ungrounded media and public speculation, resources expended, and overall cost. Another valuable objective would be to maximize inter-agency/government coordination. Some of these objectives (e.g., “minimize mortality”) are readily quantified (e.g., deaths and injuries averted). Others are less so (e.g., “maximize inter-agency/government coordination”), but these can be scaled. Objectives may be inversely related: e.g., a strategy that minimizes mortality may cost more. They are also unlikely to be weighted equally. Defining objectives and assessing their relative weight and interactions requires early engagement with decision participants. High-level decisions include whether to deflect a NEO, when to deflect, what is the best alternative for deflection/destruction, and disaster management strategies if an impact occurs. Important influences include, for example: NEO characteristics (orbital characteristics, diameter, mass, spin and composition), impact probability and location, interval between discovery and projected impact date, interval between discovery and deflection target date, costs of information collection, costs and technological feasibility of deflection alternatives, risks of deflection campaigns, requirements for inter-agency and international cooperation, and timing of informing the public. The analytical aspects of decision analysis center on estimation of the expected value (i.e. utility) of different alternatives. The expected value of an alternative is a function of the probability-weighted consequences, estimated using Bayesian calculations in a decision tree or influence diagram model. The result is a set of expected-value estimates for all alternatives evaluated that enables a ranking; the higher the expected value, the more preferred the alternative. A common way to include resource limitations is by framing the decision analysis in the context of economics (e.g., cost-effectiveness analysis). An important aspect of decision analysis in the NEO risk management case is the ability, known as sensitivity analysis, to examine the effect of parameter uncertainty upon decisions. The simplest way to evaluate uncertainty associated with the information used in a decision analysis is to adjust the input values one at a time (or simultaneously) to examine how the results change. Monte Carlo simulations can be used to adjust the inputs over ranges or distributions of values; statistical means then are used to determine the most influential variables. These techniques yield a measure known as the expected value of imperfect information. This value is highly informative, because it allows the decision-maker with imperfect information to evaluate the impact of using experiments, tests, or data collection (e.g. Earth-based observations, space-based remote sensing, etc.) to refine judgments; and indeed to estimate how much should be spent to reduce uncertainty.

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.

Naturalistic Decision Making in Power Grid Operations: Implications for Dispatcher Training and Usability Testing

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

Greitzer, Frank L.; Podmore, Robin

2008-11-17

The focus of the present study is on improved training approaches to accelerate learning and improved methods for analyzing effectiveness of tools within a high-fidelity power grid simulated environment. A theory-based model has been developed to document and understand the mental processes that an expert power system operator uses when making critical decisions. The theoretical foundation for the method is based on the concepts of situation awareness, the methods of cognitive task analysis, and the naturalistic decision making (NDM) approach of Recognition Primed Decision Making. The method has been systematically explored and refined as part of a capability demonstration ofmore » a high-fidelity real-time power system simulator under normal and emergency conditions. To examine NDM processes, we analyzed transcripts of operator-to-operator conversations during the simulated scenario to reveal and assess NDM-based performance criteria. The results of the analysis indicate that the proposed framework can be used constructively to map or assess the Situation Awareness Level of the operators at each point in the scenario. We can also identify the mental models and mental simulations that the operators employ at different points in the scenario. This report documents the method, describes elements of the model, and provides appendices that document the simulation scenario and the associated mental models used by operators in the scenario.« less

An Agent-Based Model of Farmer Decision Making in Jordan

NASA Astrophysics Data System (ADS)

Selby, Philip; Medellin-Azuara, Josue; Harou, Julien; Klassert, Christian; Yoon, Jim

2016-04-01

We describe an agent based hydro-economic model of groundwater irrigated agriculture in the Jordan Highlands. The model employs a Multi-Agent-Simulation (MAS) framework and is designed to evaluate direct and indirect outcomes of climate change scenarios and policy interventions on farmer decision making, including annual land use, groundwater use for irrigation, and water sales to a water tanker market. Land use and water use decisions are simulated for groups of farms grouped by location and their behavioural and economic similarities. Decreasing groundwater levels, and the associated increase in pumping costs, are important drivers for change within Jordan'S agricultural sector. We describe how this is considered by coupling of agricultural and groundwater models. The agricultural production model employs Positive Mathematical Programming (PMP), a method for calibrating agricultural production functions to observed planted areas. PMP has successfully been used with disaggregate models for policy analysis. We adapt the PMP approach to allow explicit evaluation of the impact of pumping costs, groundwater purchase fees and a water tanker market. The work demonstrates the applicability of agent-based agricultural decision making assessment in the Jordan Highlands and its integration with agricultural model calibration methods. The proposed approach is designed and implemented with software such that it could be used to evaluate a variety of physical and human influences on decision making in agricultural water management.

Markov chain decision model for urinary incontinence procedures.

PubMed

Kumar, Sameer; Ghildayal, Nidhi; Ghildayal, Neha

2017-03-13

Purpose Urinary incontinence (UI) is a common chronic health condition, a problem specifically among elderly women that impacts quality of life negatively. However, UI is usually viewed as likely result of old age, and as such is generally not evaluated or even managed appropriately. Many treatments are available to manage incontinence, such as bladder training and numerous surgical procedures such as Burch colposuspension and Sling for UI which have high success rates. The purpose of this paper is to analyze which of these popular surgical procedures for UI is effective. Design/methodology/approach This research employs randomized, prospective studies to obtain robust cost and utility data used in the Markov chain decision model for examining which of these surgical interventions is more effective in treating women with stress UI based on two measures: number of quality adjusted life years (QALY) and cost per QALY. Treeage Pro Healthcare software was employed in Markov decision analysis. Findings Results showed the Sling procedure is a more effective surgical intervention than the Burch. However, if a utility greater than certain utility value, for which both procedures are equally effective, is assigned to persistent incontinence, the Burch procedure is more effective than the Sling procedure. Originality/value This paper demonstrates the efficacy of a Markov chain decision modeling approach to study the comparative effectiveness analysis of available treatments for patients with UI, an important public health issue, widely prevalent among elderly women in developed and developing countries. This research also improves upon other analyses using a Markov chain decision modeling process to analyze various strategies for treating UI.

Revisiting the evidence for collapsing boundaries and urgency signals in perceptual decision-making.

PubMed

Hawkins, Guy E; Forstmann, Birte U; Wagenmakers, Eric-Jan; Ratcliff, Roger; Brown, Scott D

2015-02-11

For nearly 50 years, the dominant account of decision-making holds that noisy information is accumulated until a fixed threshold is crossed. This account has been tested extensively against behavioral and neurophysiological data for decisions about consumer goods, perceptual stimuli, eyewitness testimony, memories, and dozens of other paradigms, with no systematic misfit between model and data. Recently, the standard model has been challenged by alternative accounts that assume that less evidence is required to trigger a decision as time passes. Such "collapsing boundaries" or "urgency signals" have gained popularity in some theoretical accounts of neurophysiology. Nevertheless, evidence in favor of these models is mixed, with support coming from only a narrow range of decision paradigms compared with a long history of support from dozens of paradigms for the standard theory. We conducted the first large-scale analysis of data from humans and nonhuman primates across three distinct paradigms using powerful model-selection methods to compare evidence for fixed versus collapsing bounds. Overall, we identified evidence in favor of the standard model with fixed decision boundaries. We further found that evidence for static or dynamic response boundaries may depend on specific paradigms or procedures, such as the extent of task practice. We conclude that the difficulty of selecting between collapsing and fixed bounds models has received insufficient attention in previous research, calling into question some previous results. Copyright © 2015 the authors 0270-6474/15/352476-09$15.00/0.

Human/Automation Trade Methodology for the Moon, Mars and Beyond

NASA Technical Reports Server (NTRS)

Korsmeyer, David J.

2009-01-01

It is possible to create a consistent trade methodology that can characterize operations model alternatives for crewed exploration missions. For example, a trade-space that is organized around the objective of maximizing Crew Exploration Vehicle (CEV) independence would have the input as a classification of the category of analysis to be conducted or decision to be made, and a commitment to a detailed point in a mission profile during which the analysis or decision is to be made. For example, does the decision have to do with crew activity planning, or life support? Is the mission phase trans-Earth injection, cruise, or lunar descent? Different kinds of decision analysis of the trade-space between human and automated decisions will occurs at different points in a mission's profile. The necessary objectives at a given point in time during a mission will call for different kinds of response with respect to where and how computers and automation are expected to help provide an accurate, safe, and timely response. In this paper, a consistent methodology for assessing the trades between human and automated decisions on-board will be presented and various examples discussed.

"Setting up for the Next Big Thing": Undergraduate Women Engineering Students' Postbaccalaureate Career Decisions

ERIC Educational Resources Information Center

Smith, Kathleen N.; Gayles, Joy Gaston

2017-01-01

Using social cognitive career theory and the cognitive information processing model as frameworks, in this constructivist case study we examined the career-related experiences and decisions of 10 women engineering undergraduate seniors who accepted full-time positions. From the data analysis 3 major themes emerged: critical undergraduate…

Antecedents and Consequences of Retirement Planning and Decision-Making: A Meta-Analysis and Model

ERIC Educational Resources Information Center

Topa, Gabriela; Moriano, Juan Antonio; Depolo, Marco; Alcover, Carlos-Maria; Morales, J. Francisco

2009-01-01

In this study, meta-analytic procedures were used to examine the relationships between retirement planning, retirement decision and their antecedent and consequences. Our review of the literature generated 341 independent samples obtained from 99 primary studies with 188,222 participants. A small effect size (ES) for antecedents of retirement…

Analysis of Wastewater and Water System Renewal Decision-Making Tools and Approaches

EPA Science Inventory

In regards to the development of software for decision support for pipeline renewal, most of the attention to date has been paid to the development of asset management models which help an owner decide on which portions of a system to prioritize for needed actions. There has not ...

Family Decision Making: Benefits to Persons with Developmental Disabilities and Their Family Members

ERIC Educational Resources Information Center

Neely-Barnes, Susan; Graff, J. Carolyn; Marcenko, Maureen; Weber, Lisa

2008-01-01

Family involvement in planning and choosing services has become a key intervention concept in developmental disability services. This study (N = 547) modeled patterns of family decision making and assessed benefits to persons with developmental disabilities (DDs) and their family members. A latent profile analysis identified 4 classes that were…

An Analysis of the EPA Report on Pipeline Renewal Decision Making Tools and Approaches

EPA Science Inventory

Few DSS are commercially available for technology selection as most utilities make decisions based on in-house and consultant expertise (Matthews et al., 2011). This review presents some of the models proposed over the past 15 years for selecting technologies in the U.S. and wor...

The quandaries and promise of risk management: a scientist's perspective on integration of science and management.

Treesearch

B.G. Marcot

2007-01-01

This paper briefly lists constraints and problems of traditional approaches to natural resource risk analysis and risk management. Such problems include disparate definitions of risk, multiple and conflicting objectives and decisions, conflicting interpretations of uncertainty, and failure of articulating decision criteria, risk attitudes, modeling assumptions, and...

Optimal decision-making in mammals: insights from a robot study of rodent texture discrimination

PubMed Central

Lepora, Nathan F.; Fox, Charles W.; Evans, Mathew H.; Diamond, Mathew E.; Gurney, Kevin; Prescott, Tony J.

2012-01-01

Texture perception is studied here in a physical model of the rat whisker system consisting of a robot equipped with a biomimetic vibrissal sensor. Investigations of whisker motion in rodents have led to several explanations for texture discrimination, such as resonance or stick-slips. Meanwhile, electrophysiological studies of decision-making in monkeys have suggested a neural mechanism of evidence accumulation to threshold for competing percepts, described by a probabilistic model of Bayesian sequential analysis. For our robot whisker data, we find that variable reaction-time decision-making with sequential analysis performs better than the fixed response-time maximum-likelihood estimation. These probabilistic classifiers also use whatever available features of the whisker signals aid the discrimination, giving improved performance over a single-feature strategy, such as matching the peak power spectra of whisker vibrations. These results cast new light on how the various proposals for texture discrimination in rodents depend on the whisker contact mechanics and suggest the possibility of a common account of decision-making across mammalian species. PMID:22279155

Cost-effectiveness on a local level: whether and when to adopt a new technology.

PubMed

Woertman, Willem H; Van De Wetering, Gijs; Adang, Eddy M M

2014-04-01

Cost-effectiveness analysis has become a widely accepted tool for decision making in health care. The standard textbook cost-effectiveness analysis focuses on whether to make the switch from an old or common practice technology to an innovative technology, and in doing so, it takes a global perspective. In this article, we are interested in a local perspective, and we look at the questions of whether and when the switch from old to new should be made. A new approach to cost-effectiveness from a local (e.g., a hospital) perspective, by means of a mathematical model for cost-effectiveness that explicitly incorporates time, is proposed. A decision rule is derived for establishing whether a new technology should be adopted, as well as a general rule for establishing when it pays to postpone adoption by 1 more period, and a set of decision rules that can be used to determine the optimal timing of adoption. Finally, a simple example is presented to illustrate our model and how it leads to optimal decision making in a number of cases.

Weighing Clinical Evidence Using Patient Preferences: An Application of Probabilistic Multi-Criteria Decision Analysis.

PubMed

Broekhuizen, Henk; IJzerman, Maarten J; Hauber, A Brett; Groothuis-Oudshoorn, Catharina G M

2017-03-01

The need for patient engagement has been recognized by regulatory agencies, but there is no consensus about how to operationalize this. One approach is the formal elicitation and use of patient preferences for weighing clinical outcomes. The aim of this study was to demonstrate how patient preferences can be used to weigh clinical outcomes when both preferences and clinical outcomes are uncertain by applying a probabilistic value-based multi-criteria decision analysis (MCDA) method. Probability distributions were used to model random variation and parameter uncertainty in preferences, and parameter uncertainty in clinical outcomes. The posterior value distributions and rank probabilities for each treatment were obtained using Monte-Carlo simulations. The probability of achieving the first rank is the probability that a treatment represents the highest value to patients. We illustrated our methodology for a simplified case on six HIV treatments. Preferences were modeled with normal distributions and clinical outcomes were modeled with beta distributions. The treatment value distributions showed the rank order of treatments according to patients and illustrate the remaining decision uncertainty. This study demonstrated how patient preference data can be used to weigh clinical evidence using MCDA. The model takes into account uncertainty in preferences and clinical outcomes. The model can support decision makers during the aggregation step of the MCDA process and provides a first step toward preference-based personalized medicine, yet requires further testing regarding its appropriate use in real-world settings.

Exploratory and Confirmatory Factor Analysis of the Career Decision-Making Difficulties Questionnaire

PubMed Central

Farrokhi, Farahman; Mahdavi, Ali; Moradi, Samad

2012-01-01

Objective The present study aimed at validating the structure of Career Decision-making Difficulties Questionnaire (CDDQ). Methods Five hundred and eleven undergraduate students took part in this research; from these participants, 63 males and 200 females took part in the first study, and 63 males and 185 females completed the survey for the second study. Results The results of exploratory factor analysis (EFA) indicated strong support for the three-factor structure, consisting of lack of information about the self, inconsistent information, lack of information and lack of readiness factors. A confirmatory factor analysis was run with the second sample using structural equation modeling. As expected, the three-factor solution provided a better fit to the data than the alternative models. Conclusion CDDQ was recommended to be used for college students in this study due to the fact that this instrument measures all three aspects of the model. Future research is needed to learn whether this model would fit other different samples. PMID:22952549

Cognitive Task Analysis of Prioritization in Air Traffic Control.

ERIC Educational Resources Information Center

Redding, Richard E.; And Others

A cognitive task analysis was performed to analyze the key cognitive components of the en route air traffic controllers' jobs. The goals were to ascertain expert mental models and decision-making strategies and to identify important differences in controller knowledge, skills, and mental models as a function of expertise. Four groups of…

Design of Training Systems. Computerization of the Educational Technology Assessment Model. Volume 1.

ERIC Educational Resources Information Center

Duffy, Larry B.; And Others

The Educational Technology Assessment Model (ETAM) is a set of comprehensive procedures and variables for the analysis, synthesis, and decision making, in regard to the benefits, costs, and risks associated with introducing technical innovations in education and training. This final report summarizes the analysis, design, and development…

Learning from the Past, Looking to the Future: Modeling Social Unrest in Karachi, Pakistan

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

Olson, Jarrod; Kurzrok, Andrew J.; Hund, Gretchen

Social unrest represents a major challenge for policy makers around the globe, as it can quickly escalate from small scale disturbances to highly public protests, riots and even civil war. This research was motivated by a need to understand social instability and to unpack the comments made during a spring 2013 conference hosted by Pacific Northwest National Laboratory’s Center for Global Security and the U.S. Institute for Peace, where policymakers noted that models considering social instability are often not suitable for decision-making. This analysis shows that existing state level models of instability could be improved in spatial scale to themore » city level, even without significantly improved data access. Better data would make this analysis more complete and likely improve the quality of the model. Another challenge with incorporating modeling into decision-making is the need to understand uncertainty in a model. Policy makers are frequently tasked with making decisions without a clear outcome, so characterization of uncertainty is critical. This report describes the work and findings of the project. It took place in three phases: a literature review of social stability research, a “hindsight scan” that looked at historical data, and a “foresight scan” looking at future scenarios.« less

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.

Aircrew coordination and decisionmaking: Peer ratings of video tapes made during a full mission simulation

NASA Technical Reports Server (NTRS)

Murphy, M. R.; Awe, C. A.

1986-01-01

Six professionally active, retired captains rated the coordination and decisionmaking performances of sixteen aircrews while viewing videotapes of a simulated commercial air transport operation. The scenario featured a required diversion and a probable minimum fuel situation. Seven point Likert-type scales were used in rating variables on the basis of a model of crew coordination and decisionmaking. The variables were based on concepts of, for example, decision difficulty, efficiency, and outcome quality; and leader-subordin ate concepts such as person and task-oriented leader behavior, and competency motivation of subordinate crewmembers. Five-front-end variables of the model were in turn dependent variables for a hierarchical regression procedure. The variance in safety performance was explained 46%, by decision efficiency, command reversal, and decision quality. The variance of decision quality, an alternative substantive dependent variable to safety performance, was explained 60% by decision efficiency and the captain's quality of within-crew communications. The variance of decision efficiency, crew coordination, and command reversal were in turn explained 78%, 80%, and 60% by small numbers of preceding independent variables. A principle component, varimax factor analysis supported the model structure suggested by regression analyses.

A regret theory approach to decision curve analysis: a novel method for eliciting decision makers' preferences and decision-making.

PubMed

Tsalatsanis, Athanasios; Hozo, Iztok; Vickers, Andrew; Djulbegovic, Benjamin

2010-09-16

Decision curve analysis (DCA) has been proposed as an alternative method for evaluation of diagnostic tests, prediction models, and molecular markers. However, DCA is based on expected utility theory, which has been routinely violated by decision makers. Decision-making is governed by intuition (system 1), and analytical, deliberative process (system 2), thus, rational decision-making should reflect both formal principles of rationality and intuition about good decisions. We use the cognitive emotion of regret to serve as a link between systems 1 and 2 and to reformulate DCA. First, we analysed a classic decision tree describing three decision alternatives: treat, do not treat, and treat or no treat based on a predictive model. We then computed the expected regret for each of these alternatives as the difference between the utility of the action taken and the utility of the action that, in retrospect, should have been taken. For any pair of strategies, we measure the difference in net expected regret. Finally, we employ the concept of acceptable regret to identify the circumstances under which a potentially wrong strategy is tolerable to a decision-maker. We developed a novel dual visual analog scale to describe the relationship between regret associated with "omissions" (e.g. failure to treat) vs. "commissions" (e.g. treating unnecessary) and decision maker's preferences as expressed in terms of threshold probability. We then proved that the Net Expected Regret Difference, first presented in this paper, is equivalent to net benefits as described in the original DCA. Based on the concept of acceptable regret we identified the circumstances under which a decision maker tolerates a potentially wrong decision and expressed it in terms of probability of disease. We present a novel method for eliciting decision maker's preferences and an alternative derivation of DCA based on regret theory. Our approach may be intuitively more appealing to a decision-maker, particularly in those clinical situations when the best management option is the one associated with the least amount of regret (e.g. diagnosis and treatment of advanced cancer, etc).

A regret theory approach to decision curve analysis: A novel method for eliciting decision makers' preferences and decision-making

PubMed Central

2010-01-01

Background Decision curve analysis (DCA) has been proposed as an alternative method for evaluation of diagnostic tests, prediction models, and molecular markers. However, DCA is based on expected utility theory, which has been routinely violated by decision makers. Decision-making is governed by intuition (system 1), and analytical, deliberative process (system 2), thus, rational decision-making should reflect both formal principles of rationality and intuition about good decisions. We use the cognitive emotion of regret to serve as a link between systems 1 and 2 and to reformulate DCA. Methods First, we analysed a classic decision tree describing three decision alternatives: treat, do not treat, and treat or no treat based on a predictive model. We then computed the expected regret for each of these alternatives as the difference between the utility of the action taken and the utility of the action that, in retrospect, should have been taken. For any pair of strategies, we measure the difference in net expected regret. Finally, we employ the concept of acceptable regret to identify the circumstances under which a potentially wrong strategy is tolerable to a decision-maker. Results We developed a novel dual visual analog scale to describe the relationship between regret associated with "omissions" (e.g. failure to treat) vs. "commissions" (e.g. treating unnecessary) and decision maker's preferences as expressed in terms of threshold probability. We then proved that the Net Expected Regret Difference, first presented in this paper, is equivalent to net benefits as described in the original DCA. Based on the concept of acceptable regret we identified the circumstances under which a decision maker tolerates a potentially wrong decision and expressed it in terms of probability of disease. Conclusions We present a novel method for eliciting decision maker's preferences and an alternative derivation of DCA based on regret theory. Our approach may be intuitively more appealing to a decision-maker, particularly in those clinical situations when the best management option is the one associated with the least amount of regret (e.g. diagnosis and treatment of advanced cancer, etc). PMID:20846413

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.

A decision impact, decision conflict and economic assessment of routine Oncotype DX testing of 146 women with node-negative or pNImi, ER-positive breast cancer in the U.K.

PubMed

Holt, S; Bertelli, G; Humphreys, I; Valentine, W; Durrani, S; Pudney, D; Rolles, M; Moe, M; Khawaja, S; Sharaiha, Y; Brinkworth, E; Whelan, S; Jones, S; Bennett, H; Phillips, C J

2013-06-11

Tumour gene expression analysis is useful in predicting adjuvant chemotherapy benefit in early breast cancer patients. This study aims to examine the implications of routine Oncotype DX testing in the U.K. Women with oestrogen receptor positive (ER+), pNO or pN1mi breast cancer were assessed for adjuvant chemotherapy and subsequently offered Oncotype DX testing, with changes in chemotherapy decisions recorded. A subset of patients completed questionnaires about their uncertainties regarding chemotherapy decisions pre- and post-testing. All patients were asked to complete a diary of medical interactions over the next 6 months, from which economic data were extracted to model the cost-effectiveness of testing. Oncotype DX testing resulted in changes in chemotherapy decisions in 38 of 142 (26.8%) women, with 26 of 57 (45.6%) spared chemotherapy and 12 of 85 (14.1%) requiring chemotherapy when not initially recommended (9.9% reduction overall). Decision conflict analysis showed that Oncotype DX testing increased patients' confidence in treatment decision making. Economic analysis showed that routine Oncotype DX testing costs £6232 per quality-adjusted life year gained. Oncotype DX decreased chemotherapy use and increased confidence in treatment decision making in patients with ER+ early-stage breast cancer. Based on these findings, Oncotype DX is cost-effective in the UK setting.

Using whole disease modeling to inform resource allocation decisions: economic evaluation of a clinical guideline for colorectal cancer using a single model.

PubMed

Tappenden, Paul; Chilcott, Jim; Brennan, Alan; Squires, Hazel; Glynne-Jones, Rob; Tappenden, Janine

2013-06-01

To assess the feasibility and value of simulating whole disease and treatment pathways within a single model to provide a common economic basis for informing resource allocation decisions. A patient-level simulation model was developed with the intention of being capable of evaluating multiple topics within National Institute for Health and Clinical Excellence's colorectal cancer clinical guideline. The model simulates disease and treatment pathways from preclinical disease through to detection, diagnosis, adjuvant/neoadjuvant treatments, follow-up, curative/palliative treatments for metastases, supportive care, and eventual death. The model parameters were informed by meta-analyses, randomized trials, observational studies, health utility studies, audit data, costing sources, and expert opinion. Unobservable natural history parameters were calibrated against external data using Bayesian Markov chain Monte Carlo methods. Economic analysis was undertaken using conventional cost-utility decision rules within each guideline topic and constrained maximization rules across multiple topics. Under usual processes for guideline development, piecewise economic modeling would have been used to evaluate between one and three topics. The Whole Disease Model was capable of evaluating 11 of 15 guideline topics, ranging from alternative diagnostic technologies through to treatments for metastatic disease. The constrained maximization analysis identified a configuration of colorectal services that is expected to maximize quality-adjusted life-year gains without exceeding current expenditure levels. This study indicates that Whole Disease Model development is feasible and can allow for the economic analysis of most interventions across a disease service within a consistent conceptual and mathematical infrastructure. This disease-level modeling approach may be of particular value in providing an economic basis to support other clinical guidelines. Copyright © 2013 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Community resilience and decision theory challenges for catastrophic events.

PubMed

Cox, Louis Anthony

2012-11-01

Extreme and catastrophic events pose challenges for normative models of risk management decision making. They invite development of new methods and principles to complement existing normative decision and risk analysis. Because such events are rare, it is difficult to learn about them from experience. They can prompt both too little concern before the fact, and too much after. Emotionally charged and vivid outcomes promote probability neglect and distort risk perceptions. Aversion to acting on uncertain probabilities saps precautionary action; moral hazard distorts incentives to take care; imperfect learning and social adaptation (e.g., herd-following, group-think) complicate forecasting and coordination of individual behaviors and undermine prediction, preparation, and insurance of catastrophic events. Such difficulties raise substantial challenges for normative decision theories prescribing how catastrophe risks should be managed. This article summarizes challenges for catastrophic hazards with uncertain or unpredictable frequencies and severities, hard-to-envision and incompletely described decision alternatives and consequences, and individual responses that influence each other. Conceptual models and examples clarify where and why new methods are needed to complement traditional normative decision theories for individuals and groups. For example, prospective and retrospective preferences for risk management alternatives may conflict; procedures for combining individual beliefs or preferences can produce collective decisions that no one favors; and individual choices or behaviors in preparing for possible disasters may have no equilibrium. Recent ideas for building "disaster-resilient" communities can complement traditional normative decision theories, helping to meet the practical need for better ways to manage risks of extreme and catastrophic events. © 2012 Society for Risk Analysis.

Communication with U.S. federal decision makers : a primer with notes on the use of computer models as a means of communication.

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

Webb, Erik Karl; Tidwell, Vincent Carroll

2009-10-01

This document outlines ways to more effectively communicate with U.S. Federal decision makers by outlining the structure, authority, and motivations of various Federal groups, how to find the trusted advisors, and how to structure communication. All three branches of Federal governments have decision makers engaged in resolving major policy issues. The Legislative Branch (Congress) negotiates the authority and the resources that can be used by the Executive Branch. The Executive Branch has some latitude in implementation and prioritizing resources. The Judicial Branch resolves disputes. The goal of all decision makers is to choose and implement the option that best fitsmore » the needs and wants of the community. However, understanding the risk of technical, political and/or financial infeasibility and possible unintended consequences is extremely difficult. Primarily, decision makers are supported in their deliberations by trusted advisors who engage in the analysis of options as well as the day-to-day tasks associated with multi-party negotiations. In the best case, the trusted advisors use many sources of information to inform the process including the opinion of experts and if possible predictive analysis from which they can evaluate the projected consequences of their decisions. The paper covers the following: (1) Understanding Executive and Legislative decision makers - What can these decision makers do? (2) Finding the target audience - Who are the internal and external trusted advisors? (3) Packaging the message - How do we parse and integrate information, and how do we use computer simulation or models in policy communication?« less

Bayesian outcome-based strategy classification.

PubMed

Lee, Michael D

2016-03-01

Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014) recently developed a method for making inferences about the decision processes people use in multi-attribute forced choice tasks. Their paper makes a number of worthwhile theoretical and methodological contributions. Theoretically, they provide an insightful psychological motivation for a probabilistic extension of the widely-used "weighted additive" (WADD) model, and show how this model, as well as other important models like "take-the-best" (TTB), can and should be expressed in terms of meaningful priors. Methodologically, they develop an inference approach based on the Minimum Description Length (MDL) principles that balances both the goodness-of-fit and complexity of the decision models they consider. This paper aims to preserve these useful contributions, but provide a complementary Bayesian approach with some theoretical and methodological advantages. We develop a simple graphical model, implemented in JAGS, that allows for fully Bayesian inferences about which models people use to make decisions. To demonstrate the Bayesian approach, we apply it to the models and data considered by Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014), showing how a prior predictive analysis of the models, and posterior inferences about which models people use and the parameter settings at which they use them, can contribute to our understanding of human decision making.

Probabilistic Radiological Performance Assessment Modeling and Uncertainty

NASA Astrophysics Data System (ADS)

Tauxe, J.

2004-12-01

A generic probabilistic radiological Performance Assessment (PA) model is presented. The model, built using the GoldSim systems simulation software platform, concerns contaminant transport and dose estimation in support of decision making with uncertainty. Both the U.S. Nuclear Regulatory Commission (NRC) and the U.S. Department of Energy (DOE) require assessments of potential future risk to human receptors of disposal of LLW. Commercially operated LLW disposal facilities are licensed by the NRC (or agreement states), and the DOE operates such facilities for disposal of DOE-generated LLW. The type of PA model presented is probabilistic in nature, and hence reflects the current state of knowledge about the site by using probability distributions to capture what is expected (central tendency or average) and the uncertainty (e.g., standard deviation) associated with input parameters, and propagating through the model to arrive at output distributions that reflect expected performance and the overall uncertainty in the system. Estimates of contaminant release rates, concentrations in environmental media, and resulting doses to human receptors well into the future are made by running the model in Monte Carlo fashion, with each realization representing a possible combination of input parameter values. Statistical summaries of the results can be compared to regulatory performance objectives, and decision makers are better informed of the inherently uncertain aspects of the model which supports their decision-making. While this information may make some regulators uncomfortable, they must realize that uncertainties which were hidden in a deterministic analysis are revealed in a probabilistic analysis, and the chance of making a correct decision is now known rather than hoped for. The model includes many typical features and processes that would be part of a PA, but is entirely fictitious. This does not represent any particular site and is meant to be a generic example. A practitioner could, however, start with this model as a GoldSim template and, by adding site specific features and parameter values (distributions), use this model as a starting point for a real model to be used in real decision making.

Climate Modeling and Analysis with Decision Makers in Mind

NASA Astrophysics Data System (ADS)

Jones, A. D.; Jagannathan, K.; Calvin, K. V.; Lamarque, J. F.; Ullrich, P. A.

2016-12-01

There is a growing need for information about future climate conditions to support adaptation planning across a wide range of sectors and stakeholder communities. However, our principal tools for understanding future climate - global Earth system models - were not developed with these user needs in mind, nor have we developed transparent methods for evaluating and communicating the credibility of various climate information products with respect to the climate characteristics that matter most to decision-makers. Several recent community engagements have identified a need for "co-production" of knowledge among stakeholders and scientists. Here we highlight some of the barriers to communication and collaboration that must be overcome to improve the dialogue among researchers and climate adaptation practitioners in a meaningful way. Solutions to this challenge are two-fold: 1) new institutional arrangements and collaborative mechanisms designed to improve coordination and understanding among communities, and 2) a research agenda that explicitly incorporates stakeholder needs into model evaluation, development, and experimental design. We contrast the information content in global-scale model evaluation exercises with that required for in specific decision contexts, such as long-term agricultural management decisions. Finally, we present a vision for advancing the science of model evaluation in the context of predicting decision-relevant hydroclimate regime shifts in North America.

Improved representation of situational awareness within a dismounted small combat unit constructive simulation

NASA Astrophysics Data System (ADS)

Lee, K. David; Colony, Mike

2011-06-01

Modeling and simulation has been established as a cost-effective means of supporting the development of requirements, exploring doctrinal alternatives, assessing system performance, and performing design trade-off analysis. The Army's constructive simulation for the evaluation of equipment effectiveness in small combat unit operations is currently limited to representation of situation awareness without inclusion of the many uncertainties associated with real world combat environments. The goal of this research is to provide an ability to model situation awareness and decision process uncertainties in order to improve evaluation of the impact of battlefield equipment on ground soldier and small combat unit decision processes. Our Army Probabilistic Inference and Decision Engine (Army-PRIDE) system provides this required uncertainty modeling through the application of two critical techniques that allow Bayesian network technology to be applied to real-time applications. (Object-Oriented Bayesian Network methodology and Object-Oriented Inference technique). In this research, we implement decision process and situation awareness models for a reference scenario using Army-PRIDE and demonstrate its ability to model a variety of uncertainty elements, including: confidence of source, information completeness, and information loss. We also demonstrate that Army-PRIDE improves the realism of the current constructive simulation's decision processes through Monte Carlo simulation.

A multiobjective decision support/numerical modeling approach for design and evaluation of shallow landfill burial systems

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

Ascough, II, James Clifford

1992-05-01

The capability to objectively evaluate design performance of shallow landfill burial (SLB) systems is of great interest to diverse scientific disciplines, including hydrologists, engineers, environmental scientists, and SLB regulators. The goal of this work was to develop and validate a procedure for the nonsubjective evaluation of SLB designs under actual or simulated environmental conditions. A multiobjective decision module (MDM) based on scoring functions (Wymore, 1988) was implemented to evaluate SLB design performance. Input values to the MDM are provided by hydrologic models. The MDM assigns a total score to each SLB design alternative, thereby allowing for rapid and repeatable designmore » performance evaluation. The MDM was validated for a wide range of SLB designs under different climatic conditions. Rigorous assessment of SLB performance also requires incorporation of hydrologic probabilistic analysis and hydrologic risk into the overall design. This was accomplished through the development of a frequency analysis module. The frequency analysis module allows SLB design event magnitudes to be calculated based on the hydrologic return period. The multiobjective decision and freqeuncy anslysis modules were integrated in a decision support system (DSS) framework, SLEUTH (Shallow Landfill Evaluation Using Transport and Hydrology). SLEUTH is a Microsoft Windows {trademark} application, and is written in the Knowledge Pro Windows (Knowledge Garden, Inc., 1991) development language.« less

Model application niche analysis: An approach for assessing the transferability and generalizability of ecological models

EPA Science Inventory

A 30-year review of predictive models used in regulatory decision-making, revealed that transferring models to contexts other than that for which the models were developed was one of the biggest vulnerabilities to their legal defensibility. The use and transfer of models by ecolo...

Analysis of the decision-making process leading to appendectomy: a grounded theory study.

PubMed

Larsson, Gerry; Weibull, Henrik; Larsson, Bodil Wilde

2004-11-01

The aim was to develop a theoretical understanding of the decision-making process leading to appendectomy. A qualitative interview study was performed in the grounded theory tradition using the constant comparative method to analyze data. The study setting was one county hospital and two local hospitals in Sweden, where 11 surgeons and 15 surgical nurses were interviewed. A model was developed which suggests that surgeons' decision making regarding appendectomy is formed by the interplay between their medical assessment of the patient's condition and a set of contextual characteristics. The latter consist of three interacting factors: (1) organizational conditions, (2) the professional actors' individual characteristics and interaction, and (3) the personal characteristics of the patient and his or her family or relatives. In case the outcome of medical assessment is ambiguous, the risk evaluation and final decision will be influenced by an interaction of the contextual characteristics. It was concluded that, compared to existing, rational models of decision making, the model presented identified potentially important contextual characteristics and an outline on when they come into play.

A mechanism for value-sensitive decision-making.

PubMed

Pais, Darren; Hogan, Patrick M; Schlegel, Thomas; Franks, Nigel R; Leonard, Naomi E; Marshall, James A R

2013-01-01

We present a dynamical systems analysis of a decision-making mechanism inspired by collective choice in house-hunting honeybee swarms, revealing the crucial role of cross-inhibitory 'stop-signalling' in improving the decision-making capabilities. We show that strength of cross-inhibition is a decision-parameter influencing how decisions depend both on the difference in value and on the mean value of the alternatives; this is in contrast to many previous mechanistic models of decision-making, which are typically sensitive to decision accuracy rather than the value of the option chosen. The strength of cross-inhibition determines when deadlock over similarly valued alternatives is maintained or broken, as a function of the mean value; thus, changes in cross-inhibition strength allow adaptive time-dependent decision-making strategies. Cross-inhibition also tunes the minimum difference between alternatives required for reliable discrimination, in a manner similar to Weber's law of just-noticeable difference. Finally, cross-inhibition tunes the speed-accuracy trade-off realised when differences in the values of the alternatives are sufficiently large to matter. We propose that the model, and the significant role of the values of the alternatives, may describe other decision-making systems, including intracellular regulatory circuits, and simple neural circuits, and may provide guidance in the design of decision-making algorithms for artificial systems, particularly those functioning without centralised control.

End-of-life decision making is more than rational.

PubMed

Eliott, Jaklin A; Olver, Ian N

2005-01-01

Most medical models of end-of-life decision making by patients assume a rational autonomous adult obtaining and deliberating over information to arrive at some conclusion. If the patient is deemed incapable of this, family members are often nominated as substitutes, with assumptions that the family are united and rational. These are problematic assumptions. We interviewed 23 outpatients with cancer about the decision not to resuscitate a patient following cardiopulmonary arrest and examined their accounts of decision making using discourse analytical techniques. Our analysis suggests that participants access two different interpretative repertoires regarding the construct of persons, invoking a 'modernist' repertoire to assert the appropriateness of someone, a patient or family, making a decision, and a 'romanticist' repertoire when identifying either a patient or family as ineligible to make the decision. In determining the appropriateness of an individual to make decisions, participants informally apply 'Sanity' and 'Stability' tests, assessing both an inherent ability to reason (modernist repertoire) and the presence of emotion (romanticist repertoire) which might impact on the decision making process. Failure to pass the tests respectively excludes or excuses individuals from decision making. The absence of the romanticist repertoire in dominant models of patient decision making has ethical implications for policy makers and medical practitioners dealing with dying patients and their families.

It’s Personal: Biology Instructors Prioritize Personal Evidence over Empirical Evidence in Teaching Decisions

PubMed Central

Andrews, Tessa C.; Lemons, Paula P.

2015-01-01

Despite many calls for undergraduate biology instructors to incorporate active learning into lecture courses, few studies have focused on what it takes for instructors to make this change. We sought to investigate the process of adopting and sustaining active-learning instruction. As a framework for our research, we used the innovation-decision model, a generalized model of how individuals adopt innovations. We interviewed 17 biology instructors who were attempting to implement case study teaching and conducted qualitative text analysis on interview data. The overarching theme that emerged from our analysis was that instructors prioritized personal experience—rather than empirical evidence—in decisions regarding case study teaching. We identified personal experiences that promote case study teaching, such as anecdotal observations of student outcomes, and those that hinder case study teaching, such as insufficient teaching skills. By analyzing the differences between experienced and new case study instructors, we discovered that new case study instructors need support to deal with unsupportive colleagues and to develop the skill set needed for an active-learning classroom. We generated hypotheses that are grounded in our data about effectively supporting instructors in adopting and sustaining active-learning strategies. We also synthesized our findings with existing literature to tailor the innovation-decision model. PMID:25713092

Integrating Efficiency of Industry Processes and Practices Alongside Technology Effectiveness in Space Transportation Cost Modeling and Analysis

NASA Technical Reports Server (NTRS)

Zapata, Edgar

2012-01-01

This paper presents past and current work in dealing with indirect industry and NASA costs when providing cost estimation or analysis for NASA projects and programs. Indirect costs, when defined as those costs in a project removed from the actual hardware or software hands-on labor; makes up most of the costs of today's complex large scale NASA space/industry projects. This appears to be the case across phases from research into development into production and into the operation of the system. Space transportation is the case of interest here. Modeling and cost estimation as a process rather than a product will be emphasized. Analysis as a series of belief systems in play among decision makers and decision factors will also be emphasized to provide context.

Multiple Criteria Decision Analysis (MCDA) for evaluating new medicines in Health Technology Assessment and beyond: The Advance Value Framework.

PubMed

Angelis, Aris; Kanavos, Panos

2017-09-01

Escalating drug prices have catalysed the generation of numerous "value frameworks" with the aim of informing payers, clinicians and patients on the assessment and appraisal process of new medicines for the purpose of coverage and treatment selection decisions. Although this is an important step towards a more inclusive Value Based Assessment (VBA) approach, aspects of these frameworks are based on weak methodologies and could potentially result in misleading recommendations or decisions. In this paper, a Multiple Criteria Decision Analysis (MCDA) methodological process, based on Multi Attribute Value Theory (MAVT), is adopted for building a multi-criteria evaluation model. A five-stage model-building process is followed, using a top-down "value-focused thinking" approach, involving literature reviews and expert consultations. A generic value tree is structured capturing decision-makers' concerns for assessing the value of new medicines in the context of Health Technology Assessment (HTA) and in alignment with decision theory. The resulting value tree (Advance Value Tree) consists of three levels of criteria (top level criteria clusters, mid-level criteria, bottom level sub-criteria or attributes) relating to five key domains that can be explicitly measured and assessed: (a) burden of disease, (b) therapeutic impact, (c) safety profile (d) innovation level and (e) socioeconomic impact. A number of MAVT modelling techniques are introduced for operationalising (i.e. estimating) the model, for scoring the alternative treatment options, assigning relative weights of importance to the criteria, and combining scores and weights. Overall, the combination of these MCDA modelling techniques for the elicitation and construction of value preferences across the generic value tree provides a new value framework (Advance Value Framework) enabling the comprehensive measurement of value in a structured and transparent way. Given its flexibility to meet diverse requirements and become readily adaptable across different settings, the Advance Value Framework could be offered as a decision-support tool for evaluators and payers to aid coverage and reimbursement of new medicines. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Reducing uncertainty in value-based pricing using evidence development agreements: the case of continuous intraduodenal infusion of levodopa/carbidopa (Duodopa®) in Sweden.

PubMed

Willis, Michael; Persson, Ulf; Zoellner, York; Gradl, Birgit

2010-01-01

Value-based pricing (VBP), whereby prices are set according to the perceived benefits offered to the consumer at a time when costs and benefits are characterized by considerable uncertainty and are then reviewed ex post, is a much discussed topic in pharmaceutical reimbursement. It is usually combined with coverage with evidence development (CED), a tool in which manufacturers are granted temporary reimbursement but are required to collect and submit additional health economic data at review. Many countries, including the UK, are signalling shifts in this direction. Several countries, including Sweden, have already adopted this approach and offer good insight into the benefits and pitfalls in actual practice. To describe VBP reimbursement decision making using CED in actual practice in Sweden. Decision making by The Dental and Pharmaceutical Benefits Agency (TLV) in Sweden was reviewed using a case study of continuous intraduodenal infusion of levodopa/carbidopa (Duodopa®) in the treatment of advanced Parkinson's disease (PD) with severe motor fluctuations. The manufacturer of Duodopa® applied for reimbursement in late 2003. While the proper economic data were not included in the submission, TLV granted reimbursement until early 2005 to provide time for the manufacturer to submit a formal economic evaluation. The re-submission with economic data was considered inadequate to judge cost effectiveness, so TLV granted an additional extension of reimbursement until August 2007, at which time conclusive data were expected. The manufacturer initiated a 3-year, prospective health economic study and a formal economic model. Data from a pre-planned interim analysis of the data were loaded into the model and the cost-effectiveness ratio was the basis of the next re-submission. TLV concluded that the data were suitable for making a definite decision and that the drug was not cost effective, deciding to discontinue reimbursement for any new patients (current patients were unaffected). The manufacturer continued to collect data and to improve the economic model and re-submitted in 2008. New data and the improved model resulted in reduced uncertainty and a lower cost-effectiveness ratio in the range of Swedish kronor (SEK)430,000 per QALY gained in the base-case analysis, ranging up to SEK900,000 in the most conservative sensitivity analysis, resulting in reimbursement being granted. The case of Duodopa® provides excellent insight into VBP reimbursement decision making in combination with CED and ex post review in actual practice. Publicly available decisions document the rigorous, time-consuming process (four iterations were required before a final decision could be reached). The data generated as part of the risk-sharing agreement proved correct the initial decision to grant limited coverage despite lack of economic data. Access was provided to 100 patients while evidence was generated. Economic appraisal differs from clinical assessment, and decision makers benefit from analysis of naturalistic, actual practice data. Despite reviewing the initial trial-based, 'piggy-back' economic analysis, TLV was uncertain of the cost effectiveness in actual practice and deferred a final decision until observational data from the DAPHNE study became available. Second, acceptance of economic modelling and use of temporary reimbursement conditional on additional evidence development provide a mechanism for risk sharing between TLV and manufacturers, which enabled patient access to a drug with proven clinical benefit while necessary evidence to support claims of cost effectiveness could be generated.

Analysis for Non-Traditional Security Challenges: Methods and Tools

DTIC Science & Technology

2006-11-20

PMESII Modeling Challenges modeling or where data is not available to support the model, would aid decision Domain is large, nebulous, complex, and...traditional challenges . This includes enlisting the aid of the inter-agency and alliance/coalition communities. Second, we need to realize this...20 November 2006 MILITARY OPERATIONS RESEARCH SOCIETY MIFh MORS Workshop Analysis for Non-Traditional Security Challenges : Methods and Tools 21-23

Multi-criteria decision making to support waste management: A critical review of current practices and methods.

PubMed

Goulart Coelho, Lineker M; Lange, Liséte C; Coelho, Hosmanny Mg

2017-01-01

Solid waste management is a complex domain involving the interaction of several dimensions; thus, its analysis and control impose continuous challenges for decision makers. In this context, multi-criteria decision-making models have become important and convenient supporting tools for solid waste management because they can handle problems involving multiple dimensions and conflicting criteria. However, the selection of the multi-criteria decision-making method is a hard task since there are several multi-criteria decision-making approaches, each one with a large number of variants whose applicability depends on information availability and the aim of the study. Therefore, to support researchers and decision makers, the objectives of this article are to present a literature review of multi-criteria decision-making applications used in solid waste management, offer a critical assessment of the current practices, and provide suggestions for future works. A brief review of fundamental concepts on this topic is first provided, followed by the analysis of 260 articles related to the application of multi-criteria decision making in solid waste management. These studies were investigated in terms of the methodology, including specific steps such as normalisation, weighting, and sensitivity analysis. In addition, information related to waste type, the study objective, and aspects considered was recorded. From the articles analysed it is noted that studies using multi-criteria decision making in solid waste management are predominantly addressed to problems related to municipal solid waste involving facility location or management strategy.

Applicability of aquifer impact models to support decisions at CO2 sequestration sites

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

Keating, Elizabeth; Bacon, Diana; Carroll, Susan

2016-09-01

The National Risk Assessment Partnership has developed a suite of tools to assess and manage risk at CO2 sequestration sites (www.netldoe.gov/nrap). This capability includes polynomial or look-up table based reduced-order models (ROMs) that predict the impact of CO2 and brine leaks on overlying aquifers. The development of these computationally-efficient models and the underlying reactive transport simulations they emulate has been documented elsewhere (Carroll et al., 2014, Dai et al., 2014, Keating et al., 2015). The ROMs reproduce the ensemble behavior of large numbers of simulations and are well-suited to applications that consider a large number of scenarios to understand parametermore » sensitivity and uncertainty on the risk of CO2 leakage to groundwater quality. In this paper, we seek to demonstrate applicability of ROM-based ensemble analysis by considering what types of decisions and aquifer types would benefit from the ROM analysis. We present four hypothetical four examples where applying ROMs, in ensemble mode, could support decisions in the early stages in a geologic CO2 sequestration project. These decisions pertain to site selection, site characterization, monitoring network evaluation, and health impacts. In all cases, we consider potential brine/CO2 leak rates at the base of the aquifer to be uncertain. We show that derived probabilities provide information relevant to the decision at hand. Although the ROMs were developed using site-specific data from two aquifers (High Plains and Edwards), the models accept aquifer characteristics as variable inputs and so they may have more broad applicability. We conclude that pH and TDS predictions are the most transferable to other aquifers based on the analysis of the nine water quality metrics (pH, TDS, 4 trace metals, 3 organic compounds). Guidelines are presented for determining the aquifer types for which the ROMs should be applicable.« less

A new spatial multiple discrete-continuous modeling approach to land use change analysis.

DOT National Transportation Integrated Search

2013-09-01

This report formulates a multiple discrete-continuous probit (MDCP) land-use model within a : spatially explicit economic structural framework for land-use change decisions. The spatial : MDCP model is capable of predicting both the type and intensit...

Modeling Joint Climate and Bioenergy Policies: Challenges of integrating economic and environmental data. (Invited)

NASA Astrophysics Data System (ADS)

Hellwinckel, C. M.; West, T. O.; de La Torre Ugarte, D.; Perlack, R.

2010-12-01

In the coming decades agriculture will be asked to play a significant role in reducing carbon emissions and reducing our use of foreign oil. The Renewable Fuels Standard combined with possible climate legislation will alter the economic landscape effecting agricultural land use decisions. The joint implementation of these two policies could potentially work against one another. We have integrated biogeophysical data into the POLYSYS economic model to analyze the effects of climate change and bioenergy legislation upon regional land-use change, soil carbon, carbon emissions, biofuel production, and agricultural income. The purpose of the analysis was to use the integrated model to identify carbon and bioenergy policies that could act synergistically to meet Renewable Fuel Standard goals, reduce net emissions of carbon, and increase agricultural incomes. The heterogeneous nature of soils, crop yields, and management practices presented challenges to the modeling process. Regional variation in physical data can significantly affect economic land use decisions and patterns. For this reason, we disaggregated the economic component of the model to the county level, with sub-county soils and land-use data informing the county level decisions. Modeling carbon offset dynamics presented unique challenges, as the physical responses of local soils impact the economic incentives offered, and conversely, the resulting land-use changes impact characteristics of local soils. Additionally, using data from different resolution levels led to questions of appropriate scale of analysis. This presentation will describe the integrated model, present some significant results from our analysis, and discuss appropriate steps forward given what we learned.

An interval-parameter mixed integer multi-objective programming for environment-oriented evacuation management

NASA Astrophysics Data System (ADS)

Wu, C. Z.; Huang, G. H.; Yan, X. P.; Cai, Y. P.; Li, Y. P.

2010-05-01

Large crowds are increasingly common at political, social, economic, cultural and sports events in urban areas. This has led to attention on the management of evacuations under such situations. In this study, we optimise an approximation method for vehicle allocation and route planning in case of an evacuation. This method, based on an interval-parameter multi-objective optimisation model, has potential for use in a flexible decision support system for evacuation management. The modeling solutions are obtained by sequentially solving two sub-models corresponding to lower- and upper-bounds for the desired objective function value. The interval solutions are feasible and stable in the given decision space, and this may reduce the negative effects of uncertainty, thereby improving decision makers' estimates under different conditions. The resulting model can be used for a systematic analysis of the complex relationships among evacuation time, cost and environmental considerations. The results of a case study used to validate the proposed model show that the model does generate useful solutions for planning evacuation management and practices. Furthermore, these results are useful for evacuation planners, not only in making vehicle allocation decisions but also for providing insight into the tradeoffs among evacuation time, environmental considerations and economic objectives.

Competitive assessment of aerospace systems using system dynamics

NASA Astrophysics Data System (ADS)

Pfaender, Jens Holger

Aircraft design has recently experienced a trend away from performance centric design towards a more balanced approach with increased emphasis on engineering an economically successful system. This approach focuses on bringing forward a comprehensive economic and life-cycle cost analysis. Since the success of any system also depends on many external factors outside of the control of the designer, this traditionally has been modeled as noise affecting the uncertainty of the design. However, this approach is currently lacking a strategic treatment of necessary early decisions affecting the probability of success of a given concept in a dynamic environment. This suggests that the introduction of a dynamic method into a life-cycle cost analysis should allow the analysis of the future attractiveness of such a concept in the presence of uncertainty. One way of addressing this is through the use of a competitive market model. However, existing market models do not focus on the dynamics of the market. Instead, they focus on modeling and predicting market share through logit regression models. The resulting models exhibit relatively poor predictive capabilities. The method proposed here focuses on a top-down approach that integrates a competitive model based on work in the field of system dynamics into the aircraft design process. Demonstrating such integration is one of the primary contributions of this work, which previously has not been demonstrated. This integration is achieved through the use of surrogate models, in this case neural networks. This enabled not only the practical integration of analysis techniques, but also reduced the computational requirements so that interactive exploration as envisioned was actually possible. The example demonstration of this integration is built on the competition in the 250 seat large commercial aircraft market exemplified by the Boeing 767-400ER and the Airbus A330-200. Both aircraft models were calibrated to existing performance and certification data and then integrated into the system dynamics market model. The market model was then calibrated with historical market data. This calibration showed a much improved predictive capability as compared to the conventional logit regression models. An additional advantage of this dynamic model is that to realize this improved capability, no additional explanatory variables were required. Furthermore, the resulting market model was then integrated into a prediction profiler environment with a time variant Monte-Carlo analysis resulting in a unique trade-off environment. This environment was shown to allow interactive trade-off between aircraft design decisions and economic considerations while allowing the exploration potential market success in the light of varying external market conditions and scenarios. The resulting method is capable of reduced decision support uncertainty and identification of robust design decisions in future scenarios with a high likelihood of occurrence with special focus on the path dependent nature of future implications of decisions. Furthermore, it was possible to demonstrate the increased importance of design and technology choices on the competitiveness in scenarios with drastic increases in commodity prices during the time period modeled. Another use of the existing outputs of the Monte-Carlo analysis was then realized by showing them on a multivariate scatter plot. This plot was then shown to enable by appropriate grouping of variables to enable the top down definition of an aircraft design, also known as inverse design. In other words this enables the designer to define strategic market and return on investment goals for a number of scenarios, for example the development of fuel prices, and then directly see which specific aircraft designs meet these goals.

A critical narrative analysis of shared decision-making in acute inpatient mental health care.

PubMed

Stacey, Gemma; Felton, Anne; Morgan, Alastair; Stickley, Theo; Willis, Martin; Diamond, Bob; Houghton, Philip; Johnson, Beverley; Dumenya, John

2016-01-01

Shared decision-making (SDM) is a high priority in healthcare policy and is complementary to the recovery philosophy in mental health care. This agenda has been operationalised within the Values-Based Practice (VBP) framework, which offers a theoretical and practical model to promote democratic interprofessional approaches to decision-making. However, these are limited by a lack of recognition of the implications of power implicit within the mental health system. This study considers issues of power within the context of decision-making and examines to what extent decisions about patients' care on acute in-patient wards are perceived to be shared. Focus groups were conducted with 46 mental health professionals, service users, and carers. The data were analysed using the framework of critical narrative analysis (CNA). The findings of the study suggested each group constructed different identity positions, which placed them as inside or outside of the decision-making process. This reflected their view of themselves as best placed to influence a decision on behalf of the service user. In conclusion, the discourse of VBP and SDM needs to take account of how differentials of power and the positioning of speakers affect the context in which decisions take place.

Screen or not to screen for peripheral arterial disease: guidance from a decision model.

PubMed

Vaidya, Anil; Joore, Manuela A; Ten Cate-Hoek, Arina J; Ten Cate, Hugo; Severens, Johan L

2014-01-29

Asymptomatic Peripheral Arterial Disease (PAD) is associated with greater risk of acute cardiovascular events. This study aims to determine the cost-effectiveness of one time only PAD screening using Ankle Brachial Index (ABI) test and subsequent anti platelet preventive treatment (low dose aspirin or clopidogrel) in individuals at high risk for acute cardiovascular events compared to no screening and no treatment using decision analytic modelling. A probabilistic Markov model was developed to evaluate the life time cost-effectiveness of the strategy of selective PAD screening and consequent preventive treatment compared to no screening and no preventive treatment. The analysis was conducted from the Dutch societal perspective and to address decision uncertainty, probabilistic sensitivity analysis was performed. Results were based on average values of 1000 Monte Carlo simulations and using discount rates of 1.5% and 4% for effects and costs respectively. One way sensitivity analyses were performed to identify the two most influential model parameters affecting model outputs. Then, a two way sensitivity analysis was conducted for combinations of values tested for these two most influential parameters. For the PAD screening strategy, life years and quality adjusted life years gained were 21.79 and 15.66 respectively at a lifetime cost of 26,548 Euros. Compared to no screening and treatment (20.69 life years, 15.58 Quality Adjusted Life Years, 28,052 Euros), these results indicate that PAD screening and treatment is a dominant strategy. The cost effectiveness acceptability curves show 88% probability of PAD screening being cost effective at the Willingness To Pay (WTP) threshold of 40000 Euros. In a scenario analysis using clopidogrel as an alternative anti-platelet drug, PAD screening strategy remained dominant. This decision analysis suggests that targeted ABI screening and consequent secondary prevention of cardiovascular events using low dose aspirin or clopidogrel in the identified patients is a cost-effective strategy. Implementation of targeted PAD screening and subsequent treatment in primary care practices and in public health programs is likely to improve the societal health and to save health care costs by reducing catastrophic cardiovascular events.

RELATING ACCUMULATOR MODEL PARAMETERS AND NEURAL DYNAMICS

PubMed Central

Purcell, Braden A.; Palmeri, Thomas J.

2016-01-01

Accumulator models explain decision-making as an accumulation of evidence to a response threshold. Specific model parameters are associated with specific model mechanisms, such as the time when accumulation begins, the average rate of evidence accumulation, and the threshold. These mechanisms determine both the within-trial dynamics of evidence accumulation and the predicted behavior. Cognitive modelers usually infer what mechanisms vary during decision-making by seeing what parameters vary when a model is fitted to observed behavior. The recent identification of neural activity with evidence accumulation suggests that it may be possible to directly infer what mechanisms vary from an analysis of how neural dynamics vary. However, evidence accumulation is often noisy, and noise complicates the relationship between accumulator dynamics and the underlying mechanisms leading to those dynamics. To understand what kinds of inferences can be made about decision-making mechanisms based on measures of neural dynamics, we measured simulated accumulator model dynamics while systematically varying model parameters. In some cases, decision- making mechanisms can be directly inferred from dynamics, allowing us to distinguish between models that make identical behavioral predictions. In other cases, however, different parameterized mechanisms produce surprisingly similar dynamics, limiting the inferences that can be made based on measuring dynamics alone. Analyzing neural dynamics can provide a powerful tool to resolve model mimicry at the behavioral level, but we caution against drawing inferences based solely on neural analyses. Instead, simultaneous modeling of behavior and neural dynamics provides the most powerful approach to understand decision-making and likely other aspects of cognition and perception. PMID:28392584

Gating the holes in the Swiss cheese (part I): Expanding professor Reason's model for patient safety.

PubMed

Seshia, Shashi S; Bryan Young, G; Makhinson, Michael; Smith, Preston A; Stobart, Kent; Croskerry, Pat

2018-02-01

Although patient safety has improved steadily, harm remains a substantial global challenge. Additionally, safety needs to be ensured not only in hospitals but also across the continuum of care. Better understanding of the complex cognitive factors influencing health care-related decisions and organizational cultures could lead to more rational approaches, and thereby to further improvement. A model integrating the concepts underlying Reason's Swiss cheese theory and the cognitive-affective biases plus cascade could advance the understanding of cognitive-affective processes that underlie decisions and organizational cultures across the continuum of care. Thematic analysis, qualitative information from several sources being used to support argumentation. Complex covert cognitive phenomena underlie decisions influencing health care. In the integrated model, the Swiss cheese slices represent dynamic cognitive-affective (mental) gates: Reason's successive layers of defence. Like firewalls and antivirus programs, cognitive-affective gates normally allow the passage of rational decisions but block or counter unsounds ones. Gates can be breached (ie, holes created) at one or more levels of organizations, teams, and individuals, by (1) any element of cognitive-affective biases plus (conflicts of interest and cognitive biases being the best studied) and (2) other potential error-provoking factors. Conversely, flawed decisions can be blocked and consequences minimized; for example, by addressing cognitive biases plus and error-provoking factors, and being constantly mindful. Informed shared decision making is a neglected but critical layer of defence (cognitive-affective gate). The integrated model can be custom tailored to specific situations, and the underlying principles applied to all methods for improving safety. The model may also provide a framework for developing and evaluating strategies to optimize organizational cultures and decisions. The concept is abstract, the model is virtual, and the best supportive evidence is qualitative and indirect. The proposed model may help enhance rational decision making across the continuum of care, thereby improving patient safety globally. © 2017 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons, Ltd.

Gating the holes in the Swiss cheese (part I): Expanding professor Reason's model for patient safety

PubMed Central

Bryan Young, G.; Makhinson, Michael; Smith, Preston A.; Stobart, Kent; Croskerry, Pat

2017-01-01

Abstract Introduction Although patient safety has improved steadily, harm remains a substantial global challenge. Additionally, safety needs to be ensured not only in hospitals but also across the continuum of care. Better understanding of the complex cognitive factors influencing health care–related decisions and organizational cultures could lead to more rational approaches, and thereby to further improvement. Hypothesis A model integrating the concepts underlying Reason's Swiss cheese theory and the cognitive‐affective biases plus cascade could advance the understanding of cognitive‐affective processes that underlie decisions and organizational cultures across the continuum of care. Methods Thematic analysis, qualitative information from several sources being used to support argumentation. Discussion Complex covert cognitive phenomena underlie decisions influencing health care. In the integrated model, the Swiss cheese slices represent dynamic cognitive‐affective (mental) gates: Reason's successive layers of defence. Like firewalls and antivirus programs, cognitive‐affective gates normally allow the passage of rational decisions but block or counter unsounds ones. Gates can be breached (ie, holes created) at one or more levels of organizations, teams, and individuals, by (1) any element of cognitive‐affective biases plus (conflicts of interest and cognitive biases being the best studied) and (2) other potential error‐provoking factors. Conversely, flawed decisions can be blocked and consequences minimized; for example, by addressing cognitive biases plus and error‐provoking factors, and being constantly mindful. Informed shared decision making is a neglected but critical layer of defence (cognitive‐affective gate). The integrated model can be custom tailored to specific situations, and the underlying principles applied to all methods for improving safety. The model may also provide a framework for developing and evaluating strategies to optimize organizational cultures and decisions. Limitations The concept is abstract, the model is virtual, and the best supportive evidence is qualitative and indirect. Conclusions The proposed model may help enhance rational decision making across the continuum of care, thereby improving patient safety globally. PMID:29168290

Utilization of a Multi-Disciplinary Approach to Building Effective Command Centers: Process and Products

DTIC Science & Technology

2005-06-01

cognitive task analysis , organizational information dissemination and interaction, systems engineering, collaboration and communications processes, decision-making processes, and data collection and organization. By blending these diverse disciplines command centers can be designed to support decision-making, cognitive analysis, information technology, and the human factors engineering aspects of Command and Control (C2). This model can then be used as a baseline when dealing with work in areas of business processes, workflow engineering, information management,

Multi Criteria Evaluation Module for RiskChanges Spatial Decision Support System

NASA Astrophysics Data System (ADS)

Olyazadeh, Roya; Jaboyedoff, Michel; van Westen, Cees; Bakker, Wim

2015-04-01

Multi-Criteria Evaluation (MCE) module is one of the five modules of RiskChanges spatial decision support system. RiskChanges web-based platform aims to analyze changes in hydro-meteorological risk and provides tools for selecting the best risk reduction alternative. It is developed under CHANGES framework (changes-itn.eu) and INCREO project (increo-fp7.eu). MCE tool helps decision makers and spatial planners to evaluate, sort and rank the decision alternatives. The users can choose among different indicators that are defined within the system using Risk and Cost Benefit analysis results besides they can add their own indicators. Subsequently the system standardizes and prioritizes them. Finally, the best decision alternative is selected by using the weighted sum model (WSM). The Application of this work is to facilitate the effect of MCE for analyzing changing risk over the time under different scenarios and future years by adopting a group decision making into practice and comparing the results by numeric and graphical view within the system. We believe that this study helps decision-makers to achieve the best solution by expressing their preferences for strategies under future scenarios. Keywords: Multi-Criteria Evaluation, Spatial Decision Support System, Weighted Sum Model, Natural Hazard Risk Management

Dynamic decision making for dam-break emergency management - Part 2: Application to Tangjiashan landslide dam failure

NASA Astrophysics Data System (ADS)

Peng, M.; Zhang, L. M.

2013-02-01

Tangjiashan landslide dam, which was triggered by the Ms = 8.0 Wenchuan earthquake in 2008 in China, threatened 1.2 million people downstream of the dam. All people in Beichuan Town 3.5 km downstream of the dam and 197 thousand people in Mianyang City 85 km downstream of the dam were evacuated 10 days before the breaching of the dam. Making such an important decision under uncertainty was difficult. This paper applied a dynamic decision-making framework for dam-break emergency management (DYDEM) to help rational decision in the emergency management of the Tangjiashan landslide dam. Three stages are identified with different levels of hydrological, geological and social-economic information along the timeline of the landslide dam failure event. The probability of dam failure is taken as a time series. The dam breaching parameters are predicted with a set of empirical models in stage 1 when no soil property information is known, and a physical model in stages 2 and 3 when knowledge of soil properties has been obtained. The flood routing downstream of the dam in these three stages is analyzed to evaluate the population at risk (PAR). The flood consequences, including evacuation costs, flood damage and monetized loss of life, are evaluated as functions of warning time using a human risk analysis model based on Bayesian networks. Finally, dynamic decision analysis is conducted to find the optimal time to evacuate the population at risk with minimum total loss in each of these three stages.

Knowledge Co-production Strategies for Water Resources Modeling and Decision Making

NASA Astrophysics Data System (ADS)

Gober, P.

2016-12-01

The limited impact of scientific information on policy making and climate adaptation in North America has raised awareness of the need for new modeling strategies and knowledge transfer processes. This paper outlines the rationale for a new paradigm in water resources modeling and management, using examples from the USA and Canada. Principles include anticipatory modeling, complex system dynamics, decision making under uncertainty, visualization, capacity to represent and manipulate critical trade-offs, stakeholder engagement, local knowledge, context-specific activities, social learning, vulnerability analysis, iterative and collaborative modeling, and the concept of a boundary organization. In this framework, scientists and stakeholders are partners in the production and dissemination of knowledge for decision making, and local knowledge is fused with scientific observation and methodology. Discussion draws from experience in building long-term collaborative boundary organizations in Phoenix, Arizona in the USA and the Saskatchewan River Basin (SRB) in Canada. Examples of boundary spanning activities include the use of visualization, the concept of a decision theater, infrastructure to support social learning, social networks, and reciprocity, simulation modeling to explore "what if" scenarios of the future, surveys to elicit how water problems are framed by scientists and stakeholders, and humanistic activities (theatrical performances, art exhibitions, etc.) to draw attention to local water issues. The social processes surrounding model development and dissemination are at least as important as modeling assumptions, procedures, and results in determining whether scientific knowledge will be used effectively for water resources decision making.

Walking the line: Understanding pedestrian behaviour and risk at rail level crossings with cognitive work analysis.

PubMed

Read, Gemma J M; Salmon, Paul M; Lenné, Michael G; Stanton, Neville A

2016-03-01

Pedestrian fatalities at rail level crossings (RLXs) are a public safety concern for governments worldwide. There is little literature examining pedestrian behaviour at RLXs and no previous studies have adopted a formative approach to understanding behaviour in this context. In this article, cognitive work analysis is applied to understand the constraints that shape pedestrian behaviour at RLXs in Melbourne, Australia. The five phases of cognitive work analysis were developed using data gathered via document analysis, behavioural observation, walk-throughs and critical decision method interviews. The analysis demonstrates the complex nature of pedestrian decision making at RLXs and the findings are synthesised to provide a model illustrating the influences on pedestrian decision making in this context (i.e. time, effort and social pressures). Further, the CWA outputs are used to inform an analysis of the risks to safety associated with pedestrian behaviour at RLXs and the identification of potential interventions to reduce risk. Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Biological Event Modeling for Response Planning

NASA Astrophysics Data System (ADS)

McGowan, Clement; Cecere, Fred; Darneille, Robert; Laverdure, Nate

People worldwide continue to fear a naturally occurring or terrorist-initiated biological event. Responsible decision makers have begun to prepare for such a biological event, but critical policy and system questions remain: What are the best courses of action to prepare for and react to such an outbreak? Where resources should be stockpiled? How many hospital resources—doctors, nurses, intensive-care beds—will be required? Will quarantine be necessary? Decision analysis tools, particularly modeling and simulation, offer ways to address and help answer these questions.

A Statistical Analysis Of Construction Equipment Repair Costs Using Field Data & The Cumulative Cost Model.

DTIC Science & Technology

1998-04-28

be discussed. 2.1 ECONOMIC REPLACEMENT THEORY Decisions about heavy equipment should be made based on sound economic principles , not emotions...Life) will be less than L*. The converse is also true. 2.1.3 The Repair Limit Theory A different way of looking at the economic replacement decision...Summary Three different economic models have been reviewed in this section. The output of each is distinct. One seeks to minimize costs, one seeks to

MODELS-3 INSTALLATION PROCEDURES FOR A PC WITH AN NT OPERATING SYSTEM (MODELS-3 VERSION 4.0)

EPA Science Inventory

Models-3 is a flexible software system designed to simplify the development and use of air quality models and other environmental decision support tools. It is designed for applications ranging from regulatory and policy analysis to understanding the complex interactions of at...

MODELS-3 INSTALLATION PROCEDURES FOR A PERSONAL COMPUTER WITH A NT OPERATING SYSTEM (MODELS-3 VERSION 4.1)

EPA Science Inventory

Models-3 is a flexible system designed to simplify the development and use of air quality models and other environmental decision support tools. It is designed for applications ranging from regulatory and policy analysis to understanding the complex interactions of atmospheric...

Forecasting of Seasonal Rainfall using ENSO and IOD teleconnection with Classification Models

NASA Astrophysics Data System (ADS)

De Silva, T.; Hornberger, G. M.

2017-12-01

Seasonal to annual forecasts of precipitation patterns are very important for water infrastructure management. In particular, such forecasts can be used to inform decisions about the operation of multipurpose reservoir systems in the face of changing climate conditions. Success in making useful forecasts often is achieved by considering climate teleconnections such as the El-Nino-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) as related to sea surface temperature variations. We present an analysis to explore the utility of using rainfall relationships in Sri Lanka with ENSO and IOD to predict rainfall to the Mahaweli, river basin. Forecasting of rainfall as classes - above normal, normal, and below normal - can be useful for water resource management decision making. Quadratic discrimination analysis (QDA) and random forest models are used to identify the patterns of rainfall classes with respect to ENSO and IOD indices. These models can be used to forecast the likelihood of areal rainfall anomalies using predicted climate indices. Results can be used for decisions regarding allocation of water for agriculture and electricity generation within the Mahaweli project of Sri Lanka.

Modeling new coal projects: supercritical or subcritical?

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

Carrino, A.J.; Jones, R.B.

Decisions made on new build coal-fired plants are driven by several factors - emissions, fuel logistics and electric transmission access all provide constraints. The crucial economic decision whether to build supercritical or subcritical units often depends on assumptions concerning the reliability/availability of each technology, the cost of on-fuel operations including maintenance, the generation efficiencies and the potential for emissions credits at some future value. Modeling the influence of these key factors requires analysis and documentation to assure the assets actually meet the projected financial performance. This article addresses some of the issue related to the trade-offs that have the potentialmore » to be driven by the supercritical/subcritical decision. Solomon Associates has been collecting cost, generation and reliability data on coal-fired power generation assets for approximately 10 years using a strict methodology and taxonomy to categorize and compare actual plant operations data. This database provides validated information not only on performance, but also on alternative performance scenarios, which can provide useful insights in the pro forma financial analysis and models of new plants. 1 ref., 1 fig., 3 tabs.« less

Brain network response underlying decisions about abstract reinforcers.

PubMed

Mills-Finnerty, Colleen; Hanson, Catherine; Hanson, Stephen Jose

2014-12-01

Decision making studies typically use tasks that involve concrete action-outcome contingencies, in which subjects do something and get something. No studies have addressed decision making involving abstract reinforcers, where there are no action-outcome contingencies and choices are entirely hypothetical. The present study examines these kinds of choices, as well as whether the same biases that exist for concrete reinforcer decisions, specifically framing effects, also apply during abstract reinforcer decisions. We use both General Linear Model as well as Bayes network connectivity analysis using the Independent Multi-sample Greedy Equivalence Search (IMaGES) algorithm to examine network response underlying choices for abstract reinforcers under positive and negative framing. We find for the first time that abstract reinforcer decisions activate the same network of brain regions as concrete reinforcer decisions, including the striatum, insula, anterior cingulate, and VMPFC, results that are further supported via comparison to a meta-analysis of decision making studies. Positive and negative framing activated different parts of this network, with stronger activation in VMPFC during negative framing and in DLPFC during positive, suggesting different decision making pathways depending on frame. These results were further clarified using connectivity analysis, which revealed stronger connections between anterior cingulate, insula, and accumbens during negative framing compared to positive. Taken together, these results suggest that not only do abstract reinforcer decisions rely on the same brain substrates as concrete reinforcers, but that the response underlying framing effects on abstract reinforcers also resemble those for concrete reinforcers, specifically increased limbic system connectivity during negative frames. Copyright © 2014 Elsevier Inc. All rights reserved.

Participation in Decision Making as a Property of Complex Adaptive Systems: Developing and Testing a Measure

PubMed Central

Anderson, Ruth A.; Hsieh, Pi-Ching; Su, Hui Fang; Landerman, Lawrence R.; McDaniel, Reuben R.

2013-01-01

Objectives. To (1) describe participation in decision-making as a systems-level property of complex adaptive systems and (2) present empirical evidence of reliability and validity of a corresponding measure. Method. Study 1 was a mail survey of a single respondent (administrators or directors of nursing) in each of 197 nursing homes. Study 2 was a field study using random, proportionally stratified sampling procedure that included 195 organizations with 3,968 respondents. Analysis. In Study 1, we analyzed the data to reduce the number of scale items and establish initial reliability and validity. In Study 2, we strengthened the psychometric test using a large sample. Results. Results demonstrated validity and reliability of the participation in decision-making instrument (PDMI) while measuring participation of workers in two distinct job categories (RNs and CNAs). We established reliability at the organizational level aggregated items scores. We established validity of the multidimensional properties using convergent and discriminant validity and confirmatory factor analysis. Conclusions. Participation in decision making, when modeled as a systems-level property of organization, has multiple dimensions and is more complex than is being traditionally measured. Managers can use this model to form decision teams that maximize the depth and breadth of expertise needed and to foster connection among them. PMID:24349771

Participation in decision making as a property of complex adaptive systems: developing and testing a measure.

PubMed

Anderson, Ruth A; Plowman, Donde; Corazzini, Kirsten; Hsieh, Pi-Ching; Su, Hui Fang; Landerman, Lawrence R; McDaniel, Reuben R

2013-01-01

Objectives. To (1) describe participation in decision-making as a systems-level property of complex adaptive systems and (2) present empirical evidence of reliability and validity of a corresponding measure. Method. Study 1 was a mail survey of a single respondent (administrators or directors of nursing) in each of 197 nursing homes. Study 2 was a field study using random, proportionally stratified sampling procedure that included 195 organizations with 3,968 respondents. Analysis. In Study 1, we analyzed the data to reduce the number of scale items and establish initial reliability and validity. In Study 2, we strengthened the psychometric test using a large sample. Results. Results demonstrated validity and reliability of the participation in decision-making instrument (PDMI) while measuring participation of workers in two distinct job categories (RNs and CNAs). We established reliability at the organizational level aggregated items scores. We established validity of the multidimensional properties using convergent and discriminant validity and confirmatory factor analysis. Conclusions. Participation in decision making, when modeled as a systems-level property of organization, has multiple dimensions and is more complex than is being traditionally measured. Managers can use this model to form decision teams that maximize the depth and breadth of expertise needed and to foster connection among them.

An approach to and web-based tool for infectious disease outbreak intervention analysis

NASA Astrophysics Data System (ADS)

Daughton, Ashlynn R.; Generous, Nicholas; Priedhorsky, Reid; Deshpande, Alina

2017-04-01

Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a subjective process involving surveillance and expert opinion. However, there are many situations where neither may be available. Modeling can fill gaps in the decision making process by using available data to provide quantitative estimates of outbreak trajectories. Effective reduction of the spread of infectious diseases can be achieved through collaboration between the modeling community and public health policy community. However, such collaboration is rare, resulting in a lack of models that meet the needs of the public health community. Here we show a Susceptible-Infectious-Recovered (SIR) model modified to include control measures that allows parameter ranges, rather than parameter point estimates, and includes a web user interface for broad adoption. We apply the model to three diseases, measles, norovirus and influenza, to show the feasibility of its use and describe a research agenda to further promote interactions between decision makers and the modeling community.

Carbon Cycle Science in Support of Decision-Making

NASA Astrophysics Data System (ADS)

Brown, M. E.; West, T. O.; McGlynn, E.; Gurwick, N. P.; Duren, R. M.; Ocko, I.; Paustian, K.

2016-12-01

There has been an extensive amount of basic and applied research conducted on biogeochemical cycles, land cover change, watershed to earth system modeling, climate change, and energy efficiency. Concurrently, there continues to be interest in how to best reduce net carbon emissions, including maintaining or augmenting global carbon stocks and decreasing fossil fuel emissions. Decisions surrounding reductions in net emissions should be grounded in, and informed by, existing scientific knowledge and analyses in order to be most effective. The translation of scientific research to decision-making is rarely direct, and often requires coordination of objectives or intermediate research steps. For example, complex model output may need to be simplified to provide mean estimates for given activities; biogeochemical models used for climate change prediction may need to be altered to estimate net carbon flux associated with particular activities; or scientific analyses may need to aggregate and analyze data in a different manner to address specific questions. In the aforementioned cases, expertise and capabilities of researchers and decision-makers are both needed, and early coordination and communication is most effective. Initial analysis of existing science and current decision-making needs indicate that (a) knowledge that is co-produced by scientists and decision-makers has a higher probability of being usable for decision making, (b) scientific work in the past decade to integrate activity data into models has resulted in more usable information for decision makers, (c) attribution and accounting of carbon cycle fluxes is key to using carbon cycle science for decision-making, and (d) stronger, long-term links among research on climate and management of carbon-related sectors (e.g., energy, land use, industry, and buildings) are needed to adequately address current issues.

Traffic analysis toolbox volume IX : work zone modeling and simulation, a guide for analysts

DOT National Transportation Integrated Search

2009-03-01

This document is the second volume in the FHWA Traffic Analysis Toolbox: Work Zone Analysis series. Whereas the first volume provides guidance to decision-makers at agencies and jurisdictions considering the role of analytical tools in work zone plan...

The chronic care model versus disease management programs: a transaction cost analysis approach.

PubMed

Leeman, Jennifer; Mark, Barbara

2006-01-01

The present article applies transaction cost analysis as a framework for better understanding health plans' decisions to improve chronic illness management by using disease management programs versus redesigning care within physician practices.

Usage Intention Framework Model: A Fuzzy Logic Interpretation of the Classical Utaut Model

ERIC Educational Resources Information Center

Sandaire, Johnny

2009-01-01

A fuzzy conjoint analysis (FCA: Turksen, 1992) model for enhancing management decision in the technology adoption domain was implemented as an extension to the UTAUT model (Venkatesh, Morris, Davis, & Davis, 2003). Additionally, a UTAUT-based Usage Intention Framework Model (UIFM) introduced a closed-loop feedback system. The empirical evidence…

A multivariate decision tree analysis of biophysical factors in tropical forest fire occurrence

Treesearch

Rey S. Ofren; Edward Harvey

2000-01-01

A multivariate decision tree model was used to quantify the relative importance of complex hierarchical relationships between biophysical variables and the occurrence of tropical forest fires. The study site is the Huai Kha Kbaeng wildlife sanctuary, a World Heritage Site in northwestern Thailand where annual fires are common and particularly destructive. Thematic...

"Black Magic" and "Gold Dust": The Epistemic and Political Uses of Evidence Tools in Public Health Policy Making

ERIC Educational Resources Information Center

Stewart, Ellen; Smith, Katherine E.

2015-01-01

Concerns about the limited influence of research on decision making have prompted the development of tools intended to mediate evidence for policy audiences. This article focuses on three examples, prominent in public health: impact assessments; systematic reviews; and economic decision-making tools (cost-benefit analysis and scenario modelling).…

Multiple Forensic Interviews during Investigations of Child Sexual Abuse: A Cost-Effectiveness Analysis

ERIC Educational Resources Information Center

Block, Stephanie D.; Foster, E. Michael; Pierce, Matthew W.; Berkoff, Molly C.; Runyan, Desmond K.

2013-01-01

In suspected child sexual abuse some professionals recommend multiple child interviews to increase the likelihood of disclosure or more details to improve decision-making and increase convictions. We modeled the yield of a policy of routinely conducting multiple child interviews and increased convictions. Our decision tree reflected the path of a…

Valuing in Decision-Making Ability: Teaching, Learning, and Assessment across the Curriculum and Campus Culture at Alverno College

ERIC Educational Resources Information Center

Eastberg, Jodi R. B.

2011-01-01

For the past 40 years, Alverno College faculty, staff, and students have collaborated in the creation of an integrated learning and assessment model that requires students to demonstrate, and faculty to assess, eight core abilities: Communication, Analysis, Problem Solving, Valuing in Decision-Making, Social Interaction, Developing a Global…

Exposure Models for the Prior Distribution in Bayesian Decision Analysis for Occupational Hygiene Decision Making

PubMed Central

Lee, Eun Gyung; Kim, Seung Won; Feigley, Charles E.; Harper, Martin

2015-01-01

This study introduces two semi-quantitative methods, Structured Subjective Assessment (SSA) and Control of Substances Hazardous to Health (COSHH) Essentials, in conjunction with two-dimensional Monte Carlo simulations for determining prior probabilities. Prior distribution using expert judgment was included for comparison. Practical applications of the proposed methods were demonstrated using personal exposure measurements of isoamyl acetate in an electronics manufacturing facility and of isopropanol in a printing shop. Applicability of these methods in real workplaces was discussed based on the advantages and disadvantages of each method. Although these methods could not be completely independent of expert judgments, this study demonstrated a methodological improvement in the estimation of the prior distribution for the Bayesian decision analysis tool. The proposed methods provide a logical basis for the decision process by considering determinants of worker exposure. PMID:23252451

Toward an operational model of decision making, emotional regulation, and mental health impact.

PubMed

Collura, Thomas Francis; Zalaquett, Ronald P; Bonnstetter, Carlos Joyce; Chatters, Seria J

2014-01-01

Current brain research increasingly reveals the underlying mechanisms and processes of human behavior, cognition, and emotion. In addition to being of interest to a wide range of scientists, educators, and professionals, as well as laypeople, brain-based models are of particular value in a clinical setting. Psychiatrists, psychologists, counselors, and other mental health professionals are in need of operational models that integrate recent findings in the physical, cognitive, and emotional domains, and offer a common language for interdisciplinary understanding and communication. Based on individual traits, predispositions, and responses to stimuli, we can begin to identify emotional and behavioral pathways and mental processing patterns. The purpose of this article is to present a brain-path activation model to understand individual differences in decision making and psychopathology. The first section discusses the role of frontal lobe electroencephalography (EEG) asymmetry, summarizes state- and trait-based models of decision making, and provides a more complex analysis that supplements the traditional simple left-right brain model. Key components of the new model are the introduction of right hemisphere parallel and left hemisphere serial scanning in rendering decisions, and the proposition of pathways that incorporate both past experiences as well as future implications into the decision process. Main attributes of each decision-making mechanism are provided. The second section applies the model within the realm of clinical mental health as a tool to understand specific human behavior and pathology. Applications include general and chronic anxiety, depression, paranoia, risk taking, and the pathways employed when well-functioning operational integration is observed. Finally, specific applications such as meditation and mindfulness are offered to facilitate positive functioning.

Multiscale modelling and analysis of collective decision making in swarm robotics.

PubMed

Vigelius, Matthias; Meyer, Bernd; Pascoe, Geoffrey

2014-01-01

We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process. Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable.

QTest: Quantitative Testing of Theories of Binary Choice

PubMed Central

Regenwetter, Michel; Davis-Stober, Clintin P.; Lim, Shiau Hong; Guo, Ying; Popova, Anna; Zwilling, Chris; Cha, Yun-Shil; Messner, William

2014-01-01

The goal of this paper is to make modeling and quantitative testing accessible to behavioral decision researchers interested in substantive questions. We provide a novel, rigorous, yet very general, quantitative diagnostic framework for testing theories of binary choice. This permits the nontechnical scholar to proceed far beyond traditionally rather superficial methods of analysis, and it permits the quantitatively savvy scholar to triage theoretical proposals before investing effort into complex and specialized quantitative analyses. Our theoretical framework links static algebraic decision theory with observed variability in behavioral binary choice data. The paper is supplemented with a custom-designed public-domain statistical analysis package, the QTest software. We illustrate our approach with a quantitative analysis using published laboratory data, including tests of novel versions of “Random Cumulative Prospect Theory.” A major asset of the approach is the potential to distinguish decision makers who have a fixed preference and commit errors in observed choices from decision makers who waver in their preferences. PMID:24999495

MRI-based decision tree model for diagnosis of biliary atresia.

PubMed

Kim, Yong Hee; Kim, Myung-Joon; Shin, Hyun Joo; Yoon, Haesung; Han, Seok Joo; Koh, Hong; Roh, Yun Ho; Lee, Mi-Jung

2018-02-23

To evaluate MRI findings and to generate a decision tree model for diagnosis of biliary atresia (BA) in infants with jaundice. We retrospectively reviewed features of MRI and ultrasonography (US) performed in infants with jaundice between January 2009 and June 2016 under approval of the institutional review board, including the maximum diameter of periportal signal change on MRI (MR triangular cord thickness, MR-TCT) or US (US-TCT), visibility of common bile duct (CBD) and abnormality of gallbladder (GB). Hepatic subcapsular flow was reviewed on Doppler US. We performed conditional inference tree analysis using MRI findings to generate a decision tree model. A total of 208 infants were included, 112 in the BA group and 96 in the non-BA group. Mean age at the time of MRI was 58.7 ± 36.6 days. Visibility of CBD, abnormality of GB and MR-TCT were good discriminators for the diagnosis of BA and the MRI-based decision tree using these findings with MR-TCT cut-off 5.1 mm showed 97.3 % sensitivity, 94.8 % specificity and 96.2 % accuracy. MRI-based decision tree model reliably differentiates BA in infants with jaundice. MRI can be an objective imaging modality for the diagnosis of BA. • MRI-based decision tree model reliably differentiates biliary atresia in neonatal cholestasis. • Common bile duct, gallbladder and periportal signal changes are the discriminators. • MRI has comparable performance to ultrasonography for diagnosis of biliary atresia.

Training Decisions Technology Analysis

DTIC Science & Technology

1992-06-01

4.5.1 Relational Data Base Management 69 4.5.2 TASCS Data Content 69 4.5.3 Relationships with TDS 69 4.6 Other Air Force Modeling R&D 70 4.6.1 Time ...executive decision making were first developed by M. S. Scott Morton in the early 1970’s who, at that time , termed them " management decision systems" (Scott...Allocations to Training Settings o Managers ’ Preferences for Task Allocations to Training Settings o Times Required to Training Tasks in Various

Multi-criteria decision analysis using hydrological indicators for decision support - a conceptual framework.

NASA Astrophysics Data System (ADS)

Butchart-Kuhlmann, Daniel; Kralisch, Sven; Meinhardt, Markus; Fleischer, Melanie

2017-04-01

Assessing the quantity and quality of water available in water stressed environments under various potential climate and land-use changes is necessary for good water and environmental resources management and governance. Within the region covered by the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) project, such areas are common. One goal of the SASSCAL project is to develop and provide an integrated decision support system (DSS) with which decision makers (DMs) within a given catchment can obtain objective information regarding potential changes in water flow quantity and timing. The SASSCAL DSS builds upon existing data storage and distribution capability, through the SASSCAL Information System (IS), as well as the J2000 hydrological model. Using output from validated J2000 models, the SASSCAL DSS incorporates the calculation of a range of hydrological indicators based upon Indicators of Hydrological Alteration/Environmental Flow Components (IHA/EFC) calculated for a historic time series (pre-impact) and a set of model simulations based upon a selection of possible climate and land-use change scenarios (post-impact). These indicators, obtained using the IHA software package, are then used as input for a multi-criteria decision analysis (MCDA) undertaken using the open source diviz software package. The results of these analyses will provide DMs with an indication as to how various hydrological indicators within a catchment may be altered under different future scenarios, as well providing a ranking of how each scenario is preferred according to different DM preferences. Scenarios are represented through a combination of model input data and parameter settings in J2000, and preferences are represented through criteria weighting in the MCDA. Here, the methodology is presented and applied to the J2000 Luanginga model results using a set of hypothetical decision maker preference values as input for an MCDA based on the PROMETHEE II outranking method. Future work on the SASSCAL DSS will entail automation of this process, as well as its application to other hydrological models and land-use and/or climate change scenarios.