Optimization and resilience in natural resources management

USGS Publications Warehouse

Williams, Byron K.; Johnson, Fred A.

2015-01-01

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

Marine Steam Condenser Design Optimization.

DTIC Science & Technology

1983-12-01

to make design decisions to obtain a feasible design. CONNIN, as do most optimizers, requires complete control in determining all iterative design...neutralize all the places where such design decisions are made. By removing the ability for CONDIP to make any design decisions it became totally passive...dependent on CONNIN for design decisions , does not have that capability. Pemeabering that CONHIN requires a complete once-through analysis in order to

Age Effects and Heuristics in Decision Making*

PubMed Central

Besedeš, Tibor; Deck, Cary; Sarangi, Sudipta; Shor, Mikhael

2011-01-01

Using controlled experiments, we examine how individuals make choices when faced with multiple options. Choice tasks are designed to mimic the selection of health insurance, prescription drug, or retirement savings plans. In our experiment, available options can be objectively ranked allowing us to examine optimal decision making. First, the probability of a person selecting the optimal option declines as the number of options increases, with the decline being more pronounced for older subjects. Second, heuristics differ by age with older subjects relying more on suboptimal decision rules. In a heuristics validation experiment, older subjects make worse decisions than younger subjects. PMID:22544977

Age Effects and Heuristics in Decision Making.

PubMed

Besedeš, Tibor; Deck, Cary; Sarangi, Sudipta; Shor, Mikhael

2012-05-01

Using controlled experiments, we examine how individuals make choices when faced with multiple options. Choice tasks are designed to mimic the selection of health insurance, prescription drug, or retirement savings plans. In our experiment, available options can be objectively ranked allowing us to examine optimal decision making. First, the probability of a person selecting the optimal option declines as the number of options increases, with the decline being more pronounced for older subjects. Second, heuristics differ by age with older subjects relying more on suboptimal decision rules. In a heuristics validation experiment, older subjects make worse decisions than younger subjects.

Globally optimal trial design for local decision making.

PubMed

Eckermann, Simon; Willan, Andrew R

2009-02-01

Value of information methods allows decision makers to identify efficient trial design following a principle of maximizing the expected value to decision makers of information from potential trial designs relative to their expected cost. However, in health technology assessment (HTA) the restrictive assumption has been made that, prospectively, there is only expected value of sample information from research commissioned within jurisdiction. This paper extends the framework for optimal trial design and decision making within jurisdiction to allow for optimal trial design across jurisdictions. This is illustrated in identifying an optimal trial design for decision making across the US, the UK and Australia for early versus late external cephalic version for pregnant women presenting in the breech position. The expected net gain from locally optimal trial designs of US$0.72M is shown to increase to US$1.14M with a globally optimal trial design. In general, the proposed method of globally optimal trial design improves on optimal trial design within jurisdictions by: (i) reflecting the global value of non-rival information; (ii) allowing optimal allocation of trial sample across jurisdictions; (iii) avoiding market failure associated with free-rider effects, sub-optimal spreading of fixed costs and heterogeneity of trial information with multiple trials. Copyright (c) 2008 John Wiley & Sons, Ltd.

Decision theory, reinforcement learning, and the brain.

PubMed

Dayan, Peter; Daw, Nathaniel D

2008-12-01

Decision making is a core competence for animals and humans acting and surviving in environments they only partially comprehend, gaining rewards and punishments for their troubles. Decision-theoretic concepts permeate experiments and computational models in ethology, psychology, and neuroscience. Here, we review a well-known, coherent Bayesian approach to decision making, showing how it unifies issues in Markovian decision problems, signal detection psychophysics, sequential sampling, and optimal exploration and discuss paradigmatic psychological and neural examples of each problem. We discuss computational issues concerning what subjects know about their task and how ambitious they are in seeking optimal solutions; we address algorithmic topics concerning model-based and model-free methods for making choices; and we highlight key aspects of the neural implementation of decision making.

A detailed comparison of optimality and simplicity in perceptual decision-making

PubMed Central

Shen, Shan; Ma, Wei Ji

2017-01-01

Two prominent ideas in the study of decision-making have been that organisms behave near-optimally, and that they use simple heuristic rules. These principles might be operating in different types of tasks, but this possibility cannot be fully investigated without a direct, rigorous comparison within a single task. Such a comparison was lacking in most previous studies, because a) the optimal decision rule was simple; b) no simple suboptimal rules were considered; c) it was unclear what was optimal, or d) a simple rule could closely approximate the optimal rule. Here, we used a perceptual decision-making task in which the optimal decision rule is well-defined and complex, and makes qualitatively distinct predictions from many simple suboptimal rules. We find that all simple rules tested fail to describe human behavior, that the optimal rule accounts well for the data, and that several complex suboptimal rules are indistinguishable from the optimal one. Moreover, we found evidence that the optimal model is close to the true model: first, the better the trial-to-trial predictions of a suboptimal model agree with those of the optimal model, the better that suboptimal model fits; second, our estimate of the Kullback-Leibler divergence between the optimal model and the true model is not significantly different from zero. When observers receive no feedback, the optimal model still describes behavior best, suggesting that sensory uncertainty is implicitly represented and taken into account. Beyond the task and models studied here, our results have implications for best practices of model comparison. PMID:27177259

Decision-Making Strategies for College Students

ERIC Educational Resources Information Center

Morey, Janis T.; Dansereau, Donald F.

2010-01-01

College students' decision making is often less than optimal and sometimes leads to negative consequences. The effectiveness of two strategies for improving student decision making--node-link mapping and social perspective taking (SPT)--are examined. Participants using SPT were significantly better able to evaluate decision options and develop…

Incentives for Optimal Multi-level Allocation of HIV Prevention Resources

PubMed Central

Malvankar, Monali M.; Zaric, Gregory S.

2013-01-01

HIV/AIDS prevention funds are often allocated at multiple levels of decision-making. Optimal allocation of HIV prevention funds maximizes the number of HIV infections averted. However, decision makers often allocate using simple heuristics such as proportional allocation. We evaluate the impact of using incentives to encourage optimal allocation in a two-level decision-making process. We model an incentive based decision-making process consisting of an upper-level decision maker allocating funds to a single lower-level decision maker who then distributes funds to local programs. We assume that the lower-level utility function is linear in the amount of the budget received from the upper-level, the fraction of funds reserved for proportional allocation, and the number of infections averted. We assume that the upper level objective is to maximize the number of infections averted. We illustrate with an example using data from California, U.S. PMID:23766551

Optimal decision making on the basis of evidence represented in spike trains.

PubMed

Zhang, Jiaxiang; Bogacz, Rafal

2010-05-01

Experimental data indicate that perceptual decision making involves integration of sensory evidence in certain cortical areas. Theoretical studies have proposed that the computation in neural decision circuits approximates statistically optimal decision procedures (e.g., sequential probability ratio test) that maximize the reward rate in sequential choice tasks. However, these previous studies assumed that the sensory evidence was represented by continuous values from gaussian distributions with the same variance across alternatives. In this article, we make a more realistic assumption that sensory evidence is represented in spike trains described by the Poisson processes, which naturally satisfy the mean-variance relationship observed in sensory neurons. We show that for such a representation, the neural circuits involving cortical integrators and basal ganglia can approximate the optimal decision procedures for two and multiple alternative choice tasks.

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

PubMed

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

2016-04-28

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

The Linear Quadratic Gaussian Multistage Game with Nonclassical Information Pattern Using a Direct Solution Method

NASA Astrophysics Data System (ADS)

Clemens, Joshua William

Game theory has application across multiple fields, spanning from economic strategy to optimal control of an aircraft and missile on an intercept trajectory. The idea of game theory is fascinating in that we can actually mathematically model real-world scenarios and determine optimal decision making. It may not always be easy to mathematically model certain real-world scenarios, nonetheless, game theory gives us an appreciation for the complexity involved in decision making. This complexity is especially apparent when the players involved have access to different information upon which to base their decision making (a nonclassical information pattern). Here we will focus on the class of adversarial two-player games (sometimes referred to as pursuit-evasion games) with nonclassical information pattern. We present a two-sided (simultaneous) optimization solution method for the two-player linear quadratic Gaussian (LQG) multistage game. This direct solution method allows for further interpretation of each player's decision making (strategy) as compared to previously used formal solution methods. In addition to the optimal control strategies, we present a saddle point proof and we derive an expression for the optimal performance index value. We provide some numerical results in order to further interpret the optimal control strategies and to highlight real-world application of this game-theoretic optimal solution.

Distinct Roles of Dopamine and Subthalamic Nucleus in Learning and Probabilistic Decision Making

ERIC Educational Resources Information Center

Coulthard, Elizabeth J.; Bogacz, Rafal; Javed, Shazia; Mooney, Lucy K.; Murphy, Gillian; Keeley, Sophie; Whone, Alan L.

2012-01-01

Even simple behaviour requires us to make decisions based on combining multiple pieces of learned and new information. Making such decisions requires both learning the optimal response to each given stimulus as well as combining probabilistic information from multiple stimuli before selecting a response. Computational theories of decision making…

Adaptive sampling of information in perceptual decision-making.

PubMed

Cassey, Thomas C; Evens, David R; Bogacz, Rafal; Marshall, James A R; Ludwig, Casimir J H

2013-01-01

In many perceptual and cognitive decision-making problems, humans sample multiple noisy information sources serially, and integrate the sampled information to make an overall decision. We derive the optimal decision procedure for two-alternative choice tasks in which the different options are sampled one at a time, sources vary in the quality of the information they provide, and the available time is fixed. To maximize accuracy, the optimal observer allocates time to sampling different information sources in proportion to their noise levels. We tested human observers in a corresponding perceptual decision-making task. Observers compared the direction of two random dot motion patterns that were triggered only when fixated. Observers allocated more time to the noisier pattern, in a manner that correlated with their sensory uncertainty about the direction of the patterns. There were several differences between the optimal observer predictions and human behaviour. These differences point to a number of other factors, beyond the quality of the currently available sources of information, that influences the sampling strategy.

Goal-Directed Decision Making with Spiking Neurons.

PubMed

Friedrich, Johannes; Lengyel, Máté

2016-02-03

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. Copyright © 2016 the authors 0270-6474/16/361529-18$15.00/0.

Goal-Directed Decision Making with Spiking Neurons

PubMed Central

Lengyel, Máté

2016-01-01

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. SIGNIFICANCE STATEMENT Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. PMID:26843636

"Utilizing" signal detection theory.

PubMed

Lynn, Spencer K; Barrett, Lisa Feldman

2014-09-01

What do inferring what a person is thinking or feeling, judging a defendant's guilt, and navigating a dimly lit room have in common? They involve perceptual uncertainty (e.g., a scowling face might indicate anger or concentration, for which different responses are appropriate) and behavioral risk (e.g., a cost to making the wrong response). Signal detection theory describes these types of decisions. In this tutorial, we show how incorporating the economic concept of utility allows signal detection theory to serve as a model of optimal decision making, going beyond its common use as an analytic method. This utility approach to signal detection theory clarifies otherwise enigmatic influences of perceptual uncertainty on measures of decision-making performance (accuracy and optimality) and on behavior (an inverse relationship between bias magnitude and sensitivity optimizes utility). A "utilized" signal detection theory offers the possibility of expanding the phenomena that can be understood within a decision-making framework. © The Author(s) 2014.

Optimal Decision Making in Neural Inhibition Models

ERIC Educational Resources Information Center

van Ravenzwaaij, Don; van der Maas, Han L. J.; Wagenmakers, Eric-Jan

2012-01-01

In their influential "Psychological Review" article, Bogacz, Brown, Moehlis, Holmes, and Cohen (2006) discussed optimal decision making as accomplished by the drift diffusion model (DDM). The authors showed that neural inhibition models, such as the leaky competing accumulator model (LCA) and the feedforward inhibition model (FFI), can mimic the…

Organizational Decision Making

DTIC Science & Technology

1975-08-01

the lack of formal techniques typically used by large organizations, digress on the advantages of formal over informal... optimization ; for example one might do a number of optimization calculations, each time using a different measure of effectiveness as the optimized ...final decision. The next level of computer application involves the use of computerized optimization techniques. Optimization

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

DECISION-MAKING IN THE SCHOOLS: AN OUTSIDER’S VIEW,

DTIC Science & Technology

DECISION MAKING , EDUCATION), (*EDUCATION, MANAGEMENT PLANNING AND CONTROL), (*MANAGEMENT PLANNING AND CONTROL, EDUCATION), BUDGETS, MANAGEMENT ENGINEERING, PERSONNEL MANAGEMENT, STUDENTS, LEARNING, OPTIMIZATION

Who Chokes Under Pressure? The Big Five Personality Traits and Decision-Making under Pressure.

PubMed

Byrne, Kaileigh A; Silasi-Mansat, Crina D; Worthy, Darrell A

2015-02-01

The purpose of the present study was to examine whether the Big Five personality factors could predict who thrives or chokes under pressure during decision-making. The effects of the Big Five personality factors on decision-making ability and performance under social (Experiment 1) and combined social and time pressure (Experiment 2) were examined using the Big Five Personality Inventory and a dynamic decision-making task that required participants to learn an optimal strategy. In Experiment 1, a hierarchical multiple regression analysis showed an interaction between neuroticism and pressure condition. Neuroticism negatively predicted performance under social pressure, but did not affect decision-making under low pressure. Additionally, the negative effect of neuroticism under pressure was replicated using a combined social and time pressure manipulation in Experiment 2. These results support distraction theory whereby pressure taxes highly neurotic individuals' cognitive resources, leading to sub-optimal performance. Agreeableness also negatively predicted performance in both experiments.

Minimizing Uncertainties Impact in Decision Making with an Applicability Study for Economic Power Dispatch

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

Wang, Hong; Wang, Shaobu; Fan, Rui

This report summaries the work performed under the LDRD project on the preliminary study on knowledge automation, where specific focus has been made on the investigation of the impact of uncertainties of human decision making onto the optimization of the process operation. At first the statistics on signals from the Brain-Computing Interface (BCI) is analyzed so as to obtain the uncertainties characterization of human operators during the decision making phase using the electroencephalogram (EEG) signals. This is then followed by the discussions of an architecture that reveals the equivalence between optimization and closed loop feedback control design, where it hasmore » been shown that all the optimization problems can be transferred into the control design problem for closed loop systems. This has led to a “closed loop” framework, where the structure of the decision making is shown to be subjected to both process disturbances and controller’s uncertainties. The latter can well represent the uncertainties or randomness occurred during human decision making phase. As a result, a stochastic optimization problem has been formulated and a novel solution has been proposed using probability density function (PDF) shaping for both the cost function and the constraints using stochastic distribution control concept. A sufficient condition has been derived that guarantees the convergence of the optimal solution and discussions have been made for both the total probabilistic solution and chanced constrained optimization which have been well-studied in optimal power flows (OPF) area. A simple case study has been carried out for the economic dispatch of powers for a grid system when there are distributed energy resources (DERs) in the system, and encouraging results have been obtained showing that a significant savings on the generation cost can be expected.« less

Neurons in the Frontal Lobe Encode the Value of Multiple Decision Variables

PubMed Central

Kennerley, Steven W.; Dahmubed, Aspandiar F.; Lara, Antonio H.; Wallis, Jonathan D.

2009-01-01

A central question in behavioral science is how we select among choice alternatives to obtain consistently the most beneficial outcomes. Three variables are particularly important when making a decision: the potential payoff, the probability of success, and the cost in terms of time and effort. A key brain region in decision making is the frontal cortex as damage here impairs the ability to make optimal choices across a range of decision types. We simultaneously recorded the activity of multiple single neurons in the frontal cortex while subjects made choices involving the three aforementioned decision variables. This enabled us to contrast the relative contribution of the anterior cingulate cortex (ACC), the orbito-frontal cortex, and the lateral prefrontal cortex to the decision-making process. Neurons in all three areas encoded value relating to choices involving probability, payoff, or cost manipulations. However, the most significant signals were in the ACC, where neurons encoded multiplexed representations of the three different decision variables. This supports the notion that the ACC is an important component of the neural circuitry underlying optimal decision making. PMID:18752411

Game Theory and Risk-Based Levee System Design

NASA Astrophysics Data System (ADS)

Hui, R.; Lund, J. R.; Madani, K.

2014-12-01

Risk-based analysis has been developed for optimal levee design for economic efficiency. Along many rivers, two levees on opposite riverbanks act as a simple levee system. Being rational and self-interested, land owners on each river bank would tend to independently optimize their levees with risk-based analysis, resulting in a Pareto-inefficient levee system design from the social planner's perspective. Game theory is applied in this study to analyze decision making process in a simple levee system in which the land owners on each river bank develop their design strategies using risk-based economic optimization. For each land owner, the annual expected total cost includes expected annual damage cost and annualized construction cost. The non-cooperative Nash equilibrium is identified and compared to the social planner's optimal distribution of flood risk and damage cost throughout the system which results in the minimum total flood cost for the system. The social planner's optimal solution is not feasible without appropriate level of compensation for the transferred flood risk to guarantee and improve conditions for all parties. Therefore, cooperative game theory is then employed to develop an economically optimal design that can be implemented in practice. By examining the game in the reversible and irreversible decision making modes, the cost of decision making myopia is calculated to underline the significance of considering the externalities and evolution path of dynamic water resource problems for optimal decision making.

Optimizing Negotiation Conflict in the Cloud Service Negotiation Framework Using Probabilistic Decision Making Model

PubMed Central

Rajavel, Rajkumar; Thangarathinam, Mala

2015-01-01

Optimization of negotiation conflict in the cloud service negotiation framework is identified as one of the major challenging issues. This negotiation conflict occurs during the bilateral negotiation process between the participants due to the misperception, aggressive behavior, and uncertain preferences and goals about their opponents. Existing research work focuses on the prerequest context of negotiation conflict optimization by grouping similar negotiation pairs using distance, binary, context-dependent, and fuzzy similarity approaches. For some extent, these approaches can maximize the success rate and minimize the communication overhead among the participants. To further optimize the success rate and communication overhead, the proposed research work introduces a novel probabilistic decision making model for optimizing the negotiation conflict in the long-term negotiation context. This decision model formulates the problem of managing different types of negotiation conflict that occurs during negotiation process as a multistage Markov decision problem. At each stage of negotiation process, the proposed decision model generates the heuristic decision based on the past negotiation state information without causing any break-off among the participants. In addition, this heuristic decision using the stochastic decision tree scenario can maximize the revenue among the participants available in the cloud service negotiation framework. PMID:26543899

Optimizing Negotiation Conflict in the Cloud Service Negotiation Framework Using Probabilistic Decision Making Model.

PubMed

Rajavel, Rajkumar; Thangarathinam, Mala

2015-01-01

Optimization of negotiation conflict in the cloud service negotiation framework is identified as one of the major challenging issues. This negotiation conflict occurs during the bilateral negotiation process between the participants due to the misperception, aggressive behavior, and uncertain preferences and goals about their opponents. Existing research work focuses on the prerequest context of negotiation conflict optimization by grouping similar negotiation pairs using distance, binary, context-dependent, and fuzzy similarity approaches. For some extent, these approaches can maximize the success rate and minimize the communication overhead among the participants. To further optimize the success rate and communication overhead, the proposed research work introduces a novel probabilistic decision making model for optimizing the negotiation conflict in the long-term negotiation context. This decision model formulates the problem of managing different types of negotiation conflict that occurs during negotiation process as a multistage Markov decision problem. At each stage of negotiation process, the proposed decision model generates the heuristic decision based on the past negotiation state information without causing any break-off among the participants. In addition, this heuristic decision using the stochastic decision tree scenario can maximize the revenue among the participants available in the cloud service negotiation framework.

Advanced Information Technology in Simulation Based Life Cycle Design

NASA Technical Reports Server (NTRS)

Renaud, John E.

2003-01-01

In this research a Collaborative Optimization (CO) approach for multidisciplinary systems design is used to develop a decision based design framework for non-deterministic optimization. To date CO strategies have been developed for use in application to deterministic systems design problems. In this research the decision based design (DBD) framework proposed by Hazelrigg is modified for use in a collaborative optimization framework. The Hazelrigg framework as originally proposed provides a single level optimization strategy that combines engineering decisions with business decisions in a single level optimization. By transforming this framework for use in collaborative optimization one can decompose the business and engineering decision making processes. In the new multilevel framework of Decision Based Collaborative Optimization (DBCO) the business decisions are made at the system level. These business decisions result in a set of engineering performance targets that disciplinary engineering design teams seek to satisfy as part of subspace optimizations. The Decision Based Collaborative Optimization framework more accurately models the existing relationship between business and engineering in multidisciplinary systems design.

State dependent optimization of measurement policy

NASA Astrophysics Data System (ADS)

Konkarikoski, K.

2010-07-01

Measurements are the key to rational decision making. Measurement information generates value, when it is applied in the decision making. An investment cost and maintenance costs are associated with each component of the measurement system. Clearly, there is - under a given set of scenarios - a measurement setup that is optimal in expected (discounted) utility. This paper deals how the measurement policy optimization is affected by different system states and how this problem can be tackled.

Decision Making and Reward in Frontal Cortex

PubMed Central

Kennerley, Steven W.; Walton, Mark E.

2011-01-01

Patients with damage to the prefrontal cortex (PFC)—especially the ventral and medial parts of PFC—often show a marked inability to make choices that meet their needs and goals. These decision-making impairments often reflect both a deficit in learning concerning the consequences of a choice, as well as deficits in the ability to adapt future choices based on experienced value of the current choice. Thus, areas of PFC must support some value computations that are necessary for optimal choice. However, recent frameworks of decision making have highlighted that optimal and adaptive decision making does not simply rest on a single computation, but a number of different value computations may be necessary. Using this framework as a guide, we summarize evidence from both lesion studies and single-neuron physiology for the representation of different value computations across PFC areas. PMID:21534649

Optimization as a Reasoning Strategy for Dealing with Socioscientific Decision-Making Situations

ERIC Educational Resources Information Center

Papadouris, Nicos

2012-01-01

This paper reports on an attempt to help 12-year-old students develop a specific optimization strategy for selecting among possible solutions in socioscientific decision-making situations. We have developed teaching and learning materials for elaborating this strategy, and we have implemented them in two intact classes (N = 48). Prior to and after…

Piéron’s Law and Optimal Behavior in Perceptual Decision-Making

PubMed Central

van Maanen, Leendert; Grasman, Raoul P. P. P.; Forstmann, Birte U.; Wagenmakers, Eric-Jan

2012-01-01

Piéron’s Law is a psychophysical regularity in signal detection tasks that states that mean response times decrease as a power function of stimulus intensity. In this article, we extend Piéron’s Law to perceptual two-choice decision-making tasks, and demonstrate that the law holds as the discriminability between two competing choices is manipulated, even though the stimulus intensity remains constant. This result is consistent with predictions from a Bayesian ideal observer model. The model assumes that in order to respond optimally in a two-choice decision-making task, participants continually update the posterior probability of each response alternative, until the probability of one alternative crosses a criterion value. In addition to predictions for two-choice decision-making tasks, we extend the ideal observer model to predict Piéron’s Law in signal detection tasks. We conclude that Piéron’s Law is a general phenomenon that may be caused by optimality constraints. PMID:22232572

Neural signatures of experience-based improvements in deterministic decision-making.

PubMed

Tremel, Joshua J; Laurent, Patryk A; Wolk, David A; Wheeler, Mark E; Fiez, Julie A

2016-12-15

Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task. Behavioral measures were then analyzed with both a drift-diffusion model and a reinforcement learning model. Parameter values from each were then used as fMRI regressors to identify regions whose activity fluctuates with specific cognitive processes described by the models. The patterns of intersecting neural effects across models support two main inferences about the influence of feedback on decision-making. First, frontal, anterior insular, fusiform, and caudate nucleus regions behave like performance monitors, reflecting errors in performance predictions that signal the need for changes in control over decision-making. Second, temporoparietal, supplementary motor, and putamen regions behave like mnemonic storage sites, reflecting differences in learned item values that inform optimal decision choices. As information about optimal choices is accrued, these neural systems dynamically adjust, likely shifting the burden of decision processing from controlled performance monitoring to bottom-up, stimulus-driven choice selection. Collectively, the results provide a detailed perspective on the fundamental ability to use past experiences to improve future decisions. Copyright © 2016 Elsevier B.V. All rights reserved.

Neural signatures of experience-based improvements in deterministic decision-making

PubMed Central

Tremel, Joshua J.; Laurent, Patryk A.; Wolk, David A.; Wheeler, Mark E.; Fiez, Julie A.

2016-01-01

Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task. Behavioral measures were then analyzed with both a drift-diffusion model and a reinforcement learning model. Parameter values from each were then used as fMRI regressors to identify regions whose activity fluctuates with specific cognitive processes described by the models. The patterns of intersecting neural effects across models support two main inferences about the influence of feedback on decision-making. First, frontal, anterior insular, fusiform, and caudate nucleus regions behave like performance monitors, reflecting errors in performance predictions that signal the need for changes in control over decision-making. Second, temporoparietal, supplementary motor, and putamen regions behave like mnemonic storage sites, reflecting differences in learned item values that inform optimal decision choices. As information about optimal choices is accrued, these neural systems dynamically adjust, likely shifting the burden of decision processing from controlled performance monitoring to bottom-up, stimulus-driven choice selection. Collectively, the results provide a detailed perspective on the fundamental ability to use past experiences to improve future decisions. PMID:27523644

Confronting dynamics and uncertainty in optimal decision making for conservation

USGS Publications Warehouse

Williams, Byron K.; Johnson, Fred A.

2013-01-01

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

Shared decision-making and decision support: their role in obstetrics and gynecology.

PubMed

Tucker Edmonds, Brownsyne

2014-12-01

To discuss the role for shared decision-making in obstetrics/gynecology and to review evidence on the impact of decision aids on reproductive health decision-making. Among the 155 studies included in a 2014 Cochrane review of decision aids, 31 (29%) addressed reproductive health decisions. Although the majority did not show evidence of an effect on treatment choice, there was a greater uptake of mammography in selected groups of women exposed to decision aids compared with usual care; and a statistically significant reduction in the uptake of hormone replacement therapy among detailed decision aid users compared with simple decision aid users. Studies also found an effect on patient-centered outcomes of care, such as medication adherence, quality-of-life measures, and anxiety scores. In maternity care, only decision analysis tools affected final treatment choice, and patient-directed aids yielded no difference in planned mode of birth after cesarean. There is untapped potential for obstetricians/gynecologists to optimize decision support for reproductive health decisions. Given the limited evidence-base guiding practice, the preference-sensitive nature of reproductive health decisions, and the increase in policy efforts and financial incentives to optimize patients' satisfaction, it is increasingly important for obstetricians/gynecologists to appreciate the role of shared decision-making and decision support in providing patient-centered reproductive healthcare.

Optimal allocation model of construction land based on two-level system optimization theory

NASA Astrophysics Data System (ADS)

Liu, Min; Liu, Yanfang; Xia, Yuping; Lei, Qihong

2007-06-01

The allocation of construction land is an important task in land-use planning. Whether implementation of planning decisions is a success or not, usually depends on a reasonable and scientific distribution method. Considering the constitution of land-use planning system and planning process in China, multiple levels and multiple objective decision problems is its essence. Also, planning quantity decomposition is a two-level system optimization problem and an optimal resource allocation decision problem between a decision-maker in the topper and a number of parallel decision-makers in the lower. According the characteristics of the decision-making process of two-level decision-making system, this paper develops an optimal allocation model of construction land based on two-level linear planning. In order to verify the rationality and the validity of our model, Baoan district of Shenzhen City has been taken as a test case. Under the assistance of the allocation model, construction land is allocated to ten townships of Baoan district. The result obtained from our model is compared to that of traditional method, and results show that our model is reasonable and usable. In the end, the paper points out the shortcomings of the model and further research directions.

A decision support system using analytical hierarchy process (AHP) for the optimal environmental reclamation of an open-pit mine

NASA Astrophysics Data System (ADS)

Bascetin, A.

2007-04-01

The selection of an optimal reclamation method is one of the most important factors in open-pit design and production planning. It also affects economic considerations in open-pit design as a function of plan location and depth. Furthermore, the selection is a complex multi-person, multi-criteria decision problem. The group decision-making process can be improved by applying a systematic and logical approach to assess the priorities based on the inputs of several specialists from different functional areas within the mine company. The analytical hierarchy process (AHP) can be very useful in involving several decision makers with different conflicting objectives to arrive at a consensus decision. In this paper, the selection of an optimal reclamation method using an AHP-based model was evaluated for coal production in an open-pit coal mine located at Seyitomer region in Turkey. The use of the proposed model indicates that it can be applied to improve the group decision making in selecting a reclamation method that satisfies optimal specifications. Also, it is found that the decision process is systematic and using the proposed model can reduce the time taken to select a optimal method.

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

NASA Astrophysics Data System (ADS)

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

2014-09-01

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

Inactivation of the prelimbic or infralimbic cortex impairs decision-making in the rat gambling task.

PubMed

Zeeb, Fiona D; Baarendse, P J J; Vanderschuren, L J M J; Winstanley, Catharine A

2015-12-01

Studies employing the Iowa Gambling Task (IGT) demonstrated that areas of the frontal cortex, including the ventromedial prefrontal cortex, orbitofrontal cortex (OFC), dorsolateral prefrontal cortex, and anterior cingulate cortex (ACC), are involved in the decision-making process. However, the precise role of these regions in maintaining optimal choice is not clear. We used the rat gambling task (rGT), a rodent analogue of the IGT, to determine whether inactivation of or altered dopamine signalling within discrete cortical sub-regions disrupts decision-making. Following training on the rGT, animals were implanted with guide cannulae aimed at the prelimbic (PrL) or infralimbic (IL) cortices, the OFC, or the ACC. Prior to testing, rats received an infusion of saline or a combination of baclofen and muscimol (0.125 μg of each/side) to inactivate the region and an infusion of a dopamine D2 receptor antagonist (0, 0.1, 0.3, and 1.0 μg/side). Rats tended to increase their choice of a disadvantageous option and decrease their choice of the optimal option following inactivation of either the IL or PrL cortex. In contrast, OFC or ACC inactivation did not affect decision-making. Infusion of a dopamine D2 receptor antagonist into any sub-region did not alter choice preference. Online activity of the IL or PrL cortex is important for maintaining an optimal decision-making strategy, but optimal performance on the rGT does not require frontal cortex dopamine D2 receptor activation. Additionally, these results demonstrate that the roles of different cortical regions in cost-benefit decision-making may be dissociated using the rGT.

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

The anatomy of choice: dopamine and decision-making

PubMed Central

Friston, Karl; Schwartenbeck, Philipp; FitzGerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J.

2014-01-01

This paper considers goal-directed decision-making in terms of embodied or active inference. We associate bounded rationality with approximate Bayesian inference that optimizes a free energy bound on model evidence. Several constructs such as expected utility, exploration or novelty bonuses, softmax choice rules and optimism bias emerge as natural consequences of free energy minimization. Previous accounts of active inference have focused on predictive coding. In this paper, we consider variational Bayes as a scheme that the brain might use for approximate Bayesian inference. This scheme provides formal constraints on the computational anatomy of inference and action, which appear to be remarkably consistent with neuroanatomy. Active inference contextualizes optimal decision theory within embodied inference, where goals become prior beliefs. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (associated with softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution. Crucially, this sensitivity corresponds to the precision of beliefs about behaviour. The changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses—and they may provide a new perspective on the role of dopamine in assimilating reward prediction errors to optimize decision-making. PMID:25267823

The anatomy of choice: dopamine and decision-making.

PubMed

Friston, Karl; Schwartenbeck, Philipp; FitzGerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J

2014-11-05

This paper considers goal-directed decision-making in terms of embodied or active inference. We associate bounded rationality with approximate Bayesian inference that optimizes a free energy bound on model evidence. Several constructs such as expected utility, exploration or novelty bonuses, softmax choice rules and optimism bias emerge as natural consequences of free energy minimization. Previous accounts of active inference have focused on predictive coding. In this paper, we consider variational Bayes as a scheme that the brain might use for approximate Bayesian inference. This scheme provides formal constraints on the computational anatomy of inference and action, which appear to be remarkably consistent with neuroanatomy. Active inference contextualizes optimal decision theory within embodied inference, where goals become prior beliefs. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (associated with softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution. Crucially, this sensitivity corresponds to the precision of beliefs about behaviour. The changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses-and they may provide a new perspective on the role of dopamine in assimilating reward prediction errors to optimize decision-making.

Automatically updating predictive modeling workflows support decision-making in drug design.

PubMed

Muegge, Ingo; Bentzien, Jörg; Mukherjee, Prasenjit; Hughes, Robert O

2016-09-01

Using predictive models for early decision-making in drug discovery has become standard practice. We suggest that model building needs to be automated with minimum input and low technical maintenance requirements. Models perform best when tailored to answering specific compound optimization related questions. If qualitative answers are required, 2-bin classification models are preferred. Integrating predictive modeling results with structural information stimulates better decision making. For in silico models supporting rapid structure-activity relationship cycles the performance deteriorates within weeks. Frequent automated updates of predictive models ensure best predictions. Consensus between multiple modeling approaches increases the prediction confidence. Combining qualified and nonqualified data optimally uses all available information. Dose predictions provide a holistic alternative to multiple individual property predictions for reaching complex decisions.

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.

Rational decision-making in inhibitory control.

PubMed

Shenoy, Pradeep; Yu, Angela J

2011-01-01

An important aspect of cognitive flexibility is inhibitory control, the ability to dynamically modify or cancel planned actions in response to changes in the sensory environment or task demands. We formulate a probabilistic, rational decision-making framework for inhibitory control in the stop signal paradigm. Our model posits that subjects maintain a Bayes-optimal, continually updated representation of sensory inputs, and repeatedly assess the relative value of stopping and going on a fine temporal scale, in order to make an optimal decision on when and whether to go on each trial. We further posit that they implement this continual evaluation with respect to a global objective function capturing the various reward and penalties associated with different behavioral outcomes, such as speed and accuracy, or the relative costs of stop errors and go errors. We demonstrate that our rational decision-making model naturally gives rise to basic behavioral characteristics consistently observed for this paradigm, as well as more subtle effects due to contextual factors such as reward contingencies or motivational factors. Furthermore, we show that the classical race model can be seen as a computationally simpler, perhaps neurally plausible, approximation to optimal decision-making. This conceptual link allows us to predict how the parameters of the race model, such as the stopping latency, should change with task parameters and individual experiences/ability.

Rational Decision-Making in Inhibitory Control

PubMed Central

Shenoy, Pradeep; Yu, Angela J.

2011-01-01

An important aspect of cognitive flexibility is inhibitory control, the ability to dynamically modify or cancel planned actions in response to changes in the sensory environment or task demands. We formulate a probabilistic, rational decision-making framework for inhibitory control in the stop signal paradigm. Our model posits that subjects maintain a Bayes-optimal, continually updated representation of sensory inputs, and repeatedly assess the relative value of stopping and going on a fine temporal scale, in order to make an optimal decision on when and whether to go on each trial. We further posit that they implement this continual evaluation with respect to a global objective function capturing the various reward and penalties associated with different behavioral outcomes, such as speed and accuracy, or the relative costs of stop errors and go errors. We demonstrate that our rational decision-making model naturally gives rise to basic behavioral characteristics consistently observed for this paradigm, as well as more subtle effects due to contextual factors such as reward contingencies or motivational factors. Furthermore, we show that the classical race model can be seen as a computationally simpler, perhaps neurally plausible, approximation to optimal decision-making. This conceptual link allows us to predict how the parameters of the race model, such as the stopping latency, should change with task parameters and individual experiences/ability. PMID:21647306

Influence of Men's Personality and Social Support on Treatment Decision-Making for Localized Prostate Cancer.

PubMed

Reamer, Elyse; Yang, Felix; Holmes-Rovner, Margaret; Liu, Joe; Xu, Jinping

2017-01-01

Optimal treatment for localized prostate cancer (LPC) is controversial. We assessed the effects of personality, specialists seen, and involvement of spouse, family, or friends on treatment decision/decision-making qualities. We surveyed a population-based sample of men ≤ 75 years with newly diagnosed LPC about treatment choice, reasons for the choice, decision-making difficulty, satisfaction, and regret. Of 160 men (71 black, 89 white), with a mean age of 61 (±7.3) years, 59% chose surgery, 31% chose radiation, and 10% chose active surveillance (AS)/watchful waiting (WW). Adjusting for age, race, comorbidity, tumor risk level, and treatment status, men who consulted friends during decision-making were more likely to choose curative treatment (radiation or surgery) than WW/AS (OR = 11.1, p < 0.01; 8.7, p < 0.01). Men who saw a radiation oncologist in addition to a urologist were more likely to choose radiation than surgery (OR = 6.0, p = 0.04). Men who consulted family or friends (OR = 2.6, p < 0.01; 3.7, p < 0.01) experienced greater decision-making difficulty. No personality traits (pessimism, optimism, or faith) were associated with treatment choice/decision-making quality measures. In addition to specialist seen, consulting friends increased men's likelihood of choosing curative treatment. Consulting family or friends increased decision-making difficulty.

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.

Optimal policy for value-based decision-making.

PubMed

Tajima, Satohiro; Drugowitsch, Jan; Pouget, Alexandre

2016-08-18

For decades now, normative theories of perceptual decisions, and their implementation as drift diffusion models, have driven and significantly improved our understanding of human and animal behaviour and the underlying neural processes. While similar processes seem to govern value-based decisions, we still lack the theoretical understanding of why this ought to be the case. Here, we show that, similar to perceptual decisions, drift diffusion models implement the optimal strategy for value-based decisions. Such optimal decisions require the models' decision boundaries to collapse over time, and to depend on the a priori knowledge about reward contingencies. Diffusion models only implement the optimal strategy under specific task assumptions, and cease to be optimal once we start relaxing these assumptions, by, for example, using non-linear utility functions. Our findings thus provide the much-needed theory for value-based decisions, explain the apparent similarity to perceptual decisions, and predict conditions under which this similarity should break down.

Optimal policy for value-based decision-making

PubMed Central

Tajima, Satohiro; Drugowitsch, Jan; Pouget, Alexandre

2016-01-01

For decades now, normative theories of perceptual decisions, and their implementation as drift diffusion models, have driven and significantly improved our understanding of human and animal behaviour and the underlying neural processes. While similar processes seem to govern value-based decisions, we still lack the theoretical understanding of why this ought to be the case. Here, we show that, similar to perceptual decisions, drift diffusion models implement the optimal strategy for value-based decisions. Such optimal decisions require the models' decision boundaries to collapse over time, and to depend on the a priori knowledge about reward contingencies. Diffusion models only implement the optimal strategy under specific task assumptions, and cease to be optimal once we start relaxing these assumptions, by, for example, using non-linear utility functions. Our findings thus provide the much-needed theory for value-based decisions, explain the apparent similarity to perceptual decisions, and predict conditions under which this similarity should break down. PMID:27535638

An optimal brain can be composed of conflicting agents

PubMed Central

Livnat, Adi; Pippenger, Nicholas

2006-01-01

Many behaviors have been attributed to internal conflict within the animal and human mind. However, internal conflict has not been reconciled with evolutionary principles, in that it appears maladaptive relative to a seamless decision-making process. We study this problem through a mathematical analysis of decision-making structures. We find that, under natural physiological limitations, an optimal decision-making system can involve “selfish” agents that are in conflict with one another, even though the system is designed for a single purpose. It follows that conflict can emerge within a collective even when natural selection acts on the level of the collective only. PMID:16492775

Optimization Research of Generation Investment Based on Linear Programming Model

NASA Astrophysics Data System (ADS)

Wu, Juan; Ge, Xueqian

Linear programming is an important branch of operational research and it is a mathematical method to assist the people to carry out scientific management. GAMS is an advanced simulation and optimization modeling language and it will combine a large number of complex mathematical programming, such as linear programming LP, nonlinear programming NLP, MIP and other mixed-integer programming with the system simulation. In this paper, based on the linear programming model, the optimized investment decision-making of generation is simulated and analyzed. At last, the optimal installed capacity of power plants and the final total cost are got, which provides the rational decision-making basis for optimized investments.

A novel medical information management and decision model for uncertain demand optimization.

PubMed

Bi, Ya

2015-01-01

Accurately planning the procurement volume is an effective measure for controlling the medicine inventory cost. Due to uncertain demand it is difficult to make accurate decision on procurement volume. As to the biomedicine sensitive to time and season demand, the uncertain demand fitted by the fuzzy mathematics method is obviously better than general random distribution functions. To establish a novel medical information management and decision model for uncertain demand optimization. A novel optimal management and decision model under uncertain demand has been presented based on fuzzy mathematics and a new comprehensive improved particle swarm algorithm. The optimal management and decision model can effectively reduce the medicine inventory cost. The proposed improved particle swarm optimization is a simple and effective algorithm to improve the Fuzzy interference and hence effectively reduce the calculation complexity of the optimal management and decision model. Therefore the new model can be used for accurate decision on procurement volume under uncertain demand.

Uncertainty analysis for effluent trading planning using a Bayesian estimation-based simulation-optimization modeling approach.

PubMed

Zhang, J L; Li, Y P; Huang, G H; Baetz, B W; Liu, J

2017-06-01

In this study, a Bayesian estimation-based simulation-optimization modeling approach (BESMA) is developed for identifying effluent trading strategies. BESMA incorporates nutrient fate modeling with soil and water assessment tool (SWAT), Bayesian estimation, and probabilistic-possibilistic interval programming with fuzzy random coefficients (PPI-FRC) within a general framework. Based on the water quality protocols provided by SWAT, posterior distributions of parameters can be analyzed through Bayesian estimation; stochastic characteristic of nutrient loading can be investigated which provides the inputs for the decision making. PPI-FRC can address multiple uncertainties in the form of intervals with fuzzy random boundaries and the associated system risk through incorporating the concept of possibility and necessity measures. The possibility and necessity measures are suitable for optimistic and pessimistic decision making, respectively. BESMA is applied to a real case of effluent trading planning in the Xiangxihe watershed, China. A number of decision alternatives can be obtained under different trading ratios and treatment rates. The results can not only facilitate identification of optimal effluent-trading schemes, but also gain insight into the effects of trading ratio and treatment rate on decision making. The results also reveal that decision maker's preference towards risk would affect decision alternatives on trading scheme as well as system benefit. Compared with the conventional optimization methods, it is proved that BESMA is advantageous in (i) dealing with multiple uncertainties associated with randomness and fuzziness in effluent-trading planning within a multi-source, multi-reach and multi-period context; (ii) reflecting uncertainties existing in nutrient transport behaviors to improve the accuracy in water quality prediction; and (iii) supporting pessimistic and optimistic decision making for effluent trading as well as promoting diversity of decision alternatives. Copyright © 2017 Elsevier Ltd. All rights reserved.

Quantitative Decision Making.

ERIC Educational Resources Information Center

Baldwin, Grover H.

The use of quantitative decision making tools provides the decision maker with a range of alternatives among which to decide, permits acceptance and use of the optimal solution, and decreases risk. Training line administrators in the use of these tools can help school business officials obtain reliable information upon which to base district…

Beyond Decision Making: Cultural Ideology as Heuristic Paradigmatic Models.

ERIC Educational Resources Information Center

Whitley, L. Darrell

A paradigmatic model of cultural ideology provides a context for understanding the relationship between decision-making and personal and cultural rationality. Cultural rules or heuristics exist which indicate that many decisions can be made on the basis of established strategy rather than continual analytical calculations. When an optimal solution…

Grey situation group decision-making method based on prospect theory.

PubMed

Zhang, Na; Fang, Zhigeng; Liu, Xiaqing

2014-01-01

This paper puts forward a grey situation group decision-making method on the basis of prospect theory, in view of the grey situation group decision-making problems that decisions are often made by multiple decision experts and those experts have risk preferences. The method takes the positive and negative ideal situation distance as reference points, defines positive and negative prospect value function, and introduces decision experts' risk preference into grey situation decision-making to make the final decision be more in line with decision experts' psychological behavior. Based on TOPSIS method, this paper determines the weight of each decision expert, sets up comprehensive prospect value matrix for decision experts' evaluation, and finally determines the optimal situation. At last, this paper verifies the effectiveness and feasibility of the method by means of a specific example.

Grey Situation Group Decision-Making Method Based on Prospect Theory

PubMed Central

Zhang, Na; Fang, Zhigeng; Liu, Xiaqing

2014-01-01

This paper puts forward a grey situation group decision-making method on the basis of prospect theory, in view of the grey situation group decision-making problems that decisions are often made by multiple decision experts and those experts have risk preferences. The method takes the positive and negative ideal situation distance as reference points, defines positive and negative prospect value function, and introduces decision experts' risk preference into grey situation decision-making to make the final decision be more in line with decision experts' psychological behavior. Based on TOPSIS method, this paper determines the weight of each decision expert, sets up comprehensive prospect value matrix for decision experts' evaluation, and finally determines the optimal situation. At last, this paper verifies the effectiveness and feasibility of the method by means of a specific example. PMID:25197706

Review of experimental studies in social psychology of small groups when an optimal choice exists and application to operating room management decision-making.

PubMed

Prahl, Andrew; Dexter, Franklin; Braun, Michael T; Van Swol, Lyn

2013-11-01

Because operating room (OR) management decisions with optimal choices are made with ubiquitous biases, decisions are improved with decision-support systems. We reviewed experimental social-psychology studies to explore what an OR leader can do when working with stakeholders lacking interest in learning the OR management science but expressing opinions about decisions, nonetheless. We considered shared information to include the rules-of-thumb (heuristics) that make intuitive sense and often seem "close enough" (e.g., staffing is planned based on the average workload). We considered unshared information to include the relevant mathematics (e.g., staffing calculations). Multiple studies have shown that group discussions focus more on shared than unshared information. Quality decisions are more likely when all group participants share knowledge (e.g., have taken a course in OR management science). Several biases in OR management are caused by humans' limited abilities to estimate tails of probability distributions in their heads. Groups are more susceptible to analogous biases than are educated individuals. Since optimal solutions are not demonstrable without groups sharing common language, only with education of most group members can a knowledgeable individual influence the group. The appropriate model of decision-making is autocratic, with information obtained from stakeholders. Although such decisions are good quality, the leaders often are disliked and the decisions considered unjust. In conclusion, leaders will find the most success if they do not bring OR management operational decisions to groups, but instead act autocratically while obtaining necessary information in 1:1 conversations. The only known route for the leader making such decisions to be considered likable and for the decisions to be considered fair is through colleagues and subordinates learning the management science.

A plastic corticostriatal circuit model of adaptation in perceptual decision making

PubMed Central

Hsiao, Pao-Yueh; Lo, Chung-Chuan

2013-01-01

The ability to optimize decisions and adapt them to changing environments is a crucial brain function that increase survivability. Although much has been learned about the neuronal activity in various brain regions that are associated with decision making, and about how the nervous systems may learn to achieve optimization, the underlying neuronal mechanisms of how the nervous systems optimize decision strategies with preference given to speed or accuracy, and how the systems adapt to changes in the environment, remain unclear. Based on extensive empirical observations, we addressed the question by extending a previously described cortico-basal ganglia circuit model of perceptual decisions with the inclusion of a dynamic dopamine (DA) system that modulates spike-timing dependent plasticity (STDP). We found that, once an optimal model setting that maximized the reward rate was selected, the same setting automatically optimized decisions across different task environments through dynamic balancing between the facilitating and depressing components of the DA dynamics. Interestingly, other model parameters were also optimal if we considered the reward rate that was weighted by the subject's preferences for speed or accuracy. Specifically, the circuit model favored speed if we increased the phasic DA response to the reward prediction error, whereas the model favored accuracy if we reduced the tonic DA activity or the phasic DA responses to the estimated reward probability. The proposed model provides insight into the roles of different components of DA responses in decision adaptation and optimization in a changing environment. PMID:24339814

Optimal strategies for electric energy contract decision making

NASA Astrophysics Data System (ADS)

Song, Haili

2000-10-01

The power industry restructuring in various countries in recent years has created an environment where trading of electric energy is conducted in a market environment. In such an environment, electric power companies compete for the market share through spot and bilateral markets. Being profit driven, electric power companies need to make decisions on spot market bidding, contract evaluation, and risk management. New methods and software tools are required to meet these upcoming needs. In this research, bidding strategy and contract pricing are studied from a market participant's viewpoint; new methods are developed to guide a market participant in spot and bilateral market operation. A supplier's spot market bidding decision is studied. Stochastic optimization is formulated to calculate a supplier's optimal bids in a single time period. This decision making problem is also formulated as a Markov Decision Process. All the competitors are represented by their bidding parameters with corresponding probabilities. A systematic method is developed to calculate transition probabilities and rewards. The optimal strategy is calculated to maximize the expected reward over a planning horizon. Besides the spot market, a power producer can also trade in the bilateral markets. Bidding strategies in a bilateral market are studied with game theory techniques. Necessary and sufficient conditions of Nash Equilibrium (NE) bidding strategy are derived based on the generators' cost and the loads' willingness to pay. The study shows that in any NE, market efficiency is achieved. Furthermore, all Nash equilibria are revenue equivalent for the generators. The pricing of "Flexible" contracts, which allow delivery flexibility over a period of time with a fixed total amount of electricity to be delivered, is analyzed based on the no-arbitrage pricing principle. The proposed algorithm calculates the price based on the optimality condition of the stochastic optimization formulation. Simulation examples illustrate the tradeoffs between prices and scheduling flexibility. Spot bidding and contract pricing are not independent decision processes. The interaction between spot bidding and contract evaluation is demonstrated with game theory equilibrium model and market simulation results. It leads to the conclusion that a market participant's contract decision making needs to be further investigated as an integrated optimization formulation.

Equipment Selection by using Fuzzy TOPSIS Method

NASA Astrophysics Data System (ADS)

Yavuz, Mahmut

2016-10-01

In this study, Fuzzy TOPSIS method was performed for the selection of open pit truck and the optimal solution of the problem was investigated. Data from Turkish Coal Enterprises was used in the application of the method. This paper explains the Fuzzy TOPSIS approaches with group decision-making application in an open pit coal mine in Turkey. An algorithm of the multi-person multi-criteria decision making with fuzzy set approach was applied an equipment selection problem. It was found that Fuzzy TOPSIS with a group decision making is a method that may help decision-makers in solving different decision-making problems in mining.

Understanding Optimal Military Decision Making: Year 2 Progress Report

DTIC Science & Technology

2014-01-01

measures. ARMY RELEVANCY AND MILITARY APPLICATION AREAS Objectively defining, measuring, and developing a means to assess military optimal decision making...has the potential to enhance training and refine procedures supporting more efficient learning and task accomplishment. Through the application of...26.79 (12.39) 7.94 (62.38) N/A = Not applicable ; as it is not possible to calculate this particular variable. Table 2. Descriptive statistics of

“UTILIZING” SIGNAL DETECTION THEORY

PubMed Central

Lynn, Spencer K.; Barrett, Lisa Feldman

2014-01-01

What do inferring what a person is thinking or feeling, deciding to report a symptom to your doctor, judging a defendant’s guilt, and navigating a dimly lit room have in common? They involve perceptual uncertainty (e.g., a scowling face might indicate anger or concentration, which engender different appropriate responses), and behavioral risk (e.g., a cost to making the wrong response). Signal detection theory describes these types of decisions. In this tutorial we show how, by incorporating the economic concept of utility, signal detection theory serves as a model of optimal decision making, beyond its common use as an analytic method. This utility approach to signal detection theory highlights potentially enigmatic influences of perceptual uncertainty on measures of decision-making performance (accuracy and optimality) and on behavior (a functional relationship between bias and sensitivity). A “utilized” signal detection theory offers the possibility of expanding the phenomena that can be understood within a decision-making framework. PMID:25097061

Confronting dynamics and uncertainty in optimal decision making for conservation

NASA Astrophysics Data System (ADS)

Williams, Byron K.; Johnson, Fred A.

2013-06-01

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

Time optimized path-choice in the termite hunting ant Megaponera analis.

PubMed

Frank, Erik T; Hönle, Philipp O; Linsenmair, K Eduard

2018-05-10

Trail network systems among ants have received a lot of scientific attention due to their various applications in problem solving of networks. Recent studies have shown that ants select the fastest available path when facing different velocities on different substrates, rather than the shortest distance. The progress of decision-making by these ants is determined by pheromone-based maintenance of paths, which is a collective decision. However, path optimization through individual decision-making remains mostly unexplored. Here we present the first study of time-optimized path selection via individual decision-making by scout ants. Megaponera analis scouts search for termite foraging sites and lead highly organized raid columns to them. The path of the scout determines the path of the column. Through installation of artificial roads around M. analis nests we were able to influence the pathway choice of the raids. After road installation 59% of all recorded raids took place completely or partly on the road, instead of the direct, i.e. distance-optimized, path through grass from the nest to the termites. The raid velocity on the road was more than double the grass velocity, the detour thus saved 34.77±23.01% of the travel time compared to a hypothetical direct path. The pathway choice of the ants was similar to a mathematical model of least time allowing us to hypothesize the underlying mechanisms regulating the behavior. Our results highlight the importance of individual decision-making in the foraging behavior of ants and show a new procedure of pathway optimization. © 2018. Published by The Company of Biologists Ltd.

Clinical errors that can occur in the treatment decision-making process in psychotherapy.

PubMed

Park, Jake; Goode, Jonathan; Tompkins, Kelley A; Swift, Joshua K

2016-09-01

Clinical errors occur in the psychotherapy decision-making process whenever a less-than-optimal treatment or approach is chosen when working with clients. A less-than-optimal approach may be one that a client is unwilling to try or fully invest in based on his/her expectations and preferences, or one that may have little chance of success based on contraindications and/or limited research support. The doctor knows best and the independent choice models are two decision-making models that are frequently used within psychology, but both are associated with an increased likelihood of errors in the treatment decision-making process. In particular, these models fail to integrate all three components of the definition of evidence-based practice in psychology (American Psychological Association, 2006). In this article we describe both models and provide examples of clinical errors that can occur in each. We then introduce the shared decision-making model as an alternative that is less prone to clinical errors. PsycINFO Database Record (c) 2016 APA, all rights reserved

Reward Rate Optimization in Two-Alternative Decision Making: Empirical Tests of Theoretical Predictions

ERIC Educational Resources Information Center

Simen, Patrick; Contreras, David; Buck, Cara; Hu, Peter; Holmes, Philip; Cohen, Jonathan D.

2009-01-01

The drift-diffusion model (DDM) implements an optimal decision procedure for stationary, 2-alternative forced-choice tasks. The height of a decision threshold applied to accumulating information on each trial determines a speed-accuracy tradeoff (SAT) for the DDM, thereby accounting for a ubiquitous feature of human performance in speeded response…

Information support for decision making on dispatching control of water distribution in irrigation

NASA Astrophysics Data System (ADS)

Yurchenko, I. F.

2018-05-01

The research has been carried out on developing the technique of supporting decision making for on-line control, operational management of water allocation for the interfarm irrigation projects basing on the analytical patterns of dispatcher control. This technique provides an increase of labour productivity as well as higher management quality due to the improved level of automation, as well as decision making optimization taking into account diagnostics of the issues, solutions classification, information being required to the decision makers.

Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making.

PubMed

Aitchison, Laurence; Bang, Dan; Bahrami, Bahador; Latham, Peter E

2015-10-01

Humans stand out from other animals in that they are able to explicitly report on the reliability of their internal operations. This ability, which is known as metacognition, is typically studied by asking people to report their confidence in the correctness of some decision. However, the computations underlying confidence reports remain unclear. In this paper, we present a fully Bayesian method for directly comparing models of confidence. Using a visual two-interval forced-choice task, we tested whether confidence reports reflect heuristic computations (e.g. the magnitude of sensory data) or Bayes optimal ones (i.e. how likely a decision is to be correct given the sensory data). In a standard design in which subjects were first asked to make a decision, and only then gave their confidence, subjects were mostly Bayes optimal. In contrast, in a less-commonly used design in which subjects indicated their confidence and decision simultaneously, they were roughly equally likely to use the Bayes optimal strategy or to use a heuristic but suboptimal strategy. Our results suggest that, while people's confidence reports can reflect Bayes optimal computations, even a small unusual twist or additional element of complexity can prevent optimality.

Not a Humbug: the evolution of patient-centred medical decision-making.

PubMed

Trump, Benjamin D; Linkov, Faina; Edwards, Robert P; Linkov, Igor

2015-12-01

This 'Christmas Issue'-type paper uses the framework of 'A Christmas Carol' to tell about the evolution of decision-making in evidence-based medicine (EBM). The Ghost of the Past represents paternalistic medicine, the Ghost of the Present symbolises EBM, while the Ghost of the Future serves as a patient-centred system where research data and tools of decision science are jointly used to make optimal medical decisions for individual patients. We argue that this shift towards a patient-centred approach to EBM and medical care is the next step in the evolution of medical decision-making, which would help to empower patients with the capability to make educated decisions throughout the course of their medical treatment.

Decision-making impairments in Parkinson's disease as a by-product of defective cost-benefit analysis and feedback processing.

PubMed

Ryterska, Agata; Jahanshahi, Marjan; Osman, Magda

2014-01-01

Studies examining decision-making in people with Parkinson's disease (PD) show impaired performance on a variety of tasks. However, there are also demonstrations that patients with PD can make optimal decisions just like healthy age-matched controls. We propose that the reason for these mixed findings is that PD does not produce a generalized impairment of decision-making, but rather affects sub-components of this process. In this review we evaluate this hypothesis by considering the empirical evidence examining decision-making in PD. We suggest that of the various stages of the decision-making process, the most affected in PD are (1) the cost-benefit analysis stage and (2) the outcome evaluation stage. We consider the implications of this proposal for research in this area.

Postoptimality Analysis in the Selection of Technology Portfolios

NASA Technical Reports Server (NTRS)

Adumitroaie, Virgil; Shelton, Kacie; Elfes, Alberto; Weisbin, Charles R.

2006-01-01

This slide presentation reviews a process of postoptimally analysing the selection of technology portfolios. The rationale for the analysis stems from the need for consistent, transparent and auditable decision making processes and tools. The methodology is used to assure that project investments are selected through an optimization of net mission value. The main intent of the analysis is to gauge the degree of confidence in the optimal solution and to provide the decision maker with an array of viable selection alternatives which take into account input uncertainties and possibly satisfy non-technical constraints. A few examples of the analysis are reviewed. The goal of the postoptimality study is to enhance and improve the decision-making process by providing additional qualifications and substitutes to the optimal solution.

Neural basis of quasi-rational decision making.

PubMed

Lee, Daeyeol

2006-04-01

Standard economic theories conceive homo economicus as a rational decision maker capable of maximizing utility. In reality, however, people tend to approximate optimal decision-making strategies through a collection of heuristic routines. Some of these routines are driven by emotional processes, and others are adjusted iteratively through experience. In addition, routines specialized for social decision making, such as inference about the mental states of other decision makers, might share their origins and neural mechanisms with the ability to simulate or imagine outcomes expected from alternative actions that an individual can take. A recent surge of collaborations across economics, psychology and neuroscience has provided new insights into how such multiple elements of decision making interact in the brain.

An Intelligent Tutoring System for Classifying Students into Instructional Treatments with Mastery Scores. Research Report 94-15.

ERIC Educational Resources Information Center

Vos, Hans J.

As part of a project formulating optimal rules for decision making in computer assisted instructional systems in which the computer is used as a decision support tool, an approach that simultaneously optimizes classification of students into two treatments, each followed by a mastery decision, is presented using the framework of Bayesian decision…

Decision Aids for Naval Air ASW

DTIC Science & Technology

1980-03-15

Algorithm for Zone Optimization Investigation) NADC Developing Sonobuoy Pattern for Air ASW Search DAISY (Decision Aiding Information System) Wharton...sion making behavior. 0 Artificial intelligence sequential pattern recognition algorithm for reconstructing the decision maker’s utility functions. 0...display presenting the uncertainty area of the target. 3.1.5 Algorithm for Zone Optimization Investigation (AZOI) -- Naval Air Development Center 0 A

Three-level global resource allocation model for hiv control: A hierarchical decision system approach.

PubMed

Kassa, Semu Mitiku

2018-02-01

Funds from various global organizations, such as, The Global Fund, The World Bank, etc. are not directly distributed to the targeted risk groups. Especially in the so-called third-world-countries, the major part of the fund in HIV prevention programs comes from these global funding organizations. The allocations of these funds usually pass through several levels of decision making bodies that have their own specific parameters to control and specific objectives to achieve. However, these decisions are made mostly in a heuristic manner and this may lead to a non-optimal allocation of the scarce resources. In this paper, a hierarchical mathematical optimization model is proposed to solve such a problem. Combining existing epidemiological models with the kind of interventions being on practice, a 3-level hierarchical decision making model in optimally allocating such resources has been developed and analyzed. When the impact of antiretroviral therapy (ART) is included in the model, it has been shown that the objective function of the lower level decision making structure is a non-convex minimization problem in the allocation variables even if all the production functions for the intervention programs are assumed to be linear.

Knowledge Visualizations: A Tool to Achieve Optimized Operational Decision Making and Data Integration

DTIC Science & Technology

2015-06-01

Hadoop Distributed File System (HDFS) without any integration with Accumulo-based Knowledge Stores based on OWL/RDF. 4. Cloud Based The Apache Software...BTW, 7(12), pp. 227–241. Godin, A. & Akins, D. (2014). Extending DCGS-N naval tactical clouds from in-storage to in-memory for the integrated fires...VISUALIZATIONS: A TOOL TO ACHIEVE OPTIMIZED OPERATIONAL DECISION MAKING AND DATA INTEGRATION by Paul C. Hudson Jeffrey A. Rzasa June 2015 Thesis

A Framework for Modeling Emerging Diseases to Inform Management

PubMed Central

Katz, Rachel A.; Richgels, Katherine L.D.; Walsh, Daniel P.; Grant, Evan H.C.

2017-01-01

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge. PMID:27983501

A Framework for Modeling Emerging Diseases to Inform Management.

PubMed

Russell, Robin E; Katz, Rachel A; Richgels, Katherine L D; Walsh, Daniel P; Grant, Evan H C

2017-01-01

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge.

Probabilistic vs. non-probabilistic approaches to the neurobiology of perceptual decision-making

PubMed Central

Drugowitsch, Jan; Pouget, Alexandre

2012-01-01

Optimal binary perceptual decision making requires accumulation of evidence in the form of a probability distribution that specifies the probability of the choices being correct given the evidence so far. Reward rates can then be maximized by stopping the accumulation when the confidence about either option reaches a threshold. Behavioral and neuronal evidence suggests that humans and animals follow such a probabilitistic decision strategy, although its neural implementation has yet to be fully characterized. Here we show that that diffusion decision models and attractor network models provide an approximation to the optimal strategy only under certain circumstances. In particular, neither model type is sufficiently flexible to encode the reliability of both the momentary and the accumulated evidence, which is a pre-requisite to accumulate evidence of time-varying reliability. Probabilistic population codes, in contrast, can encode these quantities and, as a consequence, have the potential to implement the optimal strategy accurately. PMID:22884815

A framework for modeling emerging diseases to inform management

USGS Publications Warehouse

Russell, Robin E.; Katz, Rachel A.; Richgels, Katherine L. D.; Walsh, Daniel P.; Grant, Evan H. Campbell

2017-01-01

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge.

Decision-making when data and inferences are not conclusive: risk-benefit and acceptable regret approach.

PubMed

Hozo, Iztok; Schell, Michael J; Djulbegovic, Benjamin

2008-07-01

The absolute truth in research is unobtainable, as no evidence or research hypothesis is ever 100% conclusive. Therefore, all data and inferences can in principle be considered as "inconclusive." Scientific inference and decision-making need to take into account errors, which are unavoidable in the research enterprise. The errors can occur at the level of conclusions that aim to discern the truthfulness of research hypothesis based on the accuracy of research evidence and hypothesis, and decisions, the goal of which is to enable optimal decision-making under present and specific circumstances. To optimize the chance of both correct conclusions and correct decisions, the synthesis of all major statistical approaches to clinical research is needed. The integration of these approaches (frequentist, Bayesian, and decision-analytic) can be accomplished through formal risk:benefit (R:B) analysis. This chapter illustrates the rational choice of a research hypothesis using R:B analysis based on decision-theoretic expected utility theory framework and the concept of "acceptable regret" to calculate the threshold probability of the "truth" above which the benefit of accepting a research hypothesis outweighs its risks.

Regret and the rationality of choices.

PubMed

Bourgeois-Gironde, Sacha

2010-01-27

Regret helps to optimize decision behaviour. It can be defined as a rational emotion. Several recent neurobiological studies have confirmed the interface between emotion and cognition at which regret is located and documented its role in decision behaviour. These data give credibility to the incorporation of regret in decision theory that had been proposed by economists in the 1980s. However, finer distinctions are required in order to get a better grasp of how regret and behaviour influence each other. Regret can be defined as a predictive error signal but this signal does not necessarily transpose into a decision-weight influencing behaviour. Clinical studies on several types of patients show that the processing of an error signal and its influence on subsequent behaviour can be dissociated. We propose a general understanding of how regret and decision-making are connected in terms of regret being modulated by rational antecedents of choice. Regret and the modification of behaviour on its basis will depend on the criteria of rationality involved in decision-making. We indicate current and prospective lines of research in order to refine our views on how regret contributes to optimal decision-making.

A Review of Shared Decision-Making and Patient Decision Aids in Radiation Oncology.

PubMed

Woodhouse, Kristina Demas; Tremont, Katie; Vachani, Anil; Schapira, Marilyn M; Vapiwala, Neha; Simone, Charles B; Berman, Abigail T

2017-06-01

Cancer treatment decisions are complex and may be challenging for patients, as multiple treatment options can often be reasonably considered. As a result, decisional support tools have been developed to assist patients in the decision-making process. A commonly used intervention to facilitate shared decision-making is a decision aid, which provides evidence-based outcomes information and guides patients towards choosing the treatment option that best aligns with their preferences and values. To ensure high quality, systematic frameworks and standards have been proposed for the development of an optimal aid for decision making. Studies have examined the impact of these tools on facilitating treatment decisions and improving decision-related outcomes. In radiation oncology, randomized controlled trials have demonstrated that decision aids have the potential to improve patient outcomes, including increased knowledge about treatment options and decreased decisional conflict with decision-making. This article provides an overview of the shared-decision making process and summarizes the development, validation, and implementation of decision aids as patient educational tools in radiation oncology. Finally, this article reviews the findings from decision aid studies in radiation oncology and offers various strategies to effectively implement shared decision-making into clinical practice.

Noradrenergic modulation of vicarious trial-and-error behavior during a spatial decision-making task in rats.

PubMed

Amemiya, S; Noji, T; Kubota, N; Nishijima, T; Kita, I

2014-04-18

Deliberation between possible options before making a decision is crucial to responding with an optimal choice. However, the neural mechanisms regulating this deliberative decision-making process are still unclear. Recent studies have proposed that the locus coeruleus-noradrenaline (LC-NA) system plays a role in attention, behavioral flexibility, and exploration, which contribute to the search for an optimal choice under uncertain situations. In the present study, we examined whether the LC-NA system relates to the deliberative process in a T-maze spatial decision-making task in rats. To quantify deliberation in rats, we recorded vicarious trial-and-error behavior (VTE), which is considered to reflect a deliberative process exploring optimal choices. In experiment 1, we manipulated the difficulty of choice by varying the amount of reward pellets between the two maze arms (0 vs. 4, 1 vs. 3, 2 vs. 2). A difficulty-dependent increase in VTE was accompanied by a reduction of choice bias toward the high reward arm and an increase in time required to select one of the two arms in the more difficult manipulation. In addition, the increase of c-Fos-positive NA neurons in the LC depended on the task difficulty and the amount of c-Fos expression in LC-NA neurons positively correlated with the occurrence of VTE. In experiment 2, we inhibited LC-NA activity by injection of clonidine, an agonist of the alpha2 autoreceptor, during a decision-making task (1 vs. 3). The clonidine injection suppressed occurrence of VTE in the early phase of the task and subsequently impaired a valuable choice later in the task. These results suggest that the LC-NA system regulates the deliberative process during decision-making. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

A cognitive prosthesis for complex decision-making.

PubMed

Tremblay, Sébastien; Gagnon, Jean-François; Lafond, Daniel; Hodgetts, Helen M; Doiron, Maxime; Jeuniaux, Patrick P J M H

2017-01-01

While simple heuristics can be ecologically rational and effective in naturalistic decision making contexts, complex situations require analytical decision making strategies, hypothesis-testing and learning. Sub-optimal decision strategies - using simplified as opposed to analytic decision rules - have been reported in domains such as healthcare, military operational planning, and government policy making. We investigate the potential of a computational toolkit called "IMAGE" to improve decision-making by developing structural knowledge and increasing understanding of complex situations. IMAGE is tested within the context of a complex military convoy management task through (a) interactive simulations, and (b) visualization and knowledge representation capabilities. We assess the usefulness of two versions of IMAGE (desktop and immersive) compared to a baseline. Results suggest that the prosthesis helped analysts in making better decisions, but failed to increase their structural knowledge about the situation once the cognitive prosthesis is removed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Application of TOPSIS and VIKOR improved versions in a multi criteria decision analysis to develop an optimized municipal solid waste management model.

PubMed

Aghajani Mir, M; Taherei Ghazvinei, P; Sulaiman, N M N; Basri, N E A; Saheri, S; Mahmood, N Z; Jahan, A; Begum, R A; Aghamohammadi, N

2016-01-15

Selecting a suitable Multi Criteria Decision Making (MCDM) method is a crucial stage to establish a Solid Waste Management (SWM) system. Main objective of the current study is to demonstrate and evaluate a proposed method using Multiple Criteria Decision Making methods (MCDM). An improved version of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) applied to obtain the best municipal solid waste management method by comparing and ranking the scenarios. Applying this method in order to rank treatment methods is introduced as one contribution of the study. Besides, Viekriterijumsko Kompromisno Rangiranje (VIKOR) compromise solution method applied for sensitivity analyses. The proposed method can assist urban decision makers in prioritizing and selecting an optimized Municipal Solid Waste (MSW) treatment system. Besides, a logical and systematic scientific method was proposed to guide an appropriate decision-making. A modified TOPSIS methodology as a superior to existing methods for first time was applied for MSW problems. Applying this method in order to rank treatment methods is introduced as one contribution of the study. Next, 11 scenarios of MSW treatment methods are defined and compared environmentally and economically based on the waste management conditions. Results show that integrating a sanitary landfill (18.1%), RDF (3.1%), composting (2%), anaerobic digestion (40.4%), and recycling (36.4%) was an optimized model of integrated waste management. An applied decision-making structure provides the opportunity for optimum decision-making. Therefore, the mix of recycling and anaerobic digestion and a sanitary landfill with Electricity Production (EP) are the preferred options for MSW management. Copyright © 2015 Elsevier Ltd. All rights reserved.

Multi-objective decision-making model based on CBM for an aircraft fleet

NASA Astrophysics Data System (ADS)

Luo, Bin; Lin, Lin

2018-04-01

Modern production management patterns, in which multi-unit (e.g., a fleet of aircrafts) are managed in a holistic manner, have brought new challenges for multi-unit maintenance decision making. To schedule a good maintenance plan, not only does the individual machine maintenance have to be considered, but also the maintenance of the other individuals have to be taken into account. Since most condition-based maintenance researches for aircraft focused on solely reducing maintenance cost or maximizing the availability of single aircraft, as well as considering that seldom researches concentrated on both the two objectives: minimizing cost and maximizing the availability of a fleet (total number of available aircraft in fleet), a multi-objective decision-making model based on condition-based maintenance concentrated both on the above two objectives is established. Furthermore, in consideration of the decision maker may prefer providing the final optimal result in the form of discrete intervals instead of a set of points (non-dominated solutions) in real decision-making problem, a novel multi-objective optimization method based on support vector regression is proposed to solve the above multi-objective decision-making model. Finally, a case study regarding a fleet is conducted, with the results proving that the approach efficiently generates outcomes that meet the schedule requirements.

Optimal condition sampling for a network of infrastructure facilities.

DOT National Transportation Integrated Search

2011-12-31

In response to the developments in inspection technologies, infrastructure decision-making methods evolved whereby the optimum combination of inspection decisions on the one hand and maintenance and rehabilitation decisions on the other are determine...

Using multi-criteria decision making for selection of the optimal strategy for municipal solid waste management.

PubMed

Jovanovic, Sasa; Savic, Slobodan; Jovicic, Nebojsa; Boskovic, Goran; Djordjevic, Zorica

2016-09-01

Multi-criteria decision making (MCDM) is a relatively new tool for decision makers who deal with numerous and often contradictory factors during their decision making process. This paper presents a procedure to choose the optimal municipal solid waste (MSW) management system for the area of the city of Kragujevac (Republic of Serbia) based on the MCDM method. Two methods of multiple attribute decision making, i.e. SAW (simple additive weighting method) and TOPSIS (technique for order preference by similarity to ideal solution), respectively, were used to compare the proposed waste management strategies (WMS). Each of the created strategies was simulated using the software package IWM2. Total values for eight chosen parameters were calculated for all the strategies. Contribution of each of the six waste treatment options was valorized. The SAW analysis was used to obtain the sum characteristics for all the waste management treatment strategies and they were ranked accordingly. The TOPSIS method was used to calculate the relative closeness factors to the ideal solution for all the alternatives. Then, the proposed strategies were ranked in form of tables and diagrams obtained based on both MCDM methods. As shown in this paper, the results were in good agreement, which additionally confirmed and facilitated the choice of the optimal MSW management strategy. © The Author(s) 2016.

Multicriteria optimization approach to design and operation of district heating supply system over its life cycle

NASA Astrophysics Data System (ADS)

Hirsch, Piotr; Duzinkiewicz, Kazimierz; Grochowski, Michał

2017-11-01

District Heating (DH) systems are commonly supplied using local heat sources. Nowadays, modern insulation materials allow for effective and economically viable heat transportation over long distances (over 20 km). In the paper a method for optimized selection of design and operating parameters of long distance Heat Transportation System (HTS) is proposed. The method allows for evaluation of feasibility and effectivity of heat transportation from the considered heat sources. The optimized selection is formulated as multicriteria decision-making problem. The constraints for this problem include a static HTS model, allowing considerations of system life cycle, time variability and spatial topology. Thereby, variation of heat demand and ground temperature within the DH area, insulation and pipe aging and/or terrain elevation profile are taken into account in the decision-making process. The HTS construction costs, pumping power, and heat losses are considered as objective functions. Inner pipe diameter, insulation thickness, temperatures and pumping stations locations are optimized during the decision-making process. Moreover, the variants of pipe-laying e.g. one pipeline with the larger diameter or two with the smaller might be considered during the optimization. The analyzed optimization problem is multicriteria, hybrid and nonlinear. Because of such problem properties, the genetic solver was applied.

Who to Blame: Irrational Decision-Makers or Stupid Modelers? (Arne Richter Award for Outstanding Young Scientists Lecture)

NASA Astrophysics Data System (ADS)

Madani, Kaveh

2016-04-01

Water management benefits from a suite of modelling tools and techniques that help simplifying and understanding the complexities involved in managing water resource systems. Early water management models were mainly concerned with optimizing a single objective, related to the design, operations or management of water resource systems (e.g. economic cost, hydroelectricity production, reliability of water deliveries). Significant improvements in methodologies, computational capacity, and data availability over the last decades have resulted in developing more complex water management models that can now incorporate multiple objectives, various uncertainties, and big data. These models provide an improved understanding of complex water resource systems and provide opportunities for making positive impacts. Nevertheless, there remains an alarming mismatch between the optimal solutions developed by these models and the decisions made by managers and stakeholders of water resource systems. Modelers continue to consider decision makers as irrational agents who fail to implement the optimal solutions developed by sophisticated and mathematically rigours water management models. On the other hand, decision makers and stakeholders accuse modelers of being idealist, lacking a perfect understanding of reality, and developing 'smart' solutions that are not practical (stable). In this talk I will have a closer look at the mismatch between the optimality and stability of solutions and argue that conventional water resources management models suffer inherently from a full-cooperation assumption. According to this assumption, water resources management decisions are based on group rationality where in practice decisions are often based on individual rationality, making the group's optimal solution unstable for individually rational decision makers. I discuss how game theory can be used as an appropriate framework for addressing the irrational "rationality assumption" of water resources management models and for better capturing the social aspects of decision making in water management systems with multiple stakeholders.

Heuristic-based information acquisition and decision making among pilots.

PubMed

Wiggins, Mark W; Bollwerk, Sandra

2006-01-01

This research was designed to examine the impact of heuristic-based approaches to the acquisition of task-related information on the selection of an optimal alternative during simulated in-flight decision making. The work integrated features of naturalistic and normative decision making and strategies of information acquisition within a computer-based, decision support framework. The study comprised two phases, the first of which involved familiarizing pilots with three different heuristic-based strategies of information acquisition: frequency, elimination by aspects, and majority of confirming decisions. The second stage enabled participants to choose one of the three strategies of information acquisition to resolve a fourth (choice) scenario. The results indicated that task-oriented experience, rather than the information acquisition strategies, predicted the selection of the optimal alternative. It was also evident that of the three strategies available, the elimination by aspects information acquisition strategy was preferred by most participants. It was concluded that task-oriented experience, rather than the process of information acquisition, predicted task accuracy during the decision-making task. It was also concluded that pilots have a preference for one particular approach to information acquisition. Applications of outcomes of this research include the development of decision support systems that adapt to the information-processing capabilities and preferences of users.

Health-Related Decision-Making in HIV Disease

PubMed Central

Doyle, Katie L.; Woods, Steven Paul; Morgan, Erin E.; Iudicello, Jennifer E.; Cameron, Marizela V.; Gilbert, Paul E.; Beltran, Jessica

2016-01-01

Individuals living with HIV show moderate decision-making deficits, though no prior studies have evaluated the ability to make optimal health-related decisions across the HIV healthcare continuum. Forty-three HIV+ individuals with HIV−associated neurocognitive disorders (HAND+), 50 HIV+ individuals without HAND (HAND−), and 42 HIV− participants were administered two measures of health-related decision-making as part of a comprehensive neuropsychological battery: 1) The Decisional Conflict Scale (DCS), and 2) The Modified UCSD Brief Assessment for Capacity to Consent (UBACC-T). Multiple regression analyses revealed that HAND was an independent predictor of both the DCS and the UBACC-T, such that the HAND+ sample evidenced significantly poorer scores relative to comparison groups. Within the HIV+ sample, poorer health-related decision-making was associated with worse performance on tests of episodic memory, risky decision-making, and health literacy. Findings indicate that individuals with HAND evidence moderate deficits in effectively comprehending and evaluating various health-related choices. PMID:26946300

Improving Shipboard Decision Making in the CBR-D (Chemical/Biological Radiological Defense) Environment: Concepts of Use for and Functional Description of a Decision Aid/Training System (DECAID)

DTIC Science & Technology

1988-08-19

take place over the period of several days. Decisions regarding MOPP level or resource allocation made on day I may have no immediate impact, but a...present -- conditions, and manage a resource library to assist the DCA in making decisions under conditions of uncertainty. Several areas of utilization are...students work through a scenario, the device couid then display the consequences of those decisions or provide optimal decision recommendations

Multicriteria Selection of Optimal Location of TCSC in a Competitive Energy Market

NASA Astrophysics Data System (ADS)

Alomoush, Muwaffaq I.

2010-05-01

The paper investigates selection of the best location of thyristor-controlled series compensator (TCSC) in a transmission system from many candidate locations in a competitive energy market such that the TCSC causes a net valuable impact on congestion management outcome, transmission utilization, transmission losses, voltage stability, degree of fulfillment of spot market contracts, and system security. The problem is treated as a multicriteria decision-making process such that the candidate locations of TCSC are the alternatives and the conflicting objectives are the outcomes of the dispatch process, which may have different importance weights. The paper proposes some performance indices that the dispatch decision-making entity can use to measure market dispatch outcomes of each alternative. Based on agreed-upon preferences, the measures presented may help the decision maker compare and rank dispatch scenarios to ultimately decide which location is the optimal one. To solve the multicriteria decision, we use the preference ranking organization method for enrichment evaluations (PROMETHEE), which is a multicriteria decision support method that can handle complex conflicting-objective decision-making processes.

Outbreak Column 16: Cognitive errors in outbreak decision making.

PubMed

Curran, Evonne T

2015-01-01

During outbreaks, decisions must be made without all the required information. People, including infection prevention and control teams (IPCTs), who have to make decisions during uncertainty use heuristics to fill the missing data gaps. Heuristics are mental model short cuts that by-and-large enable us to make good decisions quickly. However, these heuristics contain biases and effects that at times lead to cognitive (thinking) errors. These cognitive errors are not made to deliberately misrepresent any given situation; we are subject to heuristic biases when we are trying to perform optimally. The science of decision making is large; there are over 100 different biases recognised and described. Outbreak Column 16 discusses and relates these heuristics and biases to decision making during outbreak prevention, preparedness and management. Insights as to how we might recognise and avoid them are offered.

Understanding antibiotic decision making in surgery-a qualitative analysis.

PubMed

Charani, E; Tarrant, C; Moorthy, K; Sevdalis, N; Brennan, L; Holmes, A H

2017-10-01

To investigate the characteristics and culture of antibiotic decision making in the surgical specialty. A qualitative study including ethnographic observation and face-to-face interviews with participants from six surgical teams at a teaching hospital in London was conducted. Over a 3-month period: (a) 30 ward rounds (WRs) (100 h) were observed, (b) face-to-face follow-up interviews took place with 13 key informants, (c) multidisciplinary meetings on the management of surgical patients and daily practice on wards were observed. Applying these methods provided rich data for characterizing the antibiotic decision making in surgery and enabled cross-validation and triangulation of the findings. Data from the interview transcripts and the observational notes were coded and analysed iteratively until saturation was reached. The surgical team is in a state of constant flux with individuals having to adjust to the context in which they work. The demands placed on the team to be in the operating room, and to address the surgical needs of the patient mean that the responsibility for antibiotic decision making is uncoordinated and diffuse. Antibiotic decision making is considered by surgeons as a secondary task, commonly delegated to junior members of their team and occurs in the context of disjointed communication. There is lack of clarity around medical decision making for treating infections in surgical patients. The result is sub-optimal and uncoordinated antimicrobial management. Developing the role of a perioperative clinician may help to improve patient-level outcomes and optimize decision making. Copyright © 2017 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Understanding Optimal Decision-Making in Wargaming

DTIC Science & Technology

2013-09-01

of which is a better understanding of the impact of decisions as a part of combat processes. However, using wargaming to understand decision-making...Raymond, 1989). In the aviation domain, pilots exhibit different visual scanning patterns during various phases of flying under instrument flight rules ( IFR ...human neuro- science, 7, 2013. Anna Skinner, Chris Berka, Lindsay Ohara-Long, and Marc Sebrechts. Impact of virtual en- vironment fidelity on behavioral

Evidence-based coverage decisions? Primum non nocere.

PubMed

McElwee, Newell E; Ho, S Yin; McGuigan, Kimberly A; Horn, Mark L

2006-01-01

Drug class reviews are blunt tools for medical decision making. The practice of evidence-based medicine is far more than simply systematic reviews: The patient and doctor are integral. Here we highlight areas of evidence-based coverage decision making where greater balance and transparency could serve to improve the current process, and we recommend elements of a more positive approach that could optimize patient outcomes under resource constraints.

Entrepreneurial Decision Making and Institutional Governance within the Academy: A Case Study

ERIC Educational Resources Information Center

French, Edward F.

2011-01-01

This case study explored the relationship between entrepreneurial decision making and optimal institutional governance. The study focused on a single institution, characterized as a small, tuition-driven, private institution. Twelve participants were interviewed in the study, equally divided between members of the faculty and of the…

Occupational/Career Decision-Making Thought Processes of Adolescents of High Intellectual Ability

ERIC Educational Resources Information Center

Jung, Jae Yup

2017-01-01

Three competing models of the career decision-making thought processes of adolescents of high intellectual ability were tested in this study. Survey data were collected from 664 intellectually gifted Australian adolescents and analyzed using structural equation modeling procedures. The finally accepted, optimal model suggested that, regardless of…

Economic Evaluation Enhances Public Health Decision Making

PubMed Central

Rabarison, Kristina M.; Bish, Connie L.; Massoudi, Mehran S.; Giles, Wayne H.

2015-01-01

Contemporary public health professionals must address the health needs of a diverse population with constrained budgets and shrinking funds. Economic evaluation contributes to evidence-based decision making by helping the public health community identify, measure, and compare activities with the necessary impact, scalability, and sustainability to optimize population health. Asking “how do investments in public health strategies influence or offset the need for downstream spending on medical care and/or social services?” is important when making decisions about resource allocation and scaling of interventions. PMID:26157792

Risk communication and decision-making in the prevention of invasive breast cancer.

PubMed

Partridge, Ann H

2017-08-01

Risk communication surrounding the prevention of invasive breast cancer entails not only understanding of the disease, risks and opportunities for intervention. But it also requires understanding and implementation of optimal strategies for communication with patients who are making these decisions. In this article, available evidence for the issues surrounding risk communication and decision making in the prevention of invasive breast cancer are reviewed and strategies for improvement are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Perspectives of adolescents on decision making about participation in a biobank study: a pilot study.

PubMed

Grootens-Wiegers, Petronella; Visser, Eline G; van Rossum, Annemarie M C; van Waardhuizen, Claudia N; de Wildt, Saskia N; Sweep, Boudewijn; van den Broek, Jos M; de Vries, Martine C

2017-01-01

To be able to truly involve adolescents in decision making about clinical research participation, we need more insight in the perspective of adolescents themselves. To this end, adolescents in an ongoing biobank study were consulted to test a tentative decision assessment tool. The perspectives of adolescents (n=8) concerning participation in decision making for research participation were explored in interviews with a tentative tool, which covered six topics: information material usage, understanding, disease perceptions, anxiety, decision-making process and role sharing. All adolescents unequivocally expressed the desire to be involved in decision making, but also wanted advice from their parents. The extent of the preferred role of adolescent versus parents varied between individuals. In decision making, adolescents relied on parents for information. More than half hardly used the information material. Adolescents in our study preferred a shared decision-making process. The extent of sharing varied between individuals. The decision assessment tool was a fruitful starting point to discuss adolescents' perspectives and may aid in tailoring the situation to the individual to achieve optimal participation practices. Consulting adolescents about their preferences concerning decision making using the tool will facilitate tailoring of the shared decision-making process and optimising the developing autonomy of minors.

Volatile decision dynamics: experiments, stochastic description, intermittency control and traffic optimization

NASA Astrophysics Data System (ADS)

Helbing, Dirk; Schönhof, Martin; Kern, Daniel

2002-06-01

The coordinated and efficient distribution of limited resources by individual decisions is a fundamental, unsolved problem. When individuals compete for road capacities, time, space, money, goods, etc, they normally make decisions based on aggregate rather than complete information, such as TV news or stock market indices. In related experiments, we have observed a volatile decision dynamics and far-from-optimal payoff distributions. We have also identified methods of information presentation that can considerably improve the overall performance of the system. In order to determine optimal strategies of decision guidance by means of user-specific recommendations, a stochastic behavioural description is developed. These strategies manage to increase the adaptibility to changing conditions and to reduce the deviation from the time-dependent user equilibrium, thereby enhancing the average and individual payoffs. Hence, our guidance strategies can increase the performance of all users by reducing overreaction and stabilizing the decision dynamics. These results are highly significant for predicting decision behaviour, for reaching optimal behavioural distributions by decision support systems and for information service providers. One of the promising fields of application is traffic optimization.

Acquisition of decision making criteria: reward rate ultimately beats accuracy.

PubMed

Balci, Fuat; Simen, Patrick; Niyogi, Ritwik; Saxe, Andrew; Hughes, Jessica A; Holmes, Philip; Cohen, Jonathan D

2011-02-01

Speed-accuracy trade-offs strongly influence the rate of reward that can be earned in many decision-making tasks. Previous reports suggest that human participants often adopt suboptimal speed-accuracy trade-offs in single session, two-alternative forced-choice tasks. We investigated whether humans acquired optimal speed-accuracy trade-offs when extensively trained with multiple signal qualities. When performance was characterized in terms of decision time and accuracy, our participants eventually performed nearly optimally in the case of higher signal qualities. Rather than adopting decision criteria that were individually optimal for each signal quality, participants adopted a single threshold that was nearly optimal for most signal qualities. However, setting a single threshold for different coherence conditions resulted in only negligible decrements in the maximum possible reward rate. Finally, we tested two hypotheses regarding the possible sources of suboptimal performance: (1) favoring accuracy over reward rate and (2) misestimating the reward rate due to timing uncertainty. Our findings provide support for both hypotheses, but also for the hypothesis that participants can learn to approach optimality. We find specifically that an accuracy bias dominates early performance, but diminishes greatly with practice. The residual discrepancy between optimal and observed performance can be explained by an adaptive response to uncertainty in time estimation.

An Integrated Decision-Making Model for Categorizing Weather Products and Decision Aids

NASA Technical Reports Server (NTRS)

Elgin, Peter D.; Thomas, Rickey P.

2004-01-01

The National Airspace System s capacity will experience considerable growth in the next few decades. Weather adversely affects safe air travel. The FAA and NASA are working to develop new technologies that display weather information to support situation awareness and optimize pilot decision-making in avoiding hazardous weather. Understanding situation awareness and naturalistic decision-making is an important step in achieving this goal. Information representation and situation time stress greatly influence attentional resource allocation and working memory capacity, potentially obstructing accurate situation awareness assessments. Three naturalistic decision-making theories were integrated to provide an understanding of the levels of decision making incorporated in three operational situations and two conditions. The task characteristics associated with each phase of flight govern the level of situation awareness attained and the decision making processes utilized. Weather product s attributes and situation task characteristics combine to classify weather products according to the decision-making processes best supported. In addition, a graphical interface is described that affords intuitive selection of the appropriate weather product relative to the pilot s current flight situation.

Regret and the rationality of choices

PubMed Central

Bourgeois-Gironde, Sacha

2010-01-01

Regret helps to optimize decision behaviour. It can be defined as a rational emotion. Several recent neurobiological studies have confirmed the interface between emotion and cognition at which regret is located and documented its role in decision behaviour. These data give credibility to the incorporation of regret in decision theory that had been proposed by economists in the 1980s. However, finer distinctions are required in order to get a better grasp of how regret and behaviour influence each other. Regret can be defined as a predictive error signal but this signal does not necessarily transpose into a decision-weight influencing behaviour. Clinical studies on several types of patients show that the processing of an error signal and its influence on subsequent behaviour can be dissociated. We propose a general understanding of how regret and decision-making are connected in terms of regret being modulated by rational antecedents of choice. Regret and the modification of behaviour on its basis will depend on the criteria of rationality involved in decision-making. We indicate current and prospective lines of research in order to refine our views on how regret contributes to optimal decision-making. PMID:20026463

[Impact of shared-decision making on patient satisfaction].

PubMed

Suh, Won S; Lee, Chae Kyung

2010-01-01

The purpose of this research is to analyze the impact of shared-decision making on patient satisfaction. The study is significant since it focuses on developing appropriate methodologies and analyzing data to identify patient preferences, with the goals of optimizing treatment selection, and substantiating the relationship between such preferences and their impact on outcomes. A thorough literature review that developed the framework illustrating key dimensions of shared decision making was followed by a quantitative assessment and regression analysis of patient-perceived satisfaction, and the degree of shared-decision making. A positive association was evident between shared-decision making and patient satisfaction. The impact of shared decision making on patient satisfaction was greater than other variable including gender, education, and number of visits. Patients who participate in care-related decisions and who are given an explanation of their health problems are more likely to be satisfied with their care. It would benefit health care organizations to train their medical professionals in this communication method, and to include it in their practice guidelines.

The left inferior frontal gyrus is involved in adjusting response bias during a perceptual decision-making task.

PubMed

Reckless, Greg E; Ousdal, Olga T; Server, Andres; Walter, Henrik; Andreassen, Ole A; Jensen, Jimmy

2014-05-01

Changing the way we make decisions from one environment to another allows us to maintain optimal decision-making. One way decision-making may change is how biased one is toward one option or another. Identifying the regions of the brain that underlie the change in bias will allow for a better understanding of flexible decision-making. An event-related, perceptual decision-making task where participants had to detect a picture of an animal amongst distractors was used during functional magnetic resonance imaging. Positive and negative financial motivation were used to affect a change in response bias, and changes in decision-making behavior were quantified using signal detection theory. Response bias became relatively more liberal during both positive and negative motivated trials compared to neutral trials. For both motivational conditions, the larger the liberal shift in bias, the greater the left inferior frontal gyrus (IFG) activity. There was no relationship between individuals' belief that they used a different strategy and their actual change in response bias. The present findings suggest that the left IFG plays a role in adjusting response bias across different decision environments. This suggests a potential role for the left IFG in flexible decision-making.

Structured decision making for managing pneumonia epizootics in bighorn sheep

USGS Publications Warehouse

Sells, Sarah N.; Mitchell, Michael S.; Edwards, Victoria L.; Gude, Justin A.; Anderson, Neil J.

2016-01-01

Good decision-making is essential to conserving wildlife populations. Although there may be multiple ways to address a problem, perfect solutions rarely exist. Managers are therefore tasked with identifying decisions that will best achieve desired outcomes. Structured decision making (SDM) is a method of decision analysis used to identify the most effective, efficient, and realistic decisions while accounting for values and priorities of the decision maker. The stepwise process includes identifying the management problem, defining objectives for solving the problem, developing alternative approaches to achieve the objectives, and formally evaluating which alternative is most likely to accomplish the objectives. The SDM process can be more effective than informal decision-making because it provides a transparent way to quantitatively evaluate decisions for addressing multiple management objectives while incorporating science, uncertainty, and risk tolerance. To illustrate the application of this process to a management need, we present an SDM-based decision tool developed to identify optimal decisions for proactively managing risk of pneumonia epizootics in bighorn sheep (Ovis canadensis) in Montana. Pneumonia epizootics are a major challenge for managers due to long-term impacts to herds, epistemic uncertainty in timing and location of future epizootics, and consequent difficulty knowing how or when to manage risk. The decision tool facilitates analysis of alternative decisions for how to manage herds based on predictions from a risk model, herd-specific objectives, and predicted costs and benefits of each alternative. Decision analyses for 2 example herds revealed that meeting management objectives necessitates specific approaches unique to each herd. The analyses showed how and under what circumstances the alternatives are optimal compared to other approaches and current management. Managers can be confident that these decisions are effective, efficient, and realistic because they explicitly account for important considerations managers implicitly weigh when making decisions, including competing management objectives, uncertainty in potential outcomes, and risk tolerance.

Design of an Aircrew Scheduling Decision Aid for the 6916th Electronic Security Squadron.

DTIC Science & Technology

1987-06-01

Security Classification) Design of an Aircrew Scheduling Decision Aid for the 6916th Electronic Security Squadron 12. PERSONAL AUTHOR(S) Thomas J. Kopf...Because of the great number of possible scheduling alternatives, it is difficult to find an optimal solution to-the scheduling problem. Additionally...changes to the original schedule make it even more difficult to find an optimal solution. The emergence of capable microcompu- ters, decision support

Three-class ROC analysis--the equal error utility assumption and the optimality of three-class ROC surface using the ideal observer.

PubMed

He, Xin; Frey, Eric C

2006-08-01

Previously, we have developed a decision model for three-class receiver operating characteristic (ROC) analysis based on decision theory. The proposed decision model maximizes the expected decision utility under the assumption that incorrect decisions have equal utilities under the same hypothesis (equal error utility assumption). This assumption reduced the dimensionality of the "general" three-class ROC analysis and provided a practical figure-of-merit to evaluate the three-class task performance. However, it also limits the generality of the resulting model because the equal error utility assumption will not apply for all clinical three-class decision tasks. The goal of this study was to investigate the optimality of the proposed three-class decision model with respect to several other decision criteria. In particular, besides the maximum expected utility (MEU) criterion used in the previous study, we investigated the maximum-correctness (MC) (or minimum-error), maximum likelihood (ML), and Nyman-Pearson (N-P) criteria. We found that by making assumptions for both MEU and N-P criteria, all decision criteria lead to the previously-proposed three-class decision model. As a result, this model maximizes the expected utility under the equal error utility assumption, maximizes the probability of making correct decisions, satisfies the N-P criterion in the sense that it maximizes the sensitivity of one class given the sensitivities of the other two classes, and the resulting ROC surface contains the maximum likelihood decision operating point. While the proposed three-class ROC analysis model is not optimal in the general sense due to the use of the equal error utility assumption, the range of criteria for which it is optimal increases its applicability for evaluating and comparing a range of diagnostic systems.

The impact of prostate cancer diagnosis and treatment decision-making on health-related quality of life before treatment onset.

PubMed

Cuypers, Maarten; Lamers, Romy E D; Cornel, Erik B; van de Poll-Franse, Lonneke V; de Vries, Marieke; Kil, Paul J M

2018-04-01

The objective of this study is to test if patients' health-related quality of life (HRQoL) declines after prostate biopsy to detect Pca, and after subsequent treatment decision-making in case Pca is confirmed, and to test whether personality state and traits are associated with these potential changes in HRQoL. Patients who were scheduled for prostate biopsy to detect Pca (N = 377) filled out a baseline questionnaire about HRQoL (EORTC QLQ-C30 and PR25), "big five" personality traits (BFI-10), optimism (LOT-r), and self-efficacy (Decision Self-efficacy Scale) (t0). Patients with confirmed Pca (N = 126) filled out a follow-up questionnaire on HRQoL within 2 weeks after treatment was chosen but had not yet started (t1). HRQoL declined between t0 and t1, reflected in impaired role and cognitive functioning, and elevated fatigue, constipation, and prostate-specific symptoms. Sexual activity and functioning improved. Baseline HRQoL scores were unrelated to the selection of a particular treatment, but for patients who chose a curative treatment, post-decision HRQoL showed a greater decline compared to patients who chose active surveillance. Optimism was associated with HRQoL at baseline; decisional self-efficacy was positively associated with HRQoL at follow-up. No associations between HRQoL and the "big five" personality traits were found. Patients who have undergone prostate biopsy and treatment decision-making for Pca experience a decline in HRQoL. Choosing treatment with a curative intent was associated with greater decline in HRQoL. Interventions aimed at optimism and decision self-efficacy could be helpful to reduce HRQoL impairment around the time of prostate biopsy and treatment decision-making.

Decision-making without a brain: how an amoeboid organism solves the two-armed bandit.

PubMed

Reid, Chris R; MacDonald, Hannelore; Mann, Richard P; Marshall, James A R; Latty, Tanya; Garnier, Simon

2016-06-01

Several recent studies hint at shared patterns in decision-making between taxonomically distant organisms, yet few studies demonstrate and dissect mechanisms of decision-making in simpler organisms. We examine decision-making in the unicellular slime mould Physarum polycephalum using a classical decision problem adapted from human and animal decision-making studies: the two-armed bandit problem. This problem has previously only been used to study organisms with brains, yet here we demonstrate that a brainless unicellular organism compares the relative qualities of multiple options, integrates over repeated samplings to perform well in random environments, and combines information on reward frequency and magnitude in order to make correct and adaptive decisions. We extend our inquiry by using Bayesian model selection to determine the most likely algorithm used by the cell when making decisions. We deduce that this algorithm centres around a tendency to exploit environments in proportion to their reward experienced through past sampling. The algorithm is intermediate in computational complexity between simple, reactionary heuristics and calculation-intensive optimal performance algorithms, yet it has very good relative performance. Our study provides insight into ancestral mechanisms of decision-making and suggests that fundamental principles of decision-making, information processing and even cognition are shared among diverse biological systems. © 2016 The Authors.

Expected value information improves financial risk taking across the adult life span.

PubMed

Samanez-Larkin, Gregory R; Wagner, Anthony D; Knutson, Brian

2011-04-01

When making decisions, individuals must often compensate for cognitive limitations, particularly in the face of advanced age. Recent findings suggest that age-related variability in striatal activity may increase financial risk-taking mistakes in older adults. In two studies, we sought to further characterize neural contributions to optimal financial risk taking and to determine whether decision aids could improve financial risk taking. In Study 1, neuroimaging analyses revealed that individuals whose mesolimbic activation correlated with the expected value estimates of a rational actor made more optimal financial decisions. In Study 2, presentation of expected value information improved decision making in both younger and older adults, but the addition of a distracting secondary task had little impact on decision quality. Remarkably, provision of expected value information improved the performance of older adults to match that of younger adults at baseline. These findings are consistent with the notion that mesolimbic circuits play a critical role in optimal choice, and imply that providing simplified information about expected value may improve financial risk taking across the adult life span.

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

NASA Astrophysics Data System (ADS)

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

2010-03-01

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

Framing of task performance strategies: effects on performance in a multiattribute dynamic decision making environment.

PubMed

Nygren, T E

1997-09-01

It is well documented that the way a static choice task is "framed" can dramatically alter choice behavior, often leading to observable preference reversals. This framing effect appears to result from perceived changes in the nature or location of a person's initial reference point, but it is not clear how framing effects might generalize to performance on dynamic decision making tasks that are characterized by high workload, time constraints, risk, or stress. A study was conducted to examine the hypothesis that framing can introduce affective components to the decision making process and can influence, either favorably (positive frame) or adversely (negative frame), the implementation and use of decision making strategies in dynamic high-workload environments. Results indicated that negative frame participants were significantly impaired in developing and employing a simple optimal decision strategy relative to a positive frame group. Discussion focuses on implications of these results for models of dynamic decision making.

Application of Bayesian and cost benefit risk analysis in water resources management

NASA Astrophysics Data System (ADS)

Varouchakis, E. A.; Palogos, I.; Karatzas, G. P.

2016-03-01

Decision making is a significant tool in water resources management applications. This technical note approaches a decision dilemma that has not yet been considered for the water resources management of a watershed. A common cost-benefit analysis approach, which is novel in the risk analysis of hydrologic/hydraulic applications, and a Bayesian decision analysis are applied to aid the decision making on whether or not to construct a water reservoir for irrigation purposes. The alternative option examined is a scaled parabolic fine variation in terms of over-pumping violations in contrast to common practices that usually consider short-term fines. The methodological steps are analytically presented associated with originally developed code. Such an application, and in such detail, represents new feedback. The results indicate that the probability uncertainty is the driving issue that determines the optimal decision with each methodology, and depending on the unknown probability handling, each methodology may lead to a different optimal decision. Thus, the proposed tool can help decision makers to examine and compare different scenarios using two different approaches before making a decision considering the cost of a hydrologic/hydraulic project and the varied economic charges that water table limit violations can cause inside an audit interval. In contrast to practices that assess the effect of each proposed action separately considering only current knowledge of the examined issue, this tool aids decision making by considering prior information and the sampling distribution of future successful audits.

Requiem for the max rule?

PubMed Central

Ma, Wei Ji; Shen, Shan; Dziugaite, Gintare; van den Berg, Ronald

2015-01-01

In tasks such as visual search and change detection, a key question is how observers integrate noisy measurements from multiple locations to make a decision. Decision rules proposed to model this process haven fallen into two categories: Bayes-optimal (ideal observer) rules and ad-hoc rules. Among the latter, the maximum-of-outputs (max) rule has been most prominent. Reviewing recent work and performing new model comparisons across a range of paradigms, we find that in all cases except for one, the optimal rule describes human data as well as or better than every max rule either previously proposed or newly introduced here. This casts doubt on the utility of the max rule for understanding perceptual decision-making. PMID:25584425

Algorithms for optimizing the treatment of depression: making the right decision at the right time.

PubMed

Adli, M; Rush, A J; Möller, H-J; Bauer, M

2003-11-01

Medication algorithms for the treatment of depression are designed to optimize both treatment implementation and the appropriateness of treatment strategies. Thus, they are essential tools for treating and avoiding refractory depression. Treatment algorithms are explicit treatment protocols that provide specific therapeutic pathways and decision-making tools at critical decision points throughout the treatment process. The present article provides an overview of major projects of algorithm research in the field of antidepressant therapy. The Berlin Algorithm Project and the Texas Medication Algorithm Project (TMAP) compare algorithm-guided treatments with treatment as usual. The Sequenced Treatment Alternatives to Relieve Depression Project (STAR*D) compares different treatment strategies in treatment-resistant patients.

Demographics of reintroduced populations: estimation, modeling, and decision analysis

USGS Publications Warehouse

Converse, Sarah J.; Moore, Clinton T.; Armstrong, Doug P.

2013-01-01

Reintroduction can be necessary for recovering populations of threatened species. However, the success of reintroduction efforts has been poorer than many biologists and managers would hope. To increase the benefits gained from reintroduction, management decision making should be couched within formal decision-analytic frameworks. Decision analysis is a structured process for informing decision making that recognizes that all decisions have a set of components—objectives, alternative management actions, predictive models, and optimization methods—that can be decomposed, analyzed, and recomposed to facilitate optimal, transparent decisions. Because the outcome of interest in reintroduction efforts is typically population viability or related metrics, models used in decision analysis efforts for reintroductions will need to include population models. In this special section of the Journal of Wildlife Management, we highlight examples of the construction and use of models for informing management decisions in reintroduced populations. In this introductory contribution, we review concepts in decision analysis, population modeling for analysis of decisions in reintroduction settings, and future directions. Increased use of formal decision analysis, including adaptive management, has great potential to inform reintroduction efforts. Adopting these practices will require close collaboration among managers, decision analysts, population modelers, and field biologists.

Making the Optimal Decision in Selecting Protective Clothing

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

Price, J. Mark

2008-01-15

Protective Clothing plays a major role in the decommissioning and operation of nuclear facilities. Literally thousands of dress-outs occur over the life of a decommissioning project and during outages at operational plants. In order to make the optimal decision on which type of protective clothing is best suited for the decommissioning or maintenance and repair work on radioactive systems, a number of interrelating factors must be considered. This article discusses these factors as well as surveys of plants regarding their level of usage of single use protective clothing and should help individuals making decisions about protective clothing as it appliesmore » to their application. Individuals considering using SUPC should not jump to conclusions. The survey conducted clearly indicates that plants have different drivers. An evaluation should be performed to understand the facility's true drivers for selecting clothing. It is recommended that an interdisciplinary team be formed including representatives from budgets and cost, safety, radwaste, health physics, and key user groups to perform the analysis. The right questions need to be asked and answered by the company providing the clothing to formulate a proper perspective and conclusion. The conclusions and recommendations need to be shared with senior management so that the drivers, expected results, and associated costs are understood and endorsed. In the end, the individual making the recommendation should ask himself/herself: 'Is my decision emotional, or logical and economical?' 'Have I reached the optimal decision for my plant?'.« less

The Pathways fertility preservation decision aid website for women with cancer: development and field testing.

PubMed

Woodard, Terri L; Hoffman, Aubri S; Covarrubias, Laura A; Holman, Deborah; Schover, Leslie; Bradford, Andrea; Hoffman, Derek B; Mathur, Aakrati; Thomas, Jerah; Volk, Robert J

2018-02-01

To improve survivors' awareness and knowledge of fertility preservation counseling and treatment options, this study engaged survivors and providers to design, develop, and field-test Pathways: a fertility preservation patient decision aid website for young women with cancer©. Using an adapted user-centered design process, our stakeholder advisory group and research team designed and optimized the Pathways patient decision aid website through four iterative cycles of review and revision with clinicians (n = 21) and survivors (n = 14). Field-testing (n = 20 survivors) assessed post-decision aid scores on the Fertility Preservation Knowledge Scale, feasibility of assessing women's decision-making values while using the website, and website usability/acceptability ratings. Iterative stakeholder engagement optimized the Pathways decision aid website to meet survivors' and providers' needs, including providing patient-friendly information and novel features such as interactive value clarification exercises, testimonials that model shared decision making, financial/referral resources, and a printable personal summary. Survivors scored an average of 8.2 out of 13 (SD 1.6) on the Fertility Preservation Knowledge Scale. They rated genetic screening and having a biological child as strong factors in their decision-making, and 71% indicated a preference for egg freezing. Most women (> 85%) rated Pathways favorably, and all women (100%) said they would recommend it to other women. The Pathways decision aid is a usable and acceptable tool to help women learn about fertility preservation. The Pathways decision aid may help women make well-informed values-based decisions and prevent future infertility-related distress.

Models of expert assessments and their study in problems of choice and decision-making in management of motor transport processes

NASA Astrophysics Data System (ADS)

Belokurov, V. P.; Belokurov, S. V.; Korablev, R. A.; Shtepa, A. A.

2018-05-01

The article deals with decision making concerning transport tasks on search iterations in the management of motor transport processes. An optimal selection of the best option for specific situations is suggested in the management of complex multi-criteria transport processes.

Some Ideas on the Microcomputer and the Information/Knowledge Workstation.

ERIC Educational Resources Information Center

Boon, J. A.; Pienaar, H.

1989-01-01

Identifies the optimal goal of knowledge workstations as the harmony of technology and human decision-making behaviors. Two types of decision-making processes are described and the application of each type to experimental and/or operational situations is discussed. Suggestions for technical solutions to machine-user interfaces are then offered.…

Improving Decision Making Skill Using an Online Volcanic Crisis Simulation: Impact of Data Presentation Format

ERIC Educational Resources Information Center

Barclay, Elizabeth J.; Renshaw, Carl E.; Taylor, Holly A.; Bilge, A. Reyan

2011-01-01

Creating effective computer-based learning exercises requires an understanding of optimal user interface designs for improving higher order cognitive skills. Using an online volcanic crisis simulation previously shown to improve decision making skill, we find that a user interface using a graphical presentation of the volcano monitoring data…

The neural basis of financial risk taking.

PubMed

Kuhnen, Camelia M; Knutson, Brian

2005-09-01

Investors systematically deviate from rationality when making financial decisions, yet the mechanisms responsible for these deviations have not been identified. Using event-related fMRI, we examined whether anticipatory neural activity would predict optimal and suboptimal choices in a financial decision-making task. We characterized two types of deviations from the optimal investment strategy of a rational risk-neutral agent as risk-seeking mistakes and risk-aversion mistakes. Nucleus accumbens activation preceded risky choices as well as risk-seeking mistakes, while anterior insula activation preceded riskless choices as well as risk-aversion mistakes. These findings suggest that distinct neural circuits linked to anticipatory affect promote different types of financial choices and indicate that excessive activation of these circuits may lead to investing mistakes. Thus, consideration of anticipatory neural mechanisms may add predictive power to the rational actor model of economic decision making.

The Dilution Effect and Information Integration in Perceptual Decision Making

PubMed Central

Hotaling, Jared M.; Cohen, Andrew L.; Shiffrin, Richard M.; Busemeyer, Jerome R.

2015-01-01

In cognitive science there is a seeming paradox: On the one hand, studies of human judgment and decision making have repeatedly shown that people systematically violate optimal behavior when integrating information from multiple sources. On the other hand, optimal models, often Bayesian, have been successful at accounting for information integration in fields such as categorization, memory, and perception. This apparent conflict could be due, in part, to different materials and designs that lead to differences in the nature of processing. Stimuli that require controlled integration of information, such as the quantitative or linguistic information (commonly found in judgment studies), may lead to suboptimal performance. In contrast, perceptual stimuli may lend themselves to automatic processing, resulting in integration that is closer to optimal. We tested this hypothesis with an experiment in which participants categorized faces based on resemblance to a family patriarch. The amount of evidence contained in the top and bottom halves of each test face was independently manipulated. These data allow us to investigate a canonical example of sub-optimal information integration from the judgment and decision making literature, the dilution effect. Splitting the top and bottom halves of a face, a manipulation meant to encourage controlled integration of information, produced farther from optimal behavior and larger dilution effects. The Multi-component Information Accumulation model, a hybrid optimal/averaging model of information integration, successfully accounts for key accuracy, response time, and dilution effects. PMID:26406323

The Dilution Effect and Information Integration in Perceptual Decision Making.

PubMed

Hotaling, Jared M; Cohen, Andrew L; Shiffrin, Richard M; Busemeyer, Jerome R

2015-01-01

In cognitive science there is a seeming paradox: On the one hand, studies of human judgment and decision making have repeatedly shown that people systematically violate optimal behavior when integrating information from multiple sources. On the other hand, optimal models, often Bayesian, have been successful at accounting for information integration in fields such as categorization, memory, and perception. This apparent conflict could be due, in part, to different materials and designs that lead to differences in the nature of processing. Stimuli that require controlled integration of information, such as the quantitative or linguistic information (commonly found in judgment studies), may lead to suboptimal performance. In contrast, perceptual stimuli may lend themselves to automatic processing, resulting in integration that is closer to optimal. We tested this hypothesis with an experiment in which participants categorized faces based on resemblance to a family patriarch. The amount of evidence contained in the top and bottom halves of each test face was independently manipulated. These data allow us to investigate a canonical example of sub-optimal information integration from the judgment and decision making literature, the dilution effect. Splitting the top and bottom halves of a face, a manipulation meant to encourage controlled integration of information, produced farther from optimal behavior and larger dilution effects. The Multi-component Information Accumulation model, a hybrid optimal/averaging model of information integration, successfully accounts for key accuracy, response time, and dilution effects.

Why we should use animals to study economic decision making - a perspective.

PubMed

Kalenscher, Tobias; van Wingerden, Marijn

2011-01-01

Despite the rich tradition in psychology and biology, animals as research subjects have never gained a similar acceptance in microeconomics research. With this article, we counter this trend of negligence and try to convey the message that animal models are an indispensible complement to the literature on human economic decision making. This perspective review departs from a description of the similarities in economic and evolutionary theories of human and animal decision making, with particular emphasis on the optimality aspect that both classes of theories have in common. In a second part, we outline that actual, empirically observed decisions often do not conform to the normative ideals of economic and ecological models, and that many of the behavioral violations found in humans can also be found in animals. In a third part, we make a case that the sense or nonsense of the behavioral violations of optimality principles in humans can best be understood from an evolutionary perspective, thus requiring animal research. Finally, we conclude with a critical discussion of the parallels and inherent differences in human and animal research.

Why We Should Use Animals to Study Economic Decision Making – A Perspective

PubMed Central

Kalenscher, Tobias; van Wingerden, Marijn

2011-01-01

Despite the rich tradition in psychology and biology, animals as research subjects have never gained a similar acceptance in microeconomics research. With this article, we counter this trend of negligence and try to convey the message that animal models are an indispensible complement to the literature on human economic decision making. This perspective review departs from a description of the similarities in economic and evolutionary theories of human and animal decision making, with particular emphasis on the optimality aspect that both classes of theories have in common. In a second part, we outline that actual, empirically observed decisions often do not conform to the normative ideals of economic and ecological models, and that many of the behavioral violations found in humans can also be found in animals. In a third part, we make a case that the sense or nonsense of the behavioral violations of optimality principles in humans can best be understood from an evolutionary perspective, thus requiring animal research. Finally, we conclude with a critical discussion of the parallels and inherent differences in human and animal research. PMID:21731558

Systems identification and the adaptive management of waterfowl in the United States

USGS Publications Warehouse

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

2001-01-01

Waterfowl management in the United States is one of the more visible conservation success stories in the United States. It is authorized and supported by appropriate legislative authorities, based on large-scale monitoring programs, and widely accepted by the public. The process is one of only a limited number of large-scale examples of effective collaboration between research and management, integrating scientific information with management in a coherent framework for regulatory decision-making. However, harvest management continues to face some serious technical problems, many of which focus on sequential identification of the resource system in a context of optimal decision-making. The objective of this paper is to provide a theoretical foundation of adaptive harvest management, the approach currently in use in the United States for regulatory decision-making. We lay out the legal and institutional framework for adaptive harvest management and provide a formal description of regulatory decision-making in terms of adaptive optimization. We discuss some technical and institutional challenges in applying adaptive harvest management and focus specifically on methods of estimating resource states for linear resource systems.

Chronic Stress Alters Striosome-Circuit Dynamics, Leading to Aberrant Decision-Making.

PubMed

Friedman, Alexander; Homma, Daigo; Bloem, Bernard; Gibb, Leif G; Amemori, Ken-Ichi; Hu, Dan; Delcasso, Sebastien; Truong, Timothy F; Yang, Joyce; Hood, Adam S; Mikofalvy, Katrina A; Beck, Dirk W; Nguyen, Norah; Nelson, Erik D; Toro Arana, Sebastian E; Vorder Bruegge, Ruth H; Goosens, Ki A; Graybiel, Ann M

2017-11-16

Effective evaluation of costs and benefits is a core survival capacity that in humans is considered as optimal, "rational" decision-making. This capacity is vulnerable in neuropsychiatric disorders and in the aftermath of chronic stress, in which aberrant choices and high-risk behaviors occur. We report that chronic stress exposure in rodents produces abnormal evaluation of costs and benefits resembling non-optimal decision-making in which choices of high-cost/high-reward options are sharply increased. Concomitantly, alterations in the task-related spike activity of medial prefrontal neurons correspond with increased activity of their striosome-predominant striatal projection neuron targets and with decreased and delayed striatal fast-firing interneuron activity. These effects of chronic stress on prefronto-striatal circuit dynamics could be blocked or be mimicked by selective optogenetic manipulation of these circuits. We suggest that altered excitation-inhibition dynamics of striosome-based circuit function could be an underlying mechanism by which chronic stress contributes to disorders characterized by aberrant decision-making under conflict. VIDEO ABSTRACT. Copyright © 2017 Elsevier Inc. All rights reserved.

Equality bias impairs collective decision-making across cultures

PubMed Central

Mahmoodi, Ali; Bang, Dan; Olsen, Karsten; Zhao, Yuanyuan Aimee; Shi, Zhenhao; Broberg, Kristina; Safavi, Shervin; Han, Shihui; Nili Ahmadabadi, Majid; Frith, Chris D.; Roepstorff, Andreas; Rees, Geraint; Bahrami, Bahador

2015-01-01

We tend to think that everyone deserves an equal say in a debate. This seemingly innocuous assumption can be damaging when we make decisions together as part of a group. To make optimal decisions, group members should weight their differing opinions according to how competent they are relative to one another; whenever they differ in competence, an equal weighting is suboptimal. Here, we asked how people deal with individual differences in competence in the context of a collective perceptual decision-making task. We developed a metric for estimating how participants weight their partner’s opinion relative to their own and compared this weighting to an optimal benchmark. Replicated across three countries (Denmark, Iran, and China), we show that participants assigned nearly equal weights to each other’s opinions regardless of true differences in their competence—even when informed by explicit feedback about their competence gap or under monetary incentives to maximize collective accuracy. This equality bias, whereby people behave as if they are as good or as bad as their partner, is particularly costly for a group when a competence gap separates its members. PMID:25775532

Equality bias impairs collective decision-making across cultures.

PubMed

Mahmoodi, Ali; Bang, Dan; Olsen, Karsten; Zhao, Yuanyuan Aimee; Shi, Zhenhao; Broberg, Kristina; Safavi, Shervin; Han, Shihui; Nili Ahmadabadi, Majid; Frith, Chris D; Roepstorff, Andreas; Rees, Geraint; Bahrami, Bahador

2015-03-24

We tend to think that everyone deserves an equal say in a debate. This seemingly innocuous assumption can be damaging when we make decisions together as part of a group. To make optimal decisions, group members should weight their differing opinions according to how competent they are relative to one another; whenever they differ in competence, an equal weighting is suboptimal. Here, we asked how people deal with individual differences in competence in the context of a collective perceptual decision-making task. We developed a metric for estimating how participants weight their partner's opinion relative to their own and compared this weighting to an optimal benchmark. Replicated across three countries (Denmark, Iran, and China), we show that participants assigned nearly equal weights to each other's opinions regardless of true differences in their competence-even when informed by explicit feedback about their competence gap or under monetary incentives to maximize collective accuracy. This equality bias, whereby people behave as if they are as good or as bad as their partner, is particularly costly for a group when a competence gap separates its members.

Multiple constraints compromise decision-making about implantable medical devices for individual patients: qualitative interviews with physicians.

PubMed

Gagliardi, Anna R; Ducey, Ariel; Lehoux, Pascale; Turgeon, Thomas; Kolbunik, Jeremy; Ross, Sue; Trbovich, Patricia; Easty, Anthony; Bell, Chaim; Urbach, David R

2017-12-22

Little research has examined how physicians choose medical devices for treating individual patients to reveal if interventions are needed to support decision-making and reduce device-associated morbidity and mortality. This study explored factors that influence choice of implantable device from among available options. A descriptive qualitative approach was used. Physicians who implant orthopedic and cardiovascular devices were identified in publicly available directories and web sites. They were asked how they decided what device to use in a given patient, sources of information they consulted, and how patients were engaged in decision-making. Sampling was concurrent with data collection and analysis to achieve thematic saturation. Data were analyzed using constant comparative technique by all members of the research team. Twenty-two physicians from five Canadian provinces (10 cardiovascular, 12 orthopedic; 8, 10 and 4 early, mid and late career, respectively) were interviewed. Responses did not differ by specialty, geographic region or career stage. Five major categories of themes emerged that all influence decision-making about a range of devices, and often compromise choice of the most suitable device for a given patient, potentially leading to sub-optimal clinical outcomes: lack of evidence on device performance, patient factors, physician factors, organizational and health system factors, and device and device market factors. In the absence of evidence from research or device registries, tacit knowledge from trusted colleagues and less-trusted industry representatives informed device choice. Patients were rarely engaged in decision-making. Physician preference for particular devices was a barrier to acquiring competency in devices potentially more suitable for patients. Access to suitable devices was further limited to the number of comparable devices on the market, local inventory and purchasing contract specifications. This study revealed that decision-making about devices is complex, cognitively challenging and constrained by several factors limiting access to and use of devices that could optimize patient outcomes. Further research is needed to assess the impact of these constraints on clinical outcomes, and develop interventions that optimize decision-making about device choice for treating given patients.

Intergroup Conflict and Rational Decision Making

PubMed Central

Martínez-Tur, Vicente; Peñarroja, Vicente; Serrano, Miguel A.; Hidalgo, Vanesa; Moliner, Carolina; Salvador, Alicia; Alacreu-Crespo, Adrián; Gracia, Esther; Molina, Agustín

2014-01-01

The literature has been relatively silent about post-conflict processes. However, understanding the way humans deal with post-conflict situations is a challenge in our societies. With this in mind, we focus the present study on the rationality of cooperative decision making after an intergroup conflict, i.e., the extent to which groups take advantage of post-conflict situations to obtain benefits from collaborating with the other group involved in the conflict. Based on dual-process theories of thinking and affect heuristic, we propose that intergroup conflict hinders the rationality of cooperative decision making. We also hypothesize that this rationality improves when groups are involved in an in-group deliberative discussion. Results of a laboratory experiment support the idea that intergroup conflict –associated with indicators of the activation of negative feelings (negative affect state and heart rate)– has a negative effect on the aforementioned rationality over time and on both group and individual decision making. Although intergroup conflict leads to sub-optimal decision making, rationality improves when groups and individuals subjected to intergroup conflict make decisions after an in-group deliberative discussion. Additionally, the increased rationality of the group decision making after the deliberative discussion is transferred to subsequent individual decision making. PMID:25461384

Intergroup conflict and rational decision making.

PubMed

Martínez-Tur, Vicente; Peñarroja, Vicente; Serrano, Miguel A; Hidalgo, Vanesa; Moliner, Carolina; Salvador, Alicia; Alacreu-Crespo, Adrián; Gracia, Esther; Molina, Agustín

2014-01-01

The literature has been relatively silent about post-conflict processes. However, understanding the way humans deal with post-conflict situations is a challenge in our societies. With this in mind, we focus the present study on the rationality of cooperative decision making after an intergroup conflict, i.e., the extent to which groups take advantage of post-conflict situations to obtain benefits from collaborating with the other group involved in the conflict. Based on dual-process theories of thinking and affect heuristic, we propose that intergroup conflict hinders the rationality of cooperative decision making. We also hypothesize that this rationality improves when groups are involved in an in-group deliberative discussion. Results of a laboratory experiment support the idea that intergroup conflict -associated with indicators of the activation of negative feelings (negative affect state and heart rate)- has a negative effect on the aforementioned rationality over time and on both group and individual decision making. Although intergroup conflict leads to sub-optimal decision making, rationality improves when groups and individuals subjected to intergroup conflict make decisions after an in-group deliberative discussion. Additionally, the increased rationality of the group decision making after the deliberative discussion is transferred to subsequent individual decision making.

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

A new web-based framework development for fuzzy multi-criteria group decision-making.

PubMed

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

2016-01-01

Fuzzy multi-criteria group decision making (FMCGDM) process is usually used when a group of decision-makers faces imprecise data or linguistic variables to solve the problems. However, this process contains many methods that require many time-consuming calculations depending on the number of criteria, alternatives and decision-makers in order to reach the optimal solution. In this study, a web-based FMCGDM framework that offers decision-makers a fast and reliable response service is proposed. The proposed framework includes commonly used tools for multi-criteria decision-making problems such as fuzzy Delphi, fuzzy AHP and fuzzy TOPSIS methods. The integration of these methods enables taking advantages of the strengths and complements each method's weakness. Finally, a case study of location selection for landfill waste in Morocco is performed to demonstrate how this framework can facilitate decision-making process. The results demonstrate that the proposed framework can successfully accomplish the goal of this study.

Adaptive neural coding: from biological to behavioral decision-making

PubMed Central

Louie, Kenway; Glimcher, Paul W.; Webb, Ryan

2015-01-01

Empirical decision-making in diverse species deviates from the predictions of normative choice theory, but why such suboptimal behavior occurs is unknown. Here, we propose that deviations from optimality arise from biological decision mechanisms that have evolved to maximize choice performance within intrinsic biophysical constraints. Sensory processing utilizes specific computations such as divisive normalization to maximize information coding in constrained neural circuits, and recent evidence suggests that analogous computations operate in decision-related brain areas. These adaptive computations implement a relative value code that may explain the characteristic context-dependent nature of behavioral violations of classical normative theory. Examining decision-making at the computational level thus provides a crucial link between the architecture of biological decision circuits and the form of empirical choice behavior. PMID:26722666

Why humans deviate from rational choice.

PubMed

Hewig, Johannes; Kretschmer, Nora; Trippe, Ralf H; Hecht, Holger; Coles, Michael G H; Holroyd, Clay B; Miltner, Wolfgang H R

2011-04-01

Rational choice theory predicts that humans always optimize the expected utility of options when making decisions. However, in decision-making games, humans often punish their opponents even when doing so reduces their own reward. We used the Ultimatum and Dictator games to examine the affective correlates of decision-making. We show that the feedback negativity, an event-related brain potential that originates in the anterior cingulate cortex that has been related to reinforcement learning, predicts the decision to reject unfair offers in the Ultimatum game. Furthermore, the decision to reject is positively related to more negative emotional reactions and to increased autonomic nervous system activity. These findings support the idea that subjective emotional markers guide decision-making and that the anterior cingulate cortex integrates instances of reinforcement and punishment to provide such affective markers. Copyright © 2010 Society for Psychophysiological Research.

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

NASA Astrophysics Data System (ADS)

Wikman-Svahn, Per

2013-04-01

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

Multi-disciplinary decision making in general practice.

PubMed

Kirby, Ann; Murphy, Aileen; Bradley, Colin

2018-04-09

Purpose Internationally, healthcare systems are moving towards delivering care in an integrated manner which advocates a multi-disciplinary approach to decision making. Such an approach is formally encouraged in the management of Atrial Fibrillation patients through the European Society of Cardiology guidelines. Since the emergence of new oral anticoagulants switching between oral anticoagulants (OACs) has become prevalent. This case study considers the role of multi-disciplinary decision making, given the complex nature of the agents. The purpose of this paper is to explore Irish General Practitioners' (GPs) experience of switching between all OACs for Arial Fibrillation (AF) patients; prevalence of multi-disciplinary decision making in OAC switching decisions and seeks to determine the GP characteristics that appear to influence the likelihood of multi-disciplinary decision making. Design/methodology/approach A probit model is used to determine the factors influencing multi-disciplinary decision making and a multinomial logit is used to examine the factors influencing who is involved in the multi-disciplinary decisions. Findings Results reveal that while some multi-disciplinary decision-making is occurring (64 per cent), it is not standard practice despite international guidelines on integrated care. Moreover, there is a lack of patient participation in the decision-making process. Female GPs and GPs who have initiated prescriptions for OACs are more likely to engage in multi-disciplinary decision-making surrounding switching OACs amongst AF patients. GPs with training practices were less likely to engage with cardiac consultants and those in urban areas were more likely to engage with other (non-cardiac) consultants. Originality/value For optimal decision making under uncertainty multi-disciplinary decision-making is needed to make a more informed judgement and to improve treatment decisions and reduce the opportunity cost of making the wrong decision.

Development of a model to guide decision making in amyotrophic lateral sclerosis multidisciplinary care.

PubMed

Hogden, Anne; Greenfield, David; Nugus, Peter; Kiernan, Matthew C

2015-10-01

Patients with amyotrophic lateral sclerosis (ALS) face numerous decisions for symptom management and quality of life. Models of decision making in chronic disease and cancer care are insufficient for the complex and changing needs of patients with ALS . The aim was to examine the question: how can decision making that is both effective and patient-centred be enacted in ALS multidisciplinary care? Fifty-four respondents (32 health professionals, 14 patients and eight carers) from two specialized ALS multidisciplinary clinics participated in semi-structured interviews. Interviews were transcribed, coded and analysed thematically. Comparison of stakeholder perspectives revealed six key themes of ALS decision making. These were the decision-making process; patient-centred focus; timing and planning; information sources; engagement with specialized ALS services; and access to non-specialized services. A model, embedded in the specialized ALS multidisciplinary clinic, was derived to guide patient decision making. The model is cyclic, with four stages: 'Participant Engagement'; 'Option Information'; 'Option Deliberation'; and 'Decision Implementation'. Effective and patient-centred decision making is enhanced by the structure of the specialized ALS clinic, which promotes patients' symptom management and quality of life goals. However, patient and carer engagement in ALS decision making is tested by the dynamic nature of ALS, and patient and family distress. Our model optimizes patient-centred decision making, by incorporating patients' cyclic decision-making patterns and facilitating carer inclusion in decision processes. The model captures the complexities of patient-centred decision making in ALS. The framework can assist patients and carers, health professionals, researchers and policymakers in this challenging disease environment. © 2013 John Wiley & Sons Ltd.

Systematic design for trait introgression projects.

PubMed

Cameron, John N; Han, Ye; Wang, Lizhi; Beavis, William D

2017-10-01

Using an Operations Research approach, we demonstrate design of optimal trait introgression projects with respect to competing objectives. We demonstrate an innovative approach for designing Trait Introgression (TI) projects based on optimization principles from Operations Research. If the designs of TI projects are based on clear and measurable objectives, they can be translated into mathematical models with decision variables and constraints that can be translated into Pareto optimality plots associated with any arbitrary selection strategy. The Pareto plots can be used to make rational decisions concerning the trade-offs between maximizing the probability of success while minimizing costs and time. The systematic rigor associated with a cost, time and probability of success (CTP) framework is well suited to designing TI projects that require dynamic decision making. The CTP framework also revealed that previously identified 'best' strategies can be improved to be at least twice as effective without increasing time or expenses.

Impaired strategic decision making in schizophrenia.

PubMed

Kim, Hyojin; Lee, Daeyeol; Shin, Young-Min; Chey, Jeanyung

2007-11-14

Adaptive decision making in dynamic social settings requires frequent re-evaluation of choice outcomes and revision of strategies. This requires an array of multiple cognitive abilities, such as working memory and response inhibition. Thus, the disruption of such abilities in schizophrenia can have significant implications for social dysfunctions in affected patients. In the present study, 20 schizophrenia patients and 20 control subjects completed two computerized binary decision-making tasks. In the first task, the participants played a competitive zero-sum game against a computer in which the predictable choice behavior was penalized and the optimal strategy was to choose the two targets stochastically. In the second task, the expected payoffs of the two targets were fixed and unaffected by the subject's choices, so the optimal strategy was to choose the target with the higher expected payoff exclusively. The schizophrenia patients earned significantly less money during the first task, even though their overall choice probabilities were not significantly different from the control subjects. This was mostly because patients were impaired in integrating the outcomes of their previous choices appropriately in order to maintain the optimal strategy. During the second task, the choices of patients and control subjects displayed more similar patterns. This study elucidated the specific components in strategic decision making that are impaired in schizophrenia. The deficit, which can be characterized as strategic stiffness, may have implications for the poor social adjustment in schizophrenia patients.

Certainty, leaps of faith, and tradition: rethinking clinical interventions.

PubMed

Dzurec, L C

1998-12-01

Clinical decision making requires that clinicians think quickly and in ways that will foster optimal, safe client care. Tradition influences clinical decision making, enhancing efficiency of resulting nursing action; however, since many decisions must be based on data that are either uncertain, incomplete, or indirect, clinicians are readily ensnared in processes involving potentially faulty logic associated with tradition. The author addresses the tenacity of tradition and then focuses on three processes--consensus formation, the grounding of certainty in inductive reasoning, and affirming the consequent--that have affected clinical decision making. For some recipients of care, tradition has had a substantial and invalid influence on their ability to access care.

From policy to patients and back: surgical treatment decision making for patients with breast cancer.

PubMed

Katz, Steven J; Hawley, Sarah T

2007-01-01

Persistent use of mastectomy for breast cancer has motivated concerns about overtreatment by surgeons and lack of patient involvement in decisions. However, recent studies suggest that patients perceive substantial involvement and that some patients prefer more invasive surgery, while other research suggests that surgical treatment choices might be poorly informed. Decision-making quality can be improved by increasing patients' knowledge about treatments' risks and benefits and by optimizing their involvement. The mastectomy story underscores the limitations of utilization measures as quality indicators. Strategies to improve patient outcomes should focus on tools to improve the quality of decision making and innovations in multispecialty practice.

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

PubMed

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

2018-03-22

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

Decision making based on data analysis and optimization algorithm applied for cogeneration systems integration into a grid

NASA Astrophysics Data System (ADS)

Asmar, Joseph Al; Lahoud, Chawki; Brouche, Marwan

2018-05-01

Cogeneration and trigeneration systems can contribute to the reduction of primary energy consumption and greenhouse gas emissions in residential and tertiary sectors, by reducing fossil fuels demand and grid losses with respect to conventional systems. The cogeneration systems are characterized by a very high energy efficiency (80 to 90%) as well as a less polluting aspect compared to the conventional energy production. The integration of these systems into the energy network must simultaneously take into account their economic and environmental challenges. In this paper, a decision-making strategy will be introduced and is divided into two parts. The first one is a strategy based on a multi-objective optimization tool with data analysis and the second part is based on an optimization algorithm. The power dispatching of the Lebanese electricity grid is then simulated and considered as a case study in order to prove the compatibility of the cogeneration power calculated by our decision-making technique. In addition, the thermal energy produced by the cogeneration systems which capacity is selected by our technique shows compatibility with the thermal demand for district heating.

The left inferior frontal gyrus is involved in adjusting response bias during a perceptual decision-making task

PubMed Central

Reckless, Greg E; Ousdal, Olga T; Server, Andres; Walter, Henrik; Andreassen, Ole A; Jensen, Jimmy

2014-01-01

Introduction Changing the way we make decisions from one environment to another allows us to maintain optimal decision-making. One way decision-making may change is how biased one is toward one option or another. Identifying the regions of the brain that underlie the change in bias will allow for a better understanding of flexible decision-making. Methods An event-related, perceptual decision-making task where participants had to detect a picture of an animal amongst distractors was used during functional magnetic resonance imaging. Positive and negative financial motivation were used to affect a change in response bias, and changes in decision-making behavior were quantified using signal detection theory. Results Response bias became relatively more liberal during both positive and negative motivated trials compared to neutral trials. For both motivational conditions, the larger the liberal shift in bias, the greater the left inferior frontal gyrus (IFG) activity. There was no relationship between individuals' belief that they used a different strategy and their actual change in response bias. Conclusions The present findings suggest that the left IFG plays a role in adjusting response bias across different decision environments. This suggests a potential role for the left IFG in flexible decision-making. PMID:24944869

Fuzzy methods in decision making process - A particular approach in manufacturing systems

NASA Astrophysics Data System (ADS)

Coroiu, A. M.

2015-11-01

We are living in a competitive environment, so we can see and understand that the most of manufacturing firms do the best in order to accomplish meeting demand, increasing quality, decreasing costs, and delivery rate. In present a stake point of interest is represented by the development of fuzzy technology. A particular approach for this is represented through the development of methodologies to enhance the ability to managed complicated optimization and decision making aspects involving non-probabilistic uncertainty with the reason to understand, development, and practice the fuzzy technologies to be used in fields such as economic, engineering, management, and societal problems. Fuzzy analysis represents a method for solving problems which are related to uncertainty and vagueness; it is used in multiple areas, such as engineering and has applications in decision making problems, planning and production. As a definition for decision making process we can use the next one: result of mental processes based upon cognitive process with a main role in the selection of a course of action among several alternatives. Every process of decision making can be represented as a result of a final choice and the output can be represented as an action or as an opinion of choice. Different types of uncertainty can be discovered in a wide variety of optimization and decision making problems related to planning and operation of power systems and subsystems. The mixture of the uncertainty factor in the construction of different models serves for increasing their adequacy and, as a result, the reliability and factual efficiency of decisions based on their analysis. Another definition of decision making process which came to illustrate and sustain the necessity of using fuzzy method: the decision making is an approach of choosing a strategy among many different projects in order to achieve some purposes and is formulated as three different models: high risk decision, usual risk decision and low risk decision - some specific formulas of fuzzy logic. The fuzzy set concepts has some certain parameterization features which are certain extensions of crisp and fuzzy relations respectively and have a rich potential for application to the decision making problems. The proposed approach from this paper presents advantages of fuzzy approach, in comparison with other paradigm and presents a particular way in which fuzzy logic can emerge in decision making process and planning process with implication, as a simulation, in manufacturing - involved in measuring performance of advanced manufacturing systems. Finally, an example is presented to illustrate our simulation.

Requiem for the max rule?

PubMed

Ma, Wei Ji; Shen, Shan; Dziugaite, Gintare; van den Berg, Ronald

2015-11-01

In tasks such as visual search and change detection, a key question is how observers integrate noisy measurements from multiple locations to make a decision. Decision rules proposed to model this process have fallen into two categories: Bayes-optimal (ideal observer) rules and ad-hoc rules. Among the latter, the maximum-of-outputs (max) rule has been the most prominent. Reviewing recent work and performing new model comparisons across a range of paradigms, we find that in all cases except for one, the optimal rule describes human data as well as or better than every max rule either previously proposed or newly introduced here. This casts doubt on the utility of the max rule for understanding perceptual decision-making. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of risk analysis in water resourses management

NASA Astrophysics Data System (ADS)

Varouchakis, Emmanouil; Palogos, Ioannis

2017-04-01

A common cost-benefit analysis approach, which is novel in the risk analysis of hydrologic/hydraulic applications, and a Bayesian decision analysis are applied to aid the decision making on whether or not to construct a water reservoir for irrigation purposes. The alternative option examined is a scaled parabolic fine variation in terms of over-pumping violations in contrast to common practices that usually consider short-term fines. Such an application, and in such detail, represents new feedback. The results indicate that the probability uncertainty is the driving issue that determines the optimal decision with each methodology, and depending on the unknown probability handling, each methodology may lead to a different optimal decision. Thus, the proposed tool can help decision makers (stakeholders) to examine and compare different scenarios using two different approaches before making a decision considering the cost of a hydrologic/hydraulic project and the varied economic charges that water table limit violations can cause inside an audit interval. In contrast to practices that assess the effect of each proposed action separately considering only current knowledge of the examined issue, this tool aids decision making by considering prior information and the sampling distribution of future successful audits. This tool is developed in a web service for the easier stakeholders' access.

Culture and medical decision making: Healthcare consumer perspectives in Japan and the United States.

PubMed

Alden, Dana L; Friend, John M; Lee, Angela Y; de Vries, Marieke; Osawa, Ryosuke; Chen, Qimei

2015-12-01

Two studies identified core value influences on medical decision-making processes across and within cultures. In Study 1, Japanese and American adults reported desired levels of medical decision-making influence across conditions that varied in seriousness. Cultural antecedents (interdependence, independence, and power distance) were also measured. In Study 2, American adults reviewed a colorectal cancer screening decision aid. Decision preparedness was measured along with interdependence, independence, and desire for medical information. In Study 1, higher interdependence predicted stronger desire for decision-making information in both countries, but was significantly stronger in Japan. The path from information desire to decision-making influence desire was significant only in Japan. The independence path to desire for decision-making influence was significant only in the United States. Power distance effects negatively predicted desire for decision-making influence only in the United States. For Study 2, high (low) interdependents and women (men) in the United States felt that a colorectal cancer screening decision aid helped prepare them more (less) for a medical consultation. Low interdependent men were at significantly higher risk for low decision preparedness. Study 1 suggests that Japanese participants may tend to view medical decision-making influence as an interdependent, information sharing exchange, whereas American respondents may be more interested in power sharing that emphasizes greater independence. Study 2 demonstrates the need to assess value influences on medical decision-making processes within and across cultures and suggests that individually tailored versions of decision aids may optimize decision preparedness. (c) 2015 APA, all rights reserved).

Evidence-based medicine, the research-practice gap, and biases in medical and surgical decision making in dermatology.

PubMed

Eaglstein, William H

2010-10-01

The objectives of this article are to promote a better understanding of a group of biases that influence therapeutic decision making by physicians/dermatologists and to raise the awareness that these biases contribute to a research-practice gap that has an impact on physicians and treatment solutions. The literature included a wide range of peer-reviewed articles dealing with biases in decision making, evidence-based medicine, randomized controlled clinical trials, and the research-practice gap. Bias against new therapies, bias in favor of indirect harm or omission, and bias against change when multiple new choices are offered may unconsciously affect therapeutic decision making. Although there is no comprehensive understanding or theory as to how choices are made by physicians, recognition of certain cognition patterns and their associated biases will help narrow the research-practice gap and optimize decision making regarding therapeutic choices.

Dopamine and Stress System Modulation of Sex Differences in Decision Making.

PubMed

Georgiou, Polymnia; Zanos, Panos; Bhat, Shambhu; Tracy, J Kathleen; Merchenthaler, Istvan J; McCarthy, Margaret M; Gould, Todd D

2018-01-01

Maladaptive decision making is associated with several neuropsychiatric disorders, including problem gambling and suicidal behavior. The prevalence of these disorders is higher in men vs women, suggesting gender-dependent regulation of their pathophysiology underpinnings. We assessed sex differences in decision making using the rat version of the Iowa gambling task. Female rats identified the most optimal choice from session 1, whereas male rats from session 5. Male, but not female rats, progressively improved their advantageous option responding and surpassed females. Estrus cycle phase did not affect decision making. To test whether pharmacological manipulations targeting the dopaminergic and stress systems affect decision making in a sex-dependent manner, male and female rats received injections of a dopamine D 2 receptor (D 2 R) antagonist (eticlopride), D 2 R agonist (quinpirole), corticotropin-releasing factor 1 (CRF 1 ) antagonist (antalarmin), and α 2 -adrenergic receptor antagonist (yohimbine; used as a pharmacological stressor). Alterations in mRNA levels of D 2 R and CRF 1 were also assessed. Eticlopride decreased advantageous responding in male, but not female rats, whereas quinpirole decreased advantageous responding specifically in females. Yohimbine dose-dependently decreased advantageous responding in female rats, whereas decreased advantageous responding was only observed at higher doses in males. Antalarmin increased optimal choice responding only in female rats. Higher Drd2 and Crhr1 expression in the amygdala were observed in female vs male rats. Higher amygdalar Crhr1 expression was negatively correlated with advantageous responding specifically in females. This study demonstrates the relevance of dopaminergic- and stress-dependent sex differences to maladaptive decision making.

Physical mechanism of mind changes and tradeoffs among speed, accuracy, and energy cost in brain decision making: Landscape, flux, and path perspectives

NASA Astrophysics Data System (ADS)

Han, Yan; Kun, Zhang; Jin, Wang

2016-07-01

Cognitive behaviors are determined by underlying neural networks. Many brain functions, such as learning and memory, have been successfully described by attractor dynamics. For decision making in the brain, a quantitative description of global attractor landscapes has not yet been completely given. Here, we developed a theoretical framework to quantify the landscape associated with the steady state probability distributions and associated steady state curl flux, measuring the degree of non-equilibrium through the degree of detailed balance breaking for decision making. We quantified the decision-making processes with optimal paths from the undecided attractor states to the decided attractor states, which are identified as basins of attractions, on the landscape. Both landscape and flux determine the kinetic paths and speed. The kinetics and global stability of decision making are explored by quantifying the landscape topography through the barrier heights and the mean first passage time. Our theoretical predictions are in agreement with experimental observations: more errors occur under time pressure. We quantitatively explored two mechanisms of the speed-accuracy tradeoff with speed emphasis and further uncovered the tradeoffs among speed, accuracy, and energy cost. Our results imply that there is an optimal balance among speed, accuracy, and the energy cost in decision making. We uncovered the possible mechanisms of changes of mind and how mind changes improve performance in decision processes. Our landscape approach can help facilitate an understanding of the underlying physical mechanisms of cognitive processes and identify the key factors in the corresponding neural networks. Project supported by the National Natural Science Foundation of China (Grant Nos. 21190040, 91430217, and 11305176).

Heuristics in Managing Complex Clinical Decision Tasks in Experts’ Decision Making

PubMed Central

Islam, Roosan; Weir, Charlene; Del Fiol, Guilherme

2016-01-01

Background Clinical decision support is a tool to help experts make optimal and efficient decisions. However, little is known about the high level of abstractions in the thinking process for the experts. Objective The objective of the study is to understand how clinicians manage complexity while dealing with complex clinical decision tasks. Method After approval from the Institutional Review Board (IRB), three clinical experts were interviewed the transcripts from these interviews were analyzed. Results We found five broad categories of strategies by experts for managing complex clinical decision tasks: decision conflict, mental projection, decision trade-offs, managing uncertainty and generating rule of thumb. Conclusion Complexity is created by decision conflicts, mental projection, limited options and treatment uncertainty. Experts cope with complexity in a variety of ways, including using efficient and fast decision strategies to simplify complex decision tasks, mentally simulating outcomes and focusing on only the most relevant information. Application Understanding complex decision making processes can help design allocation based on the complexity of task for clinical decision support design. PMID:27275019

Heuristics in Managing Complex Clinical Decision Tasks in Experts' Decision Making.

PubMed

Islam, Roosan; Weir, Charlene; Del Fiol, Guilherme

2014-09-01

Clinical decision support is a tool to help experts make optimal and efficient decisions. However, little is known about the high level of abstractions in the thinking process for the experts. The objective of the study is to understand how clinicians manage complexity while dealing with complex clinical decision tasks. After approval from the Institutional Review Board (IRB), three clinical experts were interviewed the transcripts from these interviews were analyzed. We found five broad categories of strategies by experts for managing complex clinical decision tasks: decision conflict, mental projection, decision trade-offs, managing uncertainty and generating rule of thumb. Complexity is created by decision conflicts, mental projection, limited options and treatment uncertainty. Experts cope with complexity in a variety of ways, including using efficient and fast decision strategies to simplify complex decision tasks, mentally simulating outcomes and focusing on only the most relevant information. Understanding complex decision making processes can help design allocation based on the complexity of task for clinical decision support design.

What failure in collective decision-making tells us about metacognition

PubMed Central

Bahrami, Bahador; Olsen, Karsten; Bang, Dan; Roepstorff, Andreas; Rees, Geraint; Frith, Chris

2012-01-01

Condorcet (1785) proposed that a majority vote drawn from individual, independent and fallible (but not totally uninformed) opinions provides near-perfect accuracy if the number of voters is adequately large. Research in social psychology has since then repeatedly demonstrated that collectives can and do fail more often than expected by Condorcet. Since human collective decisions often follow from exchange of opinions, these failures provide an exquisite opportunity to understand human communication of metacognitive confidence. This question can be addressed by recasting collective decision-making as an information-integration problem similar to multisensory (cross-modal) perception. Previous research in systems neuroscience shows that one brain can integrate information from multiple senses nearly optimally. Inverting the question, we ask: under what conditions can two brains integrate information about one sensory modality optimally? We review recent work that has taken this approach and report discoveries about the quantitative limits of collective perceptual decision-making, and the role of the mode of communication and feedback in collective decision-making. We propose that shared metacognitive confidence conveys the strength of an individual's opinion and its reliability inseparably. We further suggest that a functional role of shared metacognition is to provide substitute signals in situations where outcome is necessary for learning but unavailable or impossible to establish. PMID:22492752

Managing and learning with multiple models: Objectives and optimization algorithms

USGS Publications Warehouse

Probert, William J. M.; Hauser, C.E.; McDonald-Madden, E.; Runge, M.C.; Baxter, P.W.J.; Possingham, H.P.

2011-01-01

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. ?? 2010 Elsevier Ltd.

NASA program decisions using reliability analysis.

NASA Technical Reports Server (NTRS)

Steinberg, A.

1972-01-01

NASA made use of the analytical outputs of reliability people to make management decisions on the Apollo program. Such decisions affected the amount of the incentive fees, how much acceptance testing was necessary, how to optimize development testing, whether to approve engineering changes, and certification of flight readiness. Examples of such analysis are discussed and related to programmatic decisions.-

"Making Do" Decisions: How Home Healthcare Personnel Manage Their Exposure to Home Hazards.

PubMed

Wills, Celia E; Polivka, Barbara J; Darragh, Amy; Lavender, Steven; Sommerich, Carolyn; Stredney, Donald

2016-04-01

This study describes the decision-making processes home healthcare personnel (HHP) use to manage their personal health and safety when managing hazards in client homes. A professionally diverse national sample of 68 HHP participated in individual semi-structured interviews and focus group discussions, and described their decision making and strategies for hazard management in their work environments. HHP described 353 hazard management dilemmas within 394 specifically identified hazards, which were clustered within three broader categories: electrical/fire, slip/trip/lift, and environmental exposures. HHP described multiple types of "making do" decisions for hazard management solutions in which perceived and actual resource limitations constrained response options. A majority of hazard management decisions in the broader hazards categories (72.5%, 68.5%, and 63.5%, respectively) were classifiable as less than optimal. These findings stress the need for more support of HHPs, including comprehensive training, to improve HHP decision making and hazard management strategies, especially in context of resource constraints. © The Author(s) 2015.

“Making Do” Decisions: How Home Healthcare Personnel Manage Their Exposure to Home Hazards

PubMed Central

Wills, Celia E.; Polivka, Barbara J.; Darragh, Amy; Lavender, Steven; Sommerich, Carolyn; Stredney, Donald

2016-01-01

This study describes the decision-making processes home healthcare personnel (HHP) use to manage their personal health and safety when managing hazards in client homes. A professionally diverse national sample of 68 HHP participated in individual semi-structured interviews and focus group discussions, and described their decision making and strategies for hazard management in their work environments. HHP described 353 hazard management dilemmas within 394 specifically identified hazards, which were clustered within three broader categories: electrical/fire, slip/trip/lift, and environmental exposures. HHP described multiple types of “making do” decisions for hazard management solutions in which perceived and actual resource limitations constrained response options. A majority of hazard management decisions in the broader hazards categories (72.5%, 68.5%, and 63.5%, respectively) were classifiable as less than optimal. These findings stress the need for more support of HHPs, including comprehensive training, to improve HHP decision making and hazard management strategies, especially in context of resource constraints. PMID:26669605

The impact of simulation sequencing on perceived clinical decision making.

PubMed

Woda, Aimee; Hansen, Jamie; Paquette, Mary; Topp, Robert

2017-09-01

An emerging nursing education trend is to utilize simulated learning experiences as a means to optimize competency and decision making skills. The purpose of this study was to examine differences in students' perception of clinical decision making and clinical decision making-related self-confidence and anxiety based on the sequence (order) in which they participated in a block of simulated versus hospital-based learning experiences. A quasi-experimental crossover design was used. Between and within group differences were found relative to self-confidence with the decision making process. When comparing groups, at baseline the simulation followed by hospital group had significantly higher self-confidence scores, however, at 14-weeks both groups were not significantly different. Significant within group differences were found in the simulation followed by hospital group only, demonstrating a significant decrease in clinical decision making related anxiety across the semester. Finally, there were no significant difference in; perceived clinical decision making within or between the groups at the two measurement points. Preliminary findings suggest that simulated learning experiences can be offered with alternating sequences without impacting the process, anxiety or confidence with clinical decision making. This study provides beginning evidence to guide curriculum development and allow flexibility based on student needs and available resources. Copyright © 2017. Published by Elsevier Ltd.

Collective learning and optimal consensus decisions in social animal groups.

PubMed

Kao, Albert B; Miller, Noam; Torney, Colin; Hartnett, Andrew; Couzin, Iain D

2014-08-01

Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.

Collective Learning and Optimal Consensus Decisions in Social Animal Groups

PubMed Central

Kao, Albert B.; Miller, Noam; Torney, Colin; Hartnett, Andrew; Couzin, Iain D.

2014-01-01

Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context. PMID:25101642

Dynamic Excitatory and Inhibitory Gain Modulation Can Produce Flexible, Robust and Optimal Decision-making

PubMed Central

Niyogi, Ritwik K.; Wong-Lin, KongFatt

2013-01-01

Behavioural and neurophysiological studies in primates have increasingly shown the involvement of urgency signals during the temporal integration of sensory evidence in perceptual decision-making. Neuronal correlates of such signals have been found in the parietal cortex, and in separate studies, demonstrated attention-induced gain modulation of both excitatory and inhibitory neurons. Although previous computational models of decision-making have incorporated gain modulation, their abstract forms do not permit an understanding of the contribution of inhibitory gain modulation. Thus, the effects of co-modulating both excitatory and inhibitory neuronal gains on decision-making dynamics and behavioural performance remain unclear. In this work, we incorporate time-dependent co-modulation of the gains of both excitatory and inhibitory neurons into our previous biologically based decision circuit model. We base our computational study in the context of two classic motion-discrimination tasks performed in animals. Our model shows that by simultaneously increasing the gains of both excitatory and inhibitory neurons, a variety of the observed dynamic neuronal firing activities can be replicated. In particular, the model can exhibit winner-take-all decision-making behaviour with higher firing rates and within a significantly more robust model parameter range. It also exhibits short-tailed reaction time distributions even when operating near a dynamical bifurcation point. The model further shows that neuronal gain modulation can compensate for weaker recurrent excitation in a decision neural circuit, and support decision formation and storage. Higher neuronal gain is also suggested in the more cognitively demanding reaction time than in the fixed delay version of the task. Using the exact temporal delays from the animal experiments, fast recruitment of gain co-modulation is shown to maximize reward rate, with a timescale that is surprisingly near the experimentally fitted value. Our work provides insights into the simultaneous and rapid modulation of excitatory and inhibitory neuronal gains, which enables flexible, robust, and optimal decision-making. PMID:23825935

Decision Making and Ratio Processing in Patients with Mild Cognitive Impairment.

PubMed

Pertl, Marie-Theres; Benke, Thomas; Zamarian, Laura; Delazer, Margarete

2015-01-01

Making advantageous decisions is important in everyday life. This study aimed at assessing how patients with mild cognitive impairment (MCI) make decisions under risk. Additionally, it investigated the relationship between decision making, ratio processing, basic numerical abilities, and executive functions. Patients with MCI (n = 22) were compared with healthy controls (n = 29) on a complex task of decision making under risk (Game of Dice Task-Double, GDT-D), on two tasks evaluating basic decision making under risk, on a task of ratio processing, and on several neuropsychological background tests. Patients performed significantly lower than controls on the GDT-D and on ratio processing, whereas groups performed comparably on basic decision tasks. Specifically, in the GDT-D, patients obtained lower net scores and lower mean expected values, which indicate a less advantageous performance relative to that of controls. Performance on the GDT-D correlated significantly with performance in basic decision tasks, ratio processing, and executive-function measures when the analysis was performed on the whole sample. Patients with MCI make sub-optimal decisions in complex risk situations, whereas they perform at the same level as healthy adults in simple decision situations. Ratio processing and executive functions have an impact on the decision-making performance of both patients and healthy older adults. In order to facilitate advantageous decisions in complex everyday situations, information should be presented in an easily comprehensible form and cognitive training programs for patients with MCI should focus--among other abilities--on executive functions and ratio processing.

Do Not Fear Your Opponent: Suboptimal Changes of a Prevention Strategy when Facing Stronger Opponents

ERIC Educational Resources Information Center

Slezak, Diego Fernandez; Sigman, Mariano

2012-01-01

The time spent making a decision and its quality define a widely studied trade-off. Some models suggest that the time spent is set to optimize reward, as verified empirically in simple-decision making experiments. However, in a more complex perspective compromising components of regulation focus, ambitions, fear, risk and social variables,…

Complex Decision-Making in Heart Failure: A Systematic Review and Thematic Analysis.

PubMed

Hamel, Aimee V; Gaugler, Joseph E; Porta, Carolyn M; Hadidi, Niloufar Niakosari

Heart failure follows a highly variable and difficult course. Patients face complex decisions, including treatment with implantable cardiac defibrillators, mechanical circulatory support, and heart transplantation. The course of decision-making across multiple treatments is unclear yet integral to providing informed and shared decision-making. Recognizing commonalities across treatment decisions could help nurses and physicians to identify opportunities to introduce discussions and support shared decision-making. The specific aims of this review are to examine complex treatment decision-making, specifically implantable cardiac defibrillators, ventricular assist device, and cardiac transplantation, and to recognize commonalities and key points in the decisional process. MEDLINE, CINAHL, PsycINFO, and Web of Science were searched for English-language studies that included qualitative findings reflecting the complexity of heart failure decision-making. Using a 3-step process, findings were synthesized into themes and subthemes. Twelve articles met criteria for inclusion. Participants included patients, caregivers, and clinicians and included decisions to undergo and decline treatment. Emergent themes were "processing the decision," "timing and prognostication," and "considering the future." Subthemes described how participants received and understood information about the therapy, making and changing a treatment decision, timing their decision and gauging health status outcomes in the context of their decision, the influence of a life or death decision, and the future as a factor in their decisional process. Commonalities were present across therapies, which involved the timing of discussions, the delivery of information, and considerations of the future. Exploring this further could help support patient-centered care and optimize shared decision-making interventions.

A Decision-making Model for a Two-stage Production-delivery System in SCM Environment

NASA Astrophysics Data System (ADS)

Feng, Ding-Zhong; Yamashiro, Mitsuo

A decision-making model is developed for an optimal production policy in a two-stage production-delivery system that incorporates a fixed quantity supply of finished goods to a buyer at a fixed interval of time. First, a general cost model is formulated considering both supplier (of raw materials) and buyer (of finished products) sides. Then an optimal solution to the problem is derived on basis of the cost model. Using the proposed model and its optimal solution, one can determine optimal production lot size for each stage, optimal number of transportation for semi-finished goods, and optimal quantity of semi-finished goods transported each time to meet the lumpy demand of consumers. Also, we examine the sensitivity of raw materials ordering and production lot size to changes in ordering cost, transportation cost and manufacturing setup cost. A pragmatic computation approach for operational situations is proposed to solve integer approximation solution. Finally, we give some numerical examples.

The influence of dispositional optimism on decision regret to undergo major breast reconstructive surgery.

PubMed

Zhong, Toni; Bagher, Shaghayegh; Jindal, Kunaal; Zeng, Delong; O'Neill, Anne C; MacAdam, Sheina; Butler, Kate; Hofer, Stefan O P; Pusic, Andrea; Metcalfe, Kelly A

2013-12-01

It is not known if optimism influences regret following major reconstructive breast surgery. We examined the relationship between dispositional optimism, major complications and decision regret in patients undergoing microsurgical breast reconstruction. A consecutive series of 290 patients were surveyed. Independent variables were: (1) dispositional optimism and (2) major complications. The primary outcome was Decision Regret. A multivariate regression analysis determined the relationship between the independent variables, confounders and decision regret. Of the 181 respondents, 63% reported no regret after breast reconstruction, 26% had mild regret, and 11% moderate to severe regret. Major complications did not have a significant effect on decision regret, and the impact of dispositional optimism was not significant in Caucasian women. There was a significant effect in non-Caucasian women with less optimism who had significantly higher levels of mild regret 1.36 (CI 1.02-1.97) and moderate to severe regret 1.64 (CI 1.0-93.87). This is the first paper to identify a subgroup of non-Caucasian patients with low dispositional optimism who may be at risk for developing regret after microsurgical breast reconstruction. Possible strategies to ameliorate regret may involve addressing cultural and language barriers, setting realistic expectations, and providing more support during the pre-operative decision-making phase. © 2013 Wiley Periodicals, Inc.

Chronic and Acute Stress Promote Overexploitation in Serial Decision Making

PubMed Central

Lenow, Jennifer K.; Constantino, Sara M.

2017-01-01

Many decisions that humans make resemble foraging problems in which a currently available, known option must be weighed against an unknown alternative option. In such foraging decisions, the quality of the overall environment can be used as a proxy for estimating the value of future unknown options against which current prospects are compared. We hypothesized that such foraging-like decisions would be characteristically sensitive to stress, a physiological response that tracks biologically relevant changes in environmental context. Specifically, we hypothesized that stress would lead to more exploitative foraging behavior. To test this, we investigated how acute and chronic stress, as measured by changes in cortisol in response to an acute stress manipulation and subjective scores on a questionnaire assessing recent chronic stress, relate to performance in a virtual sequential foraging task. We found that both types of stress bias human decision makers toward overexploiting current options relative to an optimal policy. These findings suggest a possible computational role of stress in decision making in which stress biases judgments of environmental quality. SIGNIFICANCE STATEMENT Many of the most biologically relevant decisions that we make are foraging-like decisions about whether to stay with a current option or search the environment for a potentially better one. In the current study, we found that both acute physiological and chronic subjective stress are associated with greater overexploitation or staying at current options for longer than is optimal. These results suggest a domain-general way in which stress might bias foraging decisions through changing one's appraisal of the overall quality of the environment. These novel findings not only have implications for understanding how this important class of foraging decisions might be biologically implemented, but also for understanding the computational role of stress in behavior and cognition more broadly. PMID:28483979

Distinct roles of dopamine and subthalamic nucleus in learning and probabilistic decision making.

PubMed

Coulthard, Elizabeth J; Bogacz, Rafal; Javed, Shazia; Mooney, Lucy K; Murphy, Gillian; Keeley, Sophie; Whone, Alan L

2012-12-01

Even simple behaviour requires us to make decisions based on combining multiple pieces of learned and new information. Making such decisions requires both learning the optimal response to each given stimulus as well as combining probabilistic information from multiple stimuli before selecting a response. Computational theories of decision making predict that learning individual stimulus-response associations and rapid combination of information from multiple stimuli are dependent on different components of basal ganglia circuitry. In particular, learning and retention of memory, required for optimal response choice, are significantly reliant on dopamine, whereas integrating information probabilistically is critically dependent upon functioning of the glutamatergic subthalamic nucleus (computing the 'normalization term' in Bayes' theorem). Here, we test these theories by investigating 22 patients with Parkinson's disease either treated with deep brain stimulation to the subthalamic nucleus and dopaminergic therapy or managed with dopaminergic therapy alone. We use computerized tasks that probe three cognitive functions-information acquisition (learning), memory over a delay and information integration when multiple pieces of sequentially presented information have to be combined. Patients performed the tasks ON or OFF deep brain stimulation and/or ON or OFF dopaminergic therapy. Consistent with the computational theories, we show that stopping dopaminergic therapy impairs memory for probabilistic information over a delay, whereas deep brain stimulation to the region of the subthalamic nucleus disrupts decision making when multiple pieces of acquired information must be combined. Furthermore, we found that when participants needed to update their decision on the basis of the last piece of information presented in the decision-making task, patients with deep brain stimulation of the subthalamic nucleus region did not slow down appropriately to revise their plan, a pattern of behaviour that mirrors the impulsivity described clinically in some patients with subthalamic nucleus deep brain stimulation. Thus, we demonstrate distinct mechanisms for two important facets of human decision making: first, a role for dopamine in memory consolidation, and second, the critical importance of the subthalamic nucleus in successful decision making when multiple pieces of information must be combined.

Some Results of Weak Anticipative Concept Applied in Simulation Based Decision Support in Enterprise

NASA Astrophysics Data System (ADS)

Kljajić, Miroljub; Kofjač, Davorin; Kljajić Borštnar, Mirjana; Škraba, Andrej

2010-11-01

The simulation models are used as for decision support and learning in enterprises and in schools. Tree cases of successful applications demonstrate usefulness of weak anticipative information. Job shop scheduling production with makespan criterion presents a real case customized flexible furniture production optimization. The genetic algorithm for job shop scheduling optimization is presented. Simulation based inventory control for products with stochastic lead time and demand describes inventory optimization for products with stochastic lead time and demand. Dynamic programming and fuzzy control algorithms reduce the total cost without producing stock-outs in most cases. Values of decision making information based on simulation were discussed too. All two cases will be discussed from optimization, modeling and learning point of view.

Competency in health care management: a training model in epidemiologic methods for assessing and improving the quality of clinical practice through evidence-based decision making.

PubMed

Hudak, R P; Jacoby, I; Meyer, G S; Potter, A L; Hooper, T I; Krakauer, H

1997-01-01

This article describes a training model that focuses on health care management by applying epidemiologic methods to assess and improve the quality of clinical practice. The model's uniqueness is its focus on integrating clinical evidence-based decision making with fundamental principles of resource management to achieve attainable, cost-effective, high-quality health outcomes. The target students are current and prospective clinical and administrative executives who must optimize decision making at the clinical and managerial levels of health care organizations.

Regret and adaptive decision making in young children.

PubMed

O'Connor, Eimear; McCormack, Teresa; Beck, Sarah R; Feeney, Aidan

2015-07-01

In line with the claim that regret plays a role in decision making, O'Connor, McCormack, and Feeney (Child Development, 85 (2014) 1995-2010) found that children who reported feeling sadder on discovering they had made a non-optimal choice were more likely to make a different choice the next time around. We examined two issues of interpretation regarding this finding: whether the emotion measured was indeed regret and whether it was the experience of this emotion, rather than the ability to anticipate it, that affected decision making. To address the first issue, we varied the degree to which children aged 6 or 7 years were responsible for an outcome, assuming that responsibility is a necessary condition for regret. The second issue was addressed by examining whether children could accurately anticipate that they would feel worse on discovering they had made a non-optimal choice. Children were more likely to feel sad if they were responsible for the outcome; however, even if they were not responsible, children were more likely than chance to report feeling sadder. Moreover, across all conditions, feeling sadder was associated with making a better subsequent choice. In a separate task, we demonstrated that children of this age cannot accurately anticipate feeling sadder on discovering that they had not made the best choice. These findings suggest that although children may feel regret following a non-optimal choice, even if they were not responsible for an outcome, they may experience another negative emotion such as frustration. Experiencing either of these emotions seems to be sufficient to support better decision making. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Value of information and pricing new healthcare interventions.

PubMed

Willan, Andrew R; Eckermann, Simon

2012-06-01

Previous application of value-of-information methods to optimal clinical trial design have predominantly taken a societal decision-making perspective, implicitly assuming that healthcare costs are covered through public expenditure and trial research is funded by government or donation-based philanthropic agencies. In this paper, we consider the interaction between interrelated perspectives of a societal decision maker (e.g. the National Institute for Health and Clinical Excellence [NICE] in the UK) charged with the responsibility for approving new health interventions for reimbursement and the company that holds the patent for a new intervention. We establish optimal decision making from societal and company perspectives, allowing for trade-offs between the value and cost of research and the price of the new intervention. Given the current level of evidence, there exists a maximum (threshold) price acceptable to the decision maker. Submission for approval with prices above this threshold will be refused. Given the current level of evidence and the decision maker's threshold price, there exists a minimum (threshold) price acceptable to the company. If the decision maker's threshold price exceeds the company's, then current evidence is sufficient since any price between the thresholds is acceptable to both. On the other hand, if the decision maker's threshold price is lower than the company's, then no price is acceptable to both and the company's optimal strategy is to commission additional research. The methods are illustrated using a recent example from the literature.

ANFIS multi criteria decision making for overseas construction projects: a methodology

NASA Astrophysics Data System (ADS)

Utama, W. P.; Chan, A. P. C.; Zulherman; Zahoor, H.; Gao, R.; Jumas, D. Y.

2018-02-01

A critical part when a company targeting a foreign market is how to make a better decision in connection with potential project selection. Since different attributes of information are often incomplete, imprecise and ill-defined in overseas projects selection, the process of decision making by relying on the experiences and intuition is a risky attitude. This paper aims to demonstrate a decision support method in deciding overseas construction projects (OCPs). An Adaptive Neuro-Fuzzy Inference System (ANFIS), the amalgamation of Neural Network and Fuzzy Theory, was used as decision support tool to decide to go or not go on OCPs. Root mean square error (RMSE) and coefficient of correlation (R) were employed to identify the ANFIS system indicating an optimum and efficient result. The optimum result was obtained from ANFIS network with two input membership functions, Gaussian membership function (gaussmf) and hybrid optimization method. The result shows that ANFIS may help the decision-making process for go/not go decision in OCPs.

Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization.

PubMed

Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu

2016-08-15

Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

Impaired decision-making under risk in individuals with alcohol dependence

PubMed Central

Brevers, Damien; Bechara, Antoine; Cleeremans, Axel; Kornreich, Charles; Verbanck, Paul; Noël, Xavier

2014-01-01

Background Alcohol dependence is associated with poor decision-making under ambiguity, that is, when decisions are to be made in the absence of known probabilities of reward and loss. However, little is known regarding decisions made by individuals with alcohol dependence in the context of known probabilities (decision under risk). In this study, we investigated the relative contribution of these distinct aspects of decision making to alcohol dependence. Methods Thirty recently detoxified and sober asymptomatic alcohol-dependent individuals, and thirty healthy control participants were tested for decision-making under ambiguity (using the Iowa Gambling Task), and decision-making under-risk (using the Cups Task and Coin Flipping Task). We also tested their capacities for working memory storage (Digit-span Forward), and dual-tasking (Operation-span Task). Results Compared to healthy control participants, alcohol-dependent individuals made disadvantageous decisions on the Iowa Gambling Task, reflecting poor decisions under ambiguity. They also made more risky choices on the Cups and Coin Flipping Tasks reflecting poor decision-making under risk. In addition, alcohol-dependent participants showed some working memory impairments, as measured by the dual tasking, and the degree of this impairment correlated with high-risk decision-making, thus suggesting a relationship between processes sub-serving working memory and risky decisions. Conclusion These results suggest that alcohol dependent individuals are impaired in their ability to decide optimally in multiple facets of uncertainty (i.e., both risk and ambiguity), and that at least some aspects of these deficits are linked to poor working memory processes. PMID:24948198

Status Update on the NCRP Scientific Committee SC 5-1 Report: Decision Making for Late-Phase Recovery from Nuclear or Radiological Incidents - 13450

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

Chen, S.Y.

2013-07-01

In August 2008, the U.S. Department of Homeland Security (DHS) issued its final Protective Action Guide (PAG) for radiological dispersal device (RDD) and improvised nuclear device (IND) incidents. This document specifies protective actions for public health during the early and intermediate phases and cleanup guidance for the late phase of RDD or IND incidents, and it discusses approaches to implementing the necessary actions. However, while the PAG provides specific guidance for the early and intermediate phases, it prescribes no equivalent guidance for the late-phase cleanup actions. Instead, the PAG offers a general description of a complex process using a site-specificmore » optimization approach. This approach does not predetermine cleanup levels but approaches the problem from the factors that would bear on the final agreed-on cleanup levels. Based on this approach, the decision-making process involves multifaceted considerations including public health, the environment, and the economy, as well as socio-political factors. In an effort to fully define the process and approach to be used in optimizing late-phase recovery and site restoration following an RDD or IND incident, DHS has tasked the NCRP with preparing a comprehensive report addressing all aspects of the optimization process. Preparation of the NCRP report is a three-year (2010-2013) project assigned to a scientific committee, the Scientific Committee (SC) 5-1; the report was initially titled, Approach to Optimizing Decision Making for Late- Phase Recovery from Nuclear or Radiological Terrorism Incidents. Members of SC 5-1 represent a broad range of expertise, including homeland security, health physics, risk and decision analysis, economics, environmental remediation and radioactive waste management, and communication. In the wake of the Fukushima nuclear accident of 2011, and guided by a recent process led by the White House through a Principal Level Exercise (PLE), the optimization approach has since been expanded to include off-site contamination from major nuclear power plant accidents as well as other nuclear or radiological incidents. The expanded application under the current guidance has thus led to a broadened scope of the report, which is reflected in its new title, Decision Making for Late-Phase Recovery from Nuclear or Radiological Incidents. The NCRP report, which is due for publication in 2013, will substantiate the current DHS guidance by clarifying and elaborating on the processes required for the development and implementation of procedures for optimizing decision making for late-phase recovery, enabling the establishment of cleanup goals on a site-specific basis. The report will contain a series of topics addressing important issues related to the long-term recovery from nuclear or radiological incidents. Special topics relevant to supporting the optimization of the decision-making process will include cost-benefit analysis, radioactive waste management, risk communication, stakeholder interaction, risk assessment, and decontamination approaches and techniques. The committee also evaluated past nuclear and radiological incidents for their relevance to the report, including the emerging issues associated with the Fukushima nuclear accident. Thus, due to the commonality of the late-phase issues (such as the potential widespread contamination following an event), the majority of the information pertaining to the response in the late-phase decision-making period, including site-specific optimization framework and approach, could be used or adapted for use in case of similar situations that are not due to terrorism, such as those that would be caused by major nuclear facility accidents or radiological incidents. To ensure that the report and the NCRP recommendations are current and relevant to the effective implementation of federal guidance, SC 5-1 has actively coordinated with the agencies of interest and other relevant stakeholders throughout the duration of the project. The resulting report will be an important resource to guide those involved in late-phase recovery efforts following a nuclear or radiological incident. (authors)« less

Improved multi-objective ant colony optimization algorithm and its application in complex reasoning

NASA Astrophysics Data System (ADS)

Wang, Xinqing; Zhao, Yang; Wang, Dong; Zhu, Huijie; Zhang, Qing

2013-09-01

The problem of fault reasoning has aroused great concern in scientific and engineering fields. However, fault investigation and reasoning of complex system is not a simple reasoning decision-making problem. It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints. So far, little research has been carried out in this field. This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes. Three optimization objectives are considered simultaneously: maximum probability of average fault, maximum average importance, and minimum average complexity of test. Under the constraints of both known symptoms and the causal relationship among different components, a multi-objective optimization mathematical model is set up, taking minimizing cost of fault reasoning as the target function. Since the problem is non-deterministic polynomial-hard(NP-hard), a modified multi-objective ant colony algorithm is proposed, in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives. At last, a Pareto optimal set is acquired. Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set, through which the final fault causes can be identified according to decision-making demands, thus realize fault reasoning of the multi-constraint and multi-objective complex system. Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model, which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.

Framing matters: Effects of framing on older adults’ exploratory decision-making

PubMed Central

Cooper, Jessica A.; Blanco, Nathaniel; Maddox, W. Todd

2016-01-01

We examined framing effects on exploratory decision-making. In Experiment 1 we tested older and younger adults in two decision-making tasks separated by one week, finding that older adults’ decision-making performance was preserved when maximizing gains, but declined when minimizing losses. Computational modeling indicates that younger adults in both conditions, and older adults in gains-maximization, utilized a decreasing threshold strategy (which is optimal), but older adults in losses were better fit by a fixed-probability model of exploration. In Experiment 2 we examined within-subjects behavior in older and younger adults in the same exploratory decision-making task, but without a time separation between tasks. We replicated the older adult disadvantage in loss-minimization from Experiment 1, and found that the older adult deficit was significantly reduced when the loss-minimization task immediately followed the gains-maximization task. We conclude that older adults’ performance in exploratory decision-making is hindered when framed as loss-minimization, but that this deficit is attenuated when older adults can first develop a strategy in a gains-framed task. PMID:27977218

Framing matters: Effects of framing on older adults' exploratory decision-making.

PubMed

Cooper, Jessica A; Blanco, Nathaniel J; Maddox, W Todd

2017-02-01

We examined framing effects on exploratory decision-making. In Experiment 1 we tested older and younger adults in two decision-making tasks separated by one week, finding that older adults' decision-making performance was preserved when maximizing gains, but it declined when minimizing losses. Computational modeling indicates that younger adults in both conditions, and older adults in gains maximization, utilized a decreasing threshold strategy (which is optimal), but older adults in losses were better fit by a fixed-probability model of exploration. In Experiment 2 we examined within-subject behavior in older and younger adults in the same exploratory decision-making task, but without a time separation between tasks. We replicated the older adult disadvantage in loss minimization from Experiment 1 and found that the older adult deficit was significantly reduced when the loss-minimization task immediately followed the gains-maximization task. We conclude that older adults' performance in exploratory decision-making is hindered when framed as loss minimization, but that this deficit is attenuated when older adults can first develop a strategy in a gains-framed task. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Linguistic Multi-Attribute Group Decision Making with Risk Preferences and Its Use in Low-Carbon Tourism Destination Selection.

PubMed

Lin, Hui; Wang, Zhou-Jing

2017-09-17

Low-carbon tourism plays an important role in carbon emission reduction and environmental protection. Low-carbon tourism destination selection often involves multiple conflicting and incommensurate attributes or criteria and can be modelled as a multi-attribute decision-making problem. This paper develops a framework to solve multi-attribute group decision-making problems, where attribute evaluation values are provided as linguistic terms and the attribute weight information is incomplete. In order to obtain a group risk preference captured by a linguistic term set with triangular fuzzy semantic information, a nonlinear programming model is established on the basis of individual risk preferences. We first convert individual linguistic-term-based decision matrices to their respective triangular fuzzy decision matrices, which are then aggregated into a group triangular fuzzy decision matrix. Based on this group decision matrix and the incomplete attribute weight information, a linear program is developed to find an optimal attribute weight vector. A detailed procedure is devised for tackling linguistic multi-attribute group decision making problems. A low-carbon tourism destination selection case study is offered to illustrate how to use the developed group decision-making model in practice.

Linguistic Multi-Attribute Group Decision Making with Risk Preferences and Its Use in Low-Carbon Tourism Destination Selection

PubMed Central

Lin, Hui; Wang, Zhou-Jing

2017-01-01

Low-carbon tourism plays an important role in carbon emission reduction and environmental protection. Low-carbon tourism destination selection often involves multiple conflicting and incommensurate attributes or criteria and can be modelled as a multi-attribute decision-making problem. This paper develops a framework to solve multi-attribute group decision-making problems, where attribute evaluation values are provided as linguistic terms and the attribute weight information is incomplete. In order to obtain a group risk preference captured by a linguistic term set with triangular fuzzy semantic information, a nonlinear programming model is established on the basis of individual risk preferences. We first convert individual linguistic-term-based decision matrices to their respective triangular fuzzy decision matrices, which are then aggregated into a group triangular fuzzy decision matrix. Based on this group decision matrix and the incomplete attribute weight information, a linear program is developed to find an optimal attribute weight vector. A detailed procedure is devised for tackling linguistic multi-attribute group decision making problems. A low-carbon tourism destination selection case study is offered to illustrate how to use the developed group decision-making model in practice. PMID:28926985

Decision-making in product portfolios of pharmaceutical research and development – managing streams of innovation in highly regulated markets

PubMed Central

Jekunen, Antti

2014-01-01

Decision-making is a core function of any drug development firm. Developing drugs demands a firm to be highly innovative, while at the same time the activity is strictly regulated. Successful drug development offers the right to apply for a long-term patent that confers exclusive marketing rights. This article addresses the issue of what constitutes an adequate portfolio of drugs for a drug development firm and how it might be managed successfully. The paper investigates decision-making in the industry and specifically in the development of oncology drugs from various perspectives: the need for decisions, their timing, decision-making at the project level, the optimal portfolio, tools for portfolio analysis, the evaluation of patents, and finally the importance of the drug portfolio. Drug development decisions as important organizational elements should get more emphasis, and decisions in drug portfolio using modern decision-making methods should be used more widely than what currently happens. Structured, informed decisions would help avoiding late terminations of drugs in Phase III development. An improved research and development pipeline and drug portfolio management are the major elements in the general strategy targeting success. PMID:25364229

Decision-making in product portfolios of pharmaceutical research and development--managing streams of innovation in highly regulated markets.

PubMed

Jekunen, Antti

2014-01-01

Decision-making is a core function of any drug development firm. Developing drugs demands a firm to be highly innovative, while at the same time the activity is strictly regulated. Successful drug development offers the right to apply for a long-term patent that confers exclusive marketing rights. This article addresses the issue of what constitutes an adequate portfolio of drugs for a drug development firm and how it might be managed successfully. The paper investigates decision-making in the industry and specifically in the development of oncology drugs from various perspectives: the need for decisions, their timing, decision-making at the project level, the optimal portfolio, tools for portfolio analysis, the evaluation of patents, and finally the importance of the drug portfolio. Drug development decisions as important organizational elements should get more emphasis, and decisions in drug portfolio using modern decision-making methods should be used more widely than what currently happens. Structured, informed decisions would help avoiding late terminations of drugs in Phase III development. An improved research and development pipeline and drug portfolio management are the major elements in the general strategy targeting success.

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.

A Review of the Impacts of Different Approaches for Diabetes Prevention and a Framework for Making Investment Decisions

PubMed Central

2018-01-01

This paper selectively reviews the economic research on individual (i.e., diabetes prevention programs and financial rewards for weight loss) and population-wide based diabetes prevention interventions (such as food taxes, nutritional labeling, and worksite wellness programs) that demonstrate a direct reduction in diabetes incidence or improvements in diabetes risk factors such as weight, glucose or glycated hemoglobin. The paper suggests a framework to guide decision makers on how to use the available evidence to determine the optimal allocation of resources across population-wide and individual-based interventions. This framework should also assist in the discussion of what parameters are needed from research to inform decision-making on what might be the optimal mix of strategies to reduce diabetes prevalence. PMID:29543711

A Review of the Impacts of Different Approaches for Diabetes Prevention and a Framework for Making Investment Decisions.

PubMed

Alva, Maria L

2018-03-15

This paper selectively reviews the economic research on individual (i.e., diabetes prevention programs and financial rewards for weight loss) and population-wide based diabetes prevention interventions (such as food taxes, nutritional labeling, and worksite wellness programs) that demonstrate a direct reduction in diabetes incidence or improvements in diabetes risk factors such as weight, glucose or glycated hemoglobin. The paper suggests a framework to guide decision makers on how to use the available evidence to determine the optimal allocation of resources across population-wide and individual-based interventions. This framework should also assist in the discussion of what parameters are needed from research to inform decision-making on what might be the optimal mix of strategies to reduce diabetes prevalence.

Study on optimized decision-making model of offshore wind power projects investment

NASA Astrophysics Data System (ADS)

Zhao, Tian; Yang, Shangdong; Gao, Guowei; Ma, Li

2018-02-01

China’s offshore wind energy is of great potential and plays an important role in promoting China’s energy structure adjustment. However, the current development of offshore wind power in China is inadequate, and is much less developed than that of onshore wind power. On the basis of considering all kinds of risks faced by offshore wind power development, an optimized model of offshore wind power investment decision is established in this paper by proposing the risk-benefit assessment method. To prove the practicability of this method in improving the selection of wind power projects, python programming is used to simulate the investment analysis of a large number of projects. Therefore, the paper is dedicated to provide decision-making support for the sound development of offshore wind power industry.

Predicting explorative motor learning using decision-making and motor noise.

PubMed

Chen, Xiuli; Mohr, Kieran; Galea, Joseph M

2017-04-01

A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant's level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning.

Predicting explorative motor learning using decision-making and motor noise

PubMed Central

Galea, Joseph M.

2017-01-01

A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant’s level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning. PMID:28437451

Strategy for optimum acquisition of information

DOT National Transportation Integrated Search

2006-10-01

This note is a brief tutorial on a strategy optimizing the acquisition of information. It is a procedure well know to decision theorists but hardly understood or applied by those making decisions about spending dollars, time and other forms of capita...

Expected p-values in light of an ROC curve analysis applied to optimal multiple testing procedures.

PubMed

Vexler, Albert; Yu, Jihnhee; Zhao, Yang; Hutson, Alan D; Gurevich, Gregory

2017-01-01

Many statistical studies report p-values for inferential purposes. In several scenarios, the stochastic aspect of p-values is neglected, which may contribute to drawing wrong conclusions in real data experiments. The stochastic nature of p-values makes their use to examine the performance of given testing procedures or associations between investigated factors to be difficult. We turn our focus on the modern statistical literature to address the expected p-value (EPV) as a measure of the performance of decision-making rules. During the course of our study, we prove that the EPV can be considered in the context of receiver operating characteristic (ROC) curve analysis, a well-established biostatistical methodology. The ROC-based framework provides a new and efficient methodology for investigating and constructing statistical decision-making procedures, including: (1) evaluation and visualization of properties of the testing mechanisms, considering, e.g. partial EPVs; (2) developing optimal tests via the minimization of EPVs; (3) creation of novel methods for optimally combining multiple test statistics. We demonstrate that the proposed EPV-based approach allows us to maximize the integrated power of testing algorithms with respect to various significance levels. In an application, we use the proposed method to construct the optimal test and analyze a myocardial infarction disease dataset. We outline the usefulness of the "EPV/ROC" technique for evaluating different decision-making procedures, their constructions and properties with an eye towards practical applications.

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.

WORK FORCE OPTIMIZATION FOR 2025

DTIC Science & Technology

2016-02-08

AIR WAR COLLEGE AIR UNIVERSITY WORK FORCE OPTIMIZATION FOR 2025 By Edward Buckner, GS-14, Army A Research Report Submitted to the...not copyrighted, but is the property of the United States government. iii Biography GS-14 Edward Buckner attends the Air War College , Air...in improving civilian fitness should reduce medical cost paid by DOD. 3. Decision Making Skills Development Everyone is required to make

Leveraging human decision making through the optimal management of centralized resources

NASA Astrophysics Data System (ADS)

Hyden, Paul; McGrath, Richard G.

2016-05-01

Combining results from mixed integer optimization, stochastic modeling and queuing theory, we will advance the interdisciplinary problem of efficiently and effectively allocating centrally managed resources. Academia currently fails to address this, as the esoteric demands of each of these large research areas limits work across traditional boundaries. The commercial space does not currently address these challenges due to the absence of a profit metric. By constructing algorithms that explicitly use inputs across boundaries, we are able to incorporate the advantages of using human decision makers. Key improvements in the underlying algorithms are made possible by aligning decision maker goals with the feedback loops introduced between the core optimization step and the modeling of the overall stochastic process of supply and demand. A key observation is that human decision-makers must be explicitly included in the analysis for these approaches to be ultimately successful. Transformative access gives warfighters and mission owners greater understanding of global needs and allows for relationships to guide optimal resource allocation decisions. Mastery of demand processes and optimization bottlenecks reveals long term maximum marginal utility gaps in capabilities.

External audit of clinical practice and medical decision making in a new Asian oncology center: Results and implications for both developing and developed nations

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

Shakespeare, Thomas P.; Back, Michael F.; Lu, Jiade J.

2006-03-01

Purpose: The external audit of oncologist clinical practice is increasingly important because of the incorporation of audits into national maintenance of certification (MOC) programs. However, there are few reports of external audits of oncology practice or decision making. Our institution (The Cancer Institute, Singapore) was asked to externally audit an oncology department in a developing Asian nation, providing a unique opportunity to explore the feasibility of such a process. Methods and Materials: We audited 100 randomly selected patients simulated for radiotherapy in 2003, using a previously reported audit instrument assessing clinical documentation/quality assurance and medical decision making. Results: Clinical documentation/qualitymore » assurance, decision making, and overall performance criteria were adequate 74.4%, 88.3%, and 80.2% of the time, respectively. Overall 52.0% of cases received suboptimal management. Multivariate analysis revealed palliative intent was associated with improved documentation/clinical quality assurance (p = 0.07), decision making (p 0.007), overall performance (p = 0.003), and optimal treatment rates (p 0.07); non-small-cell lung cancer or central nervous system primary sites were associated with better decision making (p = 0.001), overall performance (p = 0.03), and optimal treatment rates (p = 0.002). Conclusions: Despite the poor results, the external audit had several benefits. It identified learning needs for future targeting, and the auditor provided facilitating feedback to address systematic errors identified. Our experience was also helpful in refining our national revalidation audit instrument. The feasibility of the external audit supports the consideration of including audit in national MOC programs.« less

Functional disconnection of the orbitofrontal cortex and basolateral amygdala impairs acquisition of a rat gambling task and disrupts animals' ability to alter decision-making behavior after reinforcer devaluation.

PubMed

Zeeb, Fiona D; Winstanley, Catharine A

2013-04-10

An inability to adjust choice preferences in response to changes in reward value may underlie key symptoms of many psychiatric disorders, including chemical and behavioral addictions. We developed the rat gambling task (rGT) to investigate the neurobiology underlying complex decision-making processes. As in the Iowa Gambling task, the optimal strategy is to avoid choosing larger, riskier rewards and to instead favor options associated with smaller rewards but less loss and, ultimately, greater long-term gain. Given the demonstrated importance of the orbitofrontal cortex (OFC) and basolateral amygdala (BLA) in acquisition of the rGT and Iowa Gambling task, we used a contralateral disconnection lesion procedure to assess whether functional connectivity between these regions is necessary for optimal decision-making. Disrupting the OFC-BLA pathway retarded acquisition of the rGT. Devaluing the reinforcer by inducing sensory-specific satiety altered decision-making in control groups. In contrast, disconnected rats did not update their choice preference following reward devaluation, either when the devalued reward was still delivered or when animals needed to rely on stored representations of reward value (i.e., during extinction). However, all rats exhibited decreased premature responding and slower response latencies after satiety manipulations. Hence, disconnecting the OFC and BLA did not affect general behavioral changes caused by reduced motivation, but instead prevented alterations in the value of a specific reward from contributing appropriately to cost-benefit decision-making. These results highlight the role of the OFC-BLA pathway in the decision-making process and suggest that communication between these areas is vital for the appropriate assessment of reward value to influence choice.

Affective and cognitive decision-making in adolescents.

PubMed

van Duijvenvoorde, Anna C K; Jansen, Brenda R J; Visser, Ingmar; Huizenga, Hilde M

2010-01-01

Adolescents demonstrate impaired decision-making in emotionally arousing situations, yet they appear to exhibit relatively mature decision-making skills in predominantly cognitive, low-arousal situations. In this study we compared adolescents' (13-15 years) performance on matched affective and cognitive decision-making tasks, in order to determine (1) their performance level on each task and (2) whether performance on the cognitive task was associated with performance on the affective task. Both tasks required a comparison of choice dimensions characterized by frequency of loss, amount of loss, and constant gain. Results indicated that in the affective task, adolescents performed sub-optimally by considering only the frequency of loss, whereas in the cognitive task adolescents used relatively mature decision rules by considering two or all three choice dimensions. Performance on the affective task was not related to performance on the cognitive task. These results are discussed in light of neural developmental trajectories observed in adolescence.

The child brain computes and utilizes internalized maternal choices

PubMed Central

Lim, Seung-Lark; Cherry, J. Bradley C.; Davis, Ann M.; Balakrishnan, S. N.; Ha, Oh-Ryeong; Bruce, Jared M.; Bruce, Amanda S.

2016-01-01

As children grow, they gradually learn how to make decisions independently. However, decisions like choosing healthy but less-tasty foods can be challenging for children whose self-regulation and executive cognitive functions are still maturing. We propose a computational decision-making process in which children estimate their mother's choices for them as well as their individual food preferences. By employing functional magnetic resonance imaging during real food choices, we find that the ventromedial prefrontal cortex (vmPFC) encodes children's own preferences and the left dorsolateral prefrontal cortex (dlPFC) encodes the projected mom's choices for them at the time of children's choice. Also, the left dlPFC region shows an inhibitory functional connectivity with the vmPFC at the time of children's own choice. Our study suggests that in part, children utilize their perceived caregiver's choices when making choices for themselves, which may serve as an external regulator of decision-making, leading to optimal healthy decisions. PMID:27218420

Insecticide treated bednet strategy in rural settings: can we exploit women's decision making power?

PubMed

Tilak, Rina; Tilak, V W; Bhalwar, R

2007-01-01

Use of insecticide treated bednets in prevention of malaria is a widely propagated global strategy, however, its use has been reported to be influenced and limited by many variables especially gender bias. A cross sectional field epidemiological study was conducted in a rural setting with two outcome variables, 'Bednet use'(primary outcome variable) and 'Women's Decision Making Power' which were studied in reference to various predictor variables. Analysis reveals a significant effect on the primary outcome variable 'Bednet use' of the predictor variables- age, occupation, bednet purchase decision, women's decision making power, husband's education and knowledge about malaria and its prevention. The study recommends IEC on treated bednets to be disseminated through TV targeting the elderly women who have better decision making power and mobilizing younger women who were found to prefer bednets for prevention of mosquito bites for optimizing the use of treated bednets in similar settings.

Optimal and Nonoptimal Computer-Based Test Designs for Making Pass-Fail Decisions

ERIC Educational Resources Information Center

Hambleton, Ronald K.; Xing, Dehui

2006-01-01

Now that many credentialing exams are being routinely administered by computer, new computer-based test designs, along with item response theory models, are being aggressively researched to identify specific designs that can increase the decision consistency and accuracy of pass-fail decisions. The purpose of this study was to investigate the…

Do the right thing: the assumption of optimality in lay decision theory and causal judgment.

PubMed

Johnson, Samuel G B; Rips, Lance J

2015-03-01

Human decision-making is often characterized as irrational and suboptimal. Here we ask whether people nonetheless assume optimal choices from other decision-makers: Are people intuitive classical economists? In seven experiments, we show that an agent's perceived optimality in choice affects attributions of responsibility and causation for the outcomes of their actions. We use this paradigm to examine several issues in lay decision theory, including how responsibility judgments depend on the efficacy of the agent's actual and counterfactual choices (Experiments 1-3), individual differences in responsibility assignment strategies (Experiment 4), and how people conceptualize decisions involving trade-offs among multiple goals (Experiments 5-6). We also find similar results using everyday decision problems (Experiment 7). Taken together, these experiments show that attributions of responsibility depend not only on what decision-makers do, but also on the quality of the options they choose not to take. Copyright © 2015 Elsevier Inc. All rights reserved.

Frontopolar cortex and decision-making efficiency: comparing brain activity of experts with different professional background during an exploration-exploitation task.

PubMed

Laureiro-Martínez, Daniella; Canessa, Nicola; Brusoni, Stefano; Zollo, Maurizio; Hare, Todd; Alemanno, Federica; Cappa, Stefano F

2013-01-01

An optimal balance between efficient exploitation of available resources and creative exploration of alternatives is critical for adaptation and survival. Previous studies associated these behavioral drives with, respectively, the dopaminergic mesocorticolimbic system and frontopolar-intraparietal networks. We study the activation of these systems in two age and gender-matched groups of experienced decision-makers differing in prior professional background, with the aim to understand the neural bases of individual differences in decision-making efficiency (performance divided by response time). We compare brain activity of entrepreneurs (who currently manage the organization they founded based on their venture idea) and managers (who are constantly involved in making strategic decisions but have no venture experience) engaged in a gambling-task assessing exploitative vs. explorative decision-making. Compared with managers, entrepreneurs showed higher decision-making efficiency, and a stronger activation in regions of frontopolar cortex (FPC) previously associated with explorative choice. Moreover, activity across a network of regions previously linked to explore/exploit tradeoffs explained individual differences in choice efficiency. These results suggest new avenues for the study of individual differences in the neural antecedents of efficient decision-making.

Making objective decisions in mechanical engineering problems

NASA Astrophysics Data System (ADS)

Raicu, A.; Oanta, E.; Sabau, A.

2017-08-01

Decision making process has a great influence in the development of a given project, the goal being to select an optimal choice in a given context. Because of its great importance, the decision making was studied using various science methods, finally being conceived the game theory that is considered the background for the science of logical decision making in various fields. The paper presents some basic ideas regarding the game theory in order to offer the necessary information to understand the multiple-criteria decision making (MCDM) problems in engineering. The solution is to transform the multiple-criteria problem in a one-criterion decision problem, using the notion of utility, together with the weighting sum model or the weighting product model. The weighted importance of the criteria is computed using the so-called Step method applied to a relation of preferences between the criteria. Two relevant examples from engineering are also presented. The future directions of research consist of the use of other types of criteria, the development of computer based instruments for decision making general problems and to conceive a software module based on expert system principles to be included in the Wiki software applications for polymeric materials that are already operational.

Frontopolar cortex and decision-making efficiency: comparing brain activity of experts with different professional background during an exploration-exploitation task

PubMed Central

Laureiro-Martínez, Daniella; Canessa, Nicola; Brusoni, Stefano; Zollo, Maurizio; Hare, Todd; Alemanno, Federica; Cappa, Stefano F.

2014-01-01

An optimal balance between efficient exploitation of available resources and creative exploration of alternatives is critical for adaptation and survival. Previous studies associated these behavioral drives with, respectively, the dopaminergic mesocorticolimbic system and frontopolar-intraparietal networks. We study the activation of these systems in two age and gender-matched groups of experienced decision-makers differing in prior professional background, with the aim to understand the neural bases of individual differences in decision-making efficiency (performance divided by response time). We compare brain activity of entrepreneurs (who currently manage the organization they founded based on their venture idea) and managers (who are constantly involved in making strategic decisions but have no venture experience) engaged in a gambling-task assessing exploitative vs. explorative decision-making. Compared with managers, entrepreneurs showed higher decision-making efficiency, and a stronger activation in regions of frontopolar cortex (FPC) previously associated with explorative choice. Moreover, activity across a network of regions previously linked to explore/exploit tradeoffs explained individual differences in choice efficiency. These results suggest new avenues for the study of individual differences in the neural antecedents of efficient decision-making. PMID:24478664

Use of economic evaluation in decision making: evidence and recommendations for improvement.

PubMed

Simoens, Steven

2010-10-22

Information about the value for money of a medicine as derived from an economic evaluation can be used for decision-making purposes by policy makers, healthcare payers, healthcare professionals and pharmaceutical companies. This article illustrates the use of economic evaluation by decision makers and formulates a number of recommendations to enhance the use of such evaluations for decision-making purposes. Over the last decades, there has been a substantial increase in the number of economic evaluations assessing the value for money of medicines. Economic evaluation is used by policy makers and healthcare payers to inform medicine pricing/reimbursement decisions in more and more countries. It is a suitable tool to evaluate medicines and to present information about their value for money to decision makers in a familiar format. In order to fully exploit the use of economic evaluation for decision-making purposes, researchers need to take care to conduct such economic evaluations according to methodologically sound principles. Additionally, researchers need to take into account the decision-making context. They need to identify the various objectives that decision makers pursue and discuss how decision makers can use study findings to attain these objectives. These issues require further attention from researchers, policy makers, healthcare payers, healthcare professionals and pharmaceutical companies with a view to optimizing the use of economic evaluation in decision making.

DECISION-MAKING ALIGNED WITH RAPID-CYCLE EVALUATION IN HEALTH CARE.

PubMed

Schneeweiss, Sebastian; Shrank, William H; Ruhl, Michael; Maclure, Malcolm

2015-01-01

Availability of real-time electronic healthcare data provides new opportunities for rapid-cycle evaluation (RCE) of health technologies, including healthcare delivery and payment programs. We aim to align decision-making processes with stages of RCE to optimize the usefulness and impact of rapid results. Rational decisions about program adoption depend on program effect size in relation to externalities, including implementation cost, sustainability, and likelihood of broad adoption. Drawing on case studies and experience from drug safety monitoring, we examine how decision makers have used scientific evidence on complex interventions in the past. We clarify how RCE alters the nature of policy decisions; develop the RAPID framework for synchronizing decision-maker activities with stages of RCE; and provide guidelines on evidence thresholds for incremental decision-making. In contrast to traditional evaluations, RCE provides early evidence on effectiveness and facilitates a stepped approach to decision making in expectation of future regularly updated evidence. RCE allows for identification of trends in adjusted effect size. It supports adapting a program in midstream in response to interim findings, or adapting the evaluation strategy to identify true improvements earlier. The 5-step RAPID approach that utilizes the cumulating evidence of program effectiveness over time could increase policy-makers' confidence in expediting decisions. RCE enables a step-wise approach to HTA decision-making, based on gradually emerging evidence, reducing delays in decision-making processes after traditional one-time evaluations.

Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach.

PubMed

Cavagnaro, Daniel R; Gonzalez, Richard; Myung, Jay I; Pitt, Mark A

2013-02-01

Collecting data to discriminate between models of risky choice requires careful selection of decision stimuli. Models of decision making aim to predict decisions across a wide range of possible stimuli, but practical limitations force experimenters to select only a handful of them for actual testing. Some stimuli are more diagnostic between models than others, so the choice of stimuli is critical. This paper provides the theoretical background and a methodological framework for adaptive selection of optimal stimuli for discriminating among models of risky choice. The approach, called Adaptive Design Optimization (ADO), adapts the stimulus in each experimental trial based on the results of the preceding trials. We demonstrate the validity of the approach with simulation studies aiming to discriminate Expected Utility, Weighted Expected Utility, Original Prospect Theory, and Cumulative Prospect Theory models.

Analyzing Decision Logs to Understand Decision Making in Serious Crime Investigations.

PubMed

Dando, Coral J; Ormerod, Thomas C

2017-12-01

Objective To study decision making by detectives when investigating serious crime through the examination of decision logs to explore hypothesis generation and evidence selection. Background Decision logs are used to record and justify decisions made during serious crime investigations. The complexity of investigative decision making is well documented, as are the errors associated with miscarriages of justice and inquests. The use of decision logs has not been the subject of an empirical investigation, yet they offer an important window into the nature of investigative decision making in dynamic, time-critical environments. Method A sample of decision logs from British police forces was analyzed qualitatively and quantitatively to explore hypothesis generation and evidence selection by police detectives. Results Analyses revealed diversity in documentation of decisions that did not correlate with case type and identified significant limitations of the decision log approach to supporting investigative decision making. Differences emerged between experienced and less experienced officers' decision log records in exploration of alternative hypotheses, generation of hypotheses, and sources of evidential inquiry opened over phase of investigation. Conclusion The practical use of decision logs is highly constrained by their format and context of use. Despite this, decision log records suggest that experienced detectives display strategic decision making to avoid confirmation and satisficing, which affect less experienced detectives. Application Potential applications of this research include both training in case documentation and the development of new decision log media that encourage detectives, irrespective of experience, to generate multiple hypotheses and optimize the timely selection of evidence to test them.

Assessment, design and control strategy development of a fuel cell hybrid electric vehicle for CSU's EcoCAR

NASA Astrophysics Data System (ADS)

Fox, Matthew D.

Advanced automotive technology assessment and powertrain design are increasingly performed through modeling, simulation, and optimization. But technology assessments usually target many competing criteria making any individual optimization challenging and arbitrary. Further, independent design simulations and optimizations take considerable time to execute, and design constraints and objectives change throughout the design process. Changes in design considerations usually require re-processing of simulations and more time. In this thesis, these challenges are confronted through CSU's participation in the EcoCAR2 hybrid vehicle design competition. The complexity of the competition's design objectives leveraged development of a decision support system tool to aid in multi-criteria decision making across technologies and to perform powertrain optimization. To make the decision support system interactive, and bypass the problem of long simulation times, a new approach was taken. The result of this research is CSU's architecture selection and component sizing, which optimizes a composite objective function representing the competition score. The selected architecture is an electric vehicle with an onboard range extending hydrogen fuel cell system. The vehicle has a 145kW traction motor, 18.9kWh of lithium ion battery, a 15kW fuel cell system, and 5kg of hydrogen storage capacity. Finally, a control strategy was developed that improves the vehicles performance throughout the driving range under variable driving conditions. In conclusion, the design process used in this research is reviewed and evaluated against other common design methodologies. I conclude, through the highlighted case studies, that the approach is more comprehensive than other popular design methodologies and is likely to lead to a higher quality product. The upfront modeling work and decision support system formulation will pay off in superior and timely knowledge transfer and more informed design decisions. The hypothesis is supported by the three case studies examined in this thesis.

Identification of the Criteria for Decision Making of Cut-Away Peatland Reuse

NASA Astrophysics Data System (ADS)

Padur, Kadi; Ilomets, Mati; Põder, Tõnis

2017-03-01

The total area of abandoned milled peatlands which need to be rehabilitated for sustainable land-use is nearly 10,000 ha in Estonia. According to the agreement between Estonia and the European Union, Estonia has to create suitable conditions for restoration of 2000 ha of abandoned cut-away peatlands by 2023. The decisions on rehabilitation of abandoned milled peatlands have so far relied on a limited knowledgebase with unestablished methodologies, thus the decision making process needs a significant improvement. This study aims to improve the methodology by identifying the criteria for optimal decision making to ensure sustainable land use planning after peat extraction. Therefore relevant environmental, social and economic restrictive and weighted comparison criteria, which assess reuse alternatives suitability for achieving the goal, is developed in cooperation with stakeholders. Restrictive criteria are arranged into a decision tree to help to determine the implementable reuse alternatives in various situations. Weighted comparison criteria are developed in cooperation with stakeholders to rank the reuse alternatives. The comparison criteria are organised hierarchically into a value tree. In the situation, where the selection of a suitable rehabilitation alternative for a specific milled peatland is going to be made, the weighted comparison criteria values need to be identified and the presented approach supports the optimal and transparent decision making. In addition to Estonian context the general results of the study could also be applied to a cut-away peatlands in other regions with need-based site-dependent modifications of criteria values and weights.

Identification of the Criteria for Decision Making of Cut-Away Peatland Reuse.

PubMed

Padur, Kadi; Ilomets, Mati; Põder, Tõnis

2017-03-01

The total area of abandoned milled peatlands which need to be rehabilitated for sustainable land-use is nearly 10,000 ha in Estonia. According to the agreement between Estonia and the European Union, Estonia has to create suitable conditions for restoration of 2000 ha of abandoned cut-away peatlands by 2023. The decisions on rehabilitation of abandoned milled peatlands have so far relied on a limited knowledgebase with unestablished methodologies, thus the decision making process needs a significant improvement. This study aims to improve the methodology by identifying the criteria for optimal decision making to ensure sustainable land use planning after peat extraction. Therefore relevant environmental, social and economic restrictive and weighted comparison criteria, which assess reuse alternatives suitability for achieving the goal, is developed in cooperation with stakeholders. Restrictive criteria are arranged into a decision tree to help to determine the implementable reuse alternatives in various situations. Weighted comparison criteria are developed in cooperation with stakeholders to rank the reuse alternatives. The comparison criteria are organised hierarchically into a value tree. In the situation, where the selection of a suitable rehabilitation alternative for a specific milled peatland is going to be made, the weighted comparison criteria values need to be identified and the presented approach supports the optimal and transparent decision making. In addition to Estonian context the general results of the study could also be applied to a cut-away peatlands in other regions with need-based site-dependent modifications of criteria values and weights.

A general theory of intertemporal decision-making and the perception of time.

PubMed

Namboodiri, Vijay M K; Mihalas, Stefan; Marton, Tanya M; Hussain Shuler, Marshall G

2014-01-01

Animals and humans make decisions based on their expected outcomes. Since relevant outcomes are often delayed, perceiving delays and choosing between earlier vs. later rewards (intertemporal decision-making) is an essential component of animal behavior. The myriad observations made in experiments studying intertemporal decision-making and time perception have not yet been rationalized within a single theory. Here we present a theory-Training-Integrated Maximized Estimation of Reinforcement Rate (TIMERR)-that explains a wide variety of behavioral observations made in intertemporal decision-making and the perception of time. Our theory postulates that animals make intertemporal choices to optimize expected reward rates over a limited temporal window which includes a past integration interval-over which experienced reward rate is estimated-as well as the expected delay to future reward. Using this theory, we derive mathematical expressions for both the subjective value of a delayed reward and the subjective representation of the delay. A unique contribution of our work is in finding that the past integration interval directly determines the steepness of temporal discounting and the non-linearity of time perception. In so doing, our theory provides a single framework to understand both intertemporal decision-making and time perception.

A general theory of intertemporal decision-making and the perception of time

PubMed Central

Namboodiri, Vijay M. K.; Mihalas, Stefan; Marton, Tanya M.; Hussain Shuler, Marshall G.

2014-01-01

Animals and humans make decisions based on their expected outcomes. Since relevant outcomes are often delayed, perceiving delays and choosing between earlier vs. later rewards (intertemporal decision-making) is an essential component of animal behavior. The myriad observations made in experiments studying intertemporal decision-making and time perception have not yet been rationalized within a single theory. Here we present a theory—Training-Integrated Maximized Estimation of Reinforcement Rate (TIMERR)—that explains a wide variety of behavioral observations made in intertemporal decision-making and the perception of time. Our theory postulates that animals make intertemporal choices to optimize expected reward rates over a limited temporal window which includes a past integration interval—over which experienced reward rate is estimated—as well as the expected delay to future reward. Using this theory, we derive mathematical expressions for both the subjective value of a delayed reward and the subjective representation of the delay. A unique contribution of our work is in finding that the past integration interval directly determines the steepness of temporal discounting and the non-linearity of time perception. In so doing, our theory provides a single framework to understand both intertemporal decision-making and time perception. PMID:24616677

Enhanced decision making through neuroscience

NASA Astrophysics Data System (ADS)

Szu, Harold; Jung, TP; Makeig, Scott

2012-06-01

We propose to enhance the decision making of pilot, co-pilot teams, over a range of vehicle platforms, with the aid of neuroscience. The goal is to optimize this collaborative decision making interplay in time-critical, stressful situations. We will research and measure human facial expressions, personality typing, and brainwave measurements to help answer questions related to optimum decision-making in group situations. Further, we propose to examine the nature of intuition in this decision making process. The brainwave measurements will be facilitated by a University of California, San Diego (UCSD) developed wireless Electroencephalography (EEG) sensing cap. We propose to measure brainwaves covering the whole head area with an electrode density of N=256, and yet keep within the limiting wireless bandwidth capability of m=32 readouts. This is possible because solving Independent Component Analysis (ICA) and finding the hidden brainwave sources allow us to concentrate selective measurements with an organized sparse source -->s sensing matrix [Φs], rather than the traditional purely random compressive sensing (CS) matrix[Φ].

Medical decision-making in children and adolescents: developmental and neuroscientific aspects.

PubMed

Grootens-Wiegers, Petronella; Hein, Irma M; van den Broek, Jos M; de Vries, Martine C

2017-05-08

Various international laws and guidelines stress the importance of respecting the developing autonomy of children and involving minors in decision-making regarding treatment and research participation. However, no universal agreement exists as to at what age minors should be deemed decision-making competent. Minors of the same age may show different levels of maturity. In addition, patients deemed rational conversation-partners as a child can suddenly become noncompliant as an adolescent. Age, context and development all play a role in decision-making competence. In this article we adopt a perspective on competence that specifically focuses on the impact of brain development on the child's decision-making process. We believe that the discussion on decision-making competence of minors can greatly benefit from a multidisciplinary approach. We adopted such an approach in order to contribute to the understanding on how to deal with children in decision-making situations. Evidence emerging from neuroscience research concerning the developing brain structures in minors is combined with insights from various other fields, such as psychology, decision-making science and ethics. Four capacities have been described that are required for (medical) decision-making: (1) communicating a choice; (2) understanding; (3) reasoning; and (4) appreciation. Each capacity is related to a number of specific skills and abilities that need to be sufficiently developed to support the capacity. Based on this approach it can be concluded that at the age of 12 children can have the capacity to be decision-making competent. However, this age coincides with the onset of adolescence. Early development of the brain's reward system combined with late development of the control system diminishes decision-making competence in adolescents in specific contexts. We conclude that even adolescents possessing capacities required for decision-making, may need support of facilitating environmental factors. This paper intends to offer insight in neuroscientific mechanisms underlying the medical decision-making capacities in minors and to stimulate practices for optimal involvement of minors. Developing minors become increasingly capable of decision-making, but the neurobiological development in adolescence affects competence in specific contexts. Adequate support should be offered in order to create a context in which minors can make competently make decisions.

Toward Optimal Decision Making among Vulnerable Patients Referred for Cardiac Surgery: A Qualitative Analysis of Patient and Provider Perspectives.

PubMed

Gainer, Ryan A; Curran, Janet; Buth, Karen J; David, Jennie G; Légaré, Jean-Francois; Hirsch, Gregory M

2017-07-01

Comprehension of risks, benefits, and alternative treatment options has been shown to be poor among patients referred for cardiac interventions. Patients' values and preferences are rarely explicitly sought. An increasing proportion of frail and older patients are undergoing complex cardiac surgical procedures with increased risk of both mortality and prolonged institutional care. We sought input from patients and caregivers to determine the optimal approach to decision making in this vulnerable patient population. Focus groups were held with both providers and former patients. Three focus groups were convened for Coronary Artery Bypass Graft (CABG), Valve, or CABG +Valve patients ≥ 70 y old (2-y post-op, ≤ 8-wk post-op, complicated post-op course) (n = 15). Three focus groups were convened for Intermediate Medical Care Unit (IMCU) nurses, Intensive Care Unit (ICU) nurses, surgeons, anesthesiologists and cardiac intensivists (n = 20). We used a semi-structured interview format to ask questions surrounding the informed consent process. Transcribed audio data was analyzed to develop consistent and comprehensive themes. We identified 5 main themes that influence the decision making process: educational barriers, educational facilitators, patient autonomy and perceived autonomy, patient and family expectations of care, and decision making advocates. All themes were influenced by time constraints experienced in the current consent process. Patient groups expressed a desire to receive information earlier in their care to allow time to identify personal values and preferences in developing plans for treatment. Both groups strongly supported a formal approach for shared decision making with a decisional coach to provide information and facilitate communication with the care team. Identifying the barriers and facilitators to patient and caretaker engagement in decision making is a key step in the development of a structured, patient-centered SDM approach. Intervention early in the decision process, the use of individualized decision aids that employ graphic risk presentations, and a dedicated decisional coach were identified by patients and providers as approaches with a high potential for success. The impact of such a formalized shared decision making process in cardiac surgery on decisional quality will need to be formally assessed. Given the trend toward older and frail patients referred for complex cardiac procedures, the need for an effective shared decision making process is compelling.

Decision making with epistemic uncertainty under safety constraints: An application to seismic design

USGS Publications Warehouse

Veneziano, D.; Agarwal, A.; Karaca, E.

2009-01-01

The problem of accounting for epistemic uncertainty in risk management decisions is conceptually straightforward, but is riddled with practical difficulties. Simple approximations are often used whereby future variations in epistemic uncertainty are ignored or worst-case scenarios are postulated. These strategies tend to produce sub-optimal decisions. We develop a general framework based on Bayesian decision theory and exemplify it for the case of seismic design of buildings. When temporal fluctuations of the epistemic uncertainties and regulatory safety constraints are included, the optimal level of seismic protection exceeds the normative level at the time of construction. Optimal Bayesian decisions do not depend on the aleatory or epistemic nature of the uncertainties, but only on the total (epistemic plus aleatory) uncertainty and how that total uncertainty varies randomly during the lifetime of the project. ?? 2009 Elsevier Ltd. All rights reserved.

Determinants of judgment and decision making quality: the interplay between information processing style and situational factors

PubMed Central

Ayal, Shahar; Rusou, Zohar; Zakay, Dan; Hochman, Guy

2015-01-01

A framework is presented to better characterize the role of individual differences in information processing style and their interplay with contextual factors in determining decision making quality. In Experiment 1, we show that individual differences in information processing style are flexible and can be modified by situational factors. Specifically, a situational manipulation that induced an analytical mode of thought improved decision quality. In Experiment 2, we show that this improvement in decision quality is highly contingent on the compatibility between the dominant thinking mode and the nature of the task. That is, encouraging an intuitive mode of thought led to better performance on an intuitive task but hampered performance on an analytical task. The reverse pattern was obtained when an analytical mode of thought was encouraged. We discuss the implications of these results for the assessment of decision making competence, and suggest practical directions to help individuals better adjust their information processing style to the situation at hand and make optimal decisions. PMID:26284011

Determinants of judgment and decision making quality: the interplay between information processing style and situational factors.

PubMed

Ayal, Shahar; Rusou, Zohar; Zakay, Dan; Hochman, Guy

2015-01-01

A framework is presented to better characterize the role of individual differences in information processing style and their interplay with contextual factors in determining decision making quality. In Experiment 1, we show that individual differences in information processing style are flexible and can be modified by situational factors. Specifically, a situational manipulation that induced an analytical mode of thought improved decision quality. In Experiment 2, we show that this improvement in decision quality is highly contingent on the compatibility between the dominant thinking mode and the nature of the task. That is, encouraging an intuitive mode of thought led to better performance on an intuitive task but hampered performance on an analytical task. The reverse pattern was obtained when an analytical mode of thought was encouraged. We discuss the implications of these results for the assessment of decision making competence, and suggest practical directions to help individuals better adjust their information processing style to the situation at hand and make optimal decisions.

Reduction of influence of task difficulty on perceptual decision making by STN deep brain stimulation.

PubMed

Green, Nikos; Bogacz, Rafal; Huebl, Julius; Beyer, Ann-Kristin; Kühn, Andrea A; Heekeren, Hauke R

2013-09-09

Neurocomputational models of optimal decision making ascribe a crucial role-the computation of conflict between choice alternatives-to the subthalamic nucleus (STN). Specifically, these models predict that deep brain stimulation (DBS) of the STN will diminish the influence of decision conflict on decision making. In this work, patients with Parkinson's disease judged the direction of motion in random dot stimuli while ON and OFF DBS. To induce decision conflict, we varied the task difficulty (motion coherence), leading to increased reaction time (RT) in trials with greater task difficulty in healthy subjects. Results indicate that DBS significantly influences performance for perceptual decisions under high decision conflict. RT increased substantially OFF DBS as the task became more difficult, and a diffusion model best accounted for behavioral data. In contrast, ON DBS, the influence of task difficulty on RT was significantly reduced and a race model best accounted for the observed data. Individual data fits of evidence accumulation models demonstrate different information processing under distinct DBS states. Furthermore, ON DBS, speed-accuracy tradeoffs affected the magnitude of decision criterion adjustment significantly less compared to OFF DBS. Together, these findings suggest a crucial role for the STN in adjusting decision making during high-conflict trials in perceptual decision making. Copyright © 2013 Elsevier Ltd. All rights reserved.

Reinforcement Learning Multi-Agent Modeling of Decision-Making Agents for the Study of Transboundary Surface Water Conflicts with Application to the Syr Darya River Basin

NASA Astrophysics Data System (ADS)

Riegels, N.; Siegfried, T.; Pereira Cardenal, S. J.; Jensen, R. A.; Bauer-Gottwein, P.

2008-12-01

In most economics--driven approaches to optimizing water use at the river basin scale, the system is modelled deterministically with the goal of maximizing overall benefits. However, actual operation and allocation decisions must be made under hydrologic and economic uncertainty. In addition, river basins often cross political boundaries, and different states may not be motivated to cooperate so as to maximize basin- scale benefits. Even within states, competing agents such as irrigation districts, municipal water agencies, and large industrial users may not have incentives to cooperate to realize efficiency gains identified in basin- level studies. More traditional simulation--optimization approaches assume pre-commitment by individual agents and stakeholders and unconditional compliance on each side. While this can help determine attainable gains and tradeoffs from efficient management, such hardwired policies do not account for dynamic feedback between agents themselves or between agents and their environments (e.g. due to climate change etc.). In reality however, we are dealing with an out-of-equilibrium multi-agent system, where there is neither global knowledge nor global control, but rather continuous strategic interaction between decision making agents. Based on the theory of stochastic games, we present a computational framework that allows for studying the dynamic feedback between decision--making agents themselves and an inherently uncertain environment in a spatially and temporally distributed manner. Agents with decision-making control over water allocation such as countries, irrigation districts, and municipalities are represented by reinforcement learning agents and coupled to a detailed hydrologic--economic model. This approach emphasizes learning by agents from their continuous interaction with other agents and the environment. It provides a convenient framework for the solution of the problem of dynamic decision-making in a mixed cooperative / non-cooperative environment with which different institutional setups and incentive systems can be studied so to identify reasonable ways to reach desirable, Pareto--optimal allocation outcomes. Preliminary results from an application to the Syr Darya river basin in Central Asia will be presented and discussed. The Syr Darya River is a classic example of a transboundary river basin in which basin-wide efficiency gains identified in optimization studies have not been sufficient to induce cooperative management of the river by the riparian states.

Diffusion Decision Model: Current Issues and History

PubMed Central

Ratcliff, Roger; Smith, Philip L.; Brown, Scott D.; McKoon, Gail

2016-01-01

There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in psychology. They view decision making as a process of noisy accumulation of evidence from a stimulus. The standard model assumes that evidence accumulates at a constant rate during the second or two it takes to make a decision. This process can be linked to the behaviors of populations of neurons and to theories of optimality. Diffusion models have been used successfully in a range of cognitive tasks and as psychometric tools in clinical research to examine individual differences. In this article, we relate the models to both earlier and more recent research in psychology. PMID:26952739

Evidence of Cognitive Bias in Decision Making Around Implantable-Cardioverter Defibrillators: A Qualitative Framework Analysis.

PubMed

Matlock, Daniel D; Jones, Jacqueline; Nowels, Carolyn T; Jenkins, Amy; Allen, Larry A; Kutner, Jean S

2017-11-01

Studies have demonstrated that patients with primary prevention implantable cardioverter-defibrillators (ICDs) often misunderstand the ICD. Advances in behavioral economics demonstrate that some misunderstandings may be due to cognitive biases. We aimed to explore the influence of cognitive bias on ICD decision making. We used a qualitative framework analysis including 9 cognitive biases: affect heuristic, affective forecasting, anchoring, availability, default effects, halo effects, optimism bias, framing effects, and state dependence. We interviewed 48 patients from 4 settings in Denver. The majority were male (n = 32). Overall median age was 61 years. We found frequent evidence for framing, default, and halo effects; some evidence of optimism bias, affect heuristic, state dependence, anchoring and availability bias; and little or no evidence of affective forecasting. Framing effects were apparent in overestimation of benefits and downplaying or omitting potential harms. We found evidence of cognitive bias in decision making for ICD implantation. The majority of these biases appeared to encourage ICD treatment. Published by Elsevier Inc.

A preference aggregation model and application in AHP-group decision making

NASA Astrophysics Data System (ADS)

Yang, Taiyi; Yang, De; Chao, Xiangrui

2018-04-01

Group decision making process integrate individual preferences to obtain the group preference by applying aggregation rules and preference relations. The two most useful approaches, the aggregation of individual judgements and the aggregation of individual priorities, traditionally are employed in the Analytic Hierarchy Process to deal with group decision making problems. In both cases, it is assumed that the group preference is approximate weighted mathematical expectation of individual judgements and individual priorities. We propose new preference aggregation methods using optimization models in order to obtain group preference which is close to all individual priorities. Some illustrative examples are finally examined to demonstrate proposed models for application.

Making Good Decisions in Healthcare with Multi-Criteria Decision Analysis: The Use, Current Research and Future Development of MCDA.

PubMed

Mühlbacher, Axel C; Kaczynski, Anika

2016-02-01

Healthcare decision making is usually characterized by a low degree of transparency. The demand for transparent decision processes can be fulfilled only when assessment, appraisal and decisions about health technologies are performed under a systematic construct of benefit assessment. The benefit of an intervention is often multidimensional and, thus, must be represented by several decision criteria. Complex decision problems require an assessment and appraisal of various criteria; therefore, a decision process that systematically identifies the best available alternative and enables an optimal and transparent decision is needed. For that reason, decision criteria must be weighted and goal achievement must be scored for all alternatives. Methods of multi-criteria decision analysis (MCDA) are available to analyse and appraise multiple clinical endpoints and structure complex decision problems in healthcare decision making. By means of MCDA, value judgments, priorities and preferences of patients, insurees and experts can be integrated systematically and transparently into the decision-making process. This article describes the MCDA framework and identifies potential areas where MCDA can be of use (e.g. approval, guidelines and reimbursement/pricing of health technologies). A literature search was performed to identify current research in healthcare. The results showed that healthcare decision making is addressing the problem of multiple decision criteria and is focusing on the future development and use of techniques to weight and score different decision criteria. This article emphasizes the use and future benefit of MCDA.

Satisficing in split-second decision making is characterized by strategic cue discounting.

PubMed

Oh, Hanna; Beck, Jeffrey M; Zhu, Pingping; Sommer, Marc A; Ferrari, Silvia; Egner, Tobias

2016-12-01

Much of our real-life decision making is bounded by uncertain information, limitations in cognitive resources, and a lack of time to allocate to the decision process. It is thought that humans overcome these limitations through satisficing, fast but "good-enough" heuristic decision making that prioritizes some sources of information (cues) while ignoring others. However, the decision-making strategies we adopt under uncertainty and time pressure, for example during emergencies that demand split-second choices, are presently unknown. To characterize these decision strategies quantitatively, the present study examined how people solve a novel multicue probabilistic classification task under varying time pressure, by tracking shifts in decision strategies using variational Bayesian inference. We found that under low time pressure, participants correctly weighted and integrated all available cues to arrive at near-optimal decisions. With increasingly demanding, subsecond time pressures, however, participants systematically discounted a subset of the cue information by dropping the least informative cue(s) from their decision making process. Thus, the human cognitive apparatus copes with uncertainty and severe time pressure by adopting a "drop-the-worst" cue decision making strategy that minimizes cognitive time and effort investment while preserving the consideration of the most diagnostic cue information, thus maintaining "good-enough" accuracy. This advance in our understanding of satisficing strategies could form the basis of predicting human choices in high time pressure scenarios. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Noise, cost and speed-accuracy trade-offs: decision-making in a decentralized system

PubMed Central

Marshall, James A.R.; Dornhaus, Anna; Franks, Nigel R.; Kovacs, Tim

2005-01-01

Many natural and artificial decision-making systems face decision problems where there is an inherent compromise between two or more objectives. One such common compromise is between the speed and accuracy of a decision. The ability to exploit the characteristics of a decision problem in order to vary between the extremes of making maximally rapid, or maximally accurate decisions, is a useful property of such systems. Colonies of the ant Temnothorax albipennis (formerly Leptothorax albipennis) are a paradigmatic decentralized decision-making system, and have been shown flexibly to compromise accuracy for speed when making decisions during house-hunting. During emigration, a colony must typically evaluate and choose between several possible alternative new nest sites of differing quality. In this paper, we examine this speed-accuracy trade-off through modelling, and conclude that noise and time-cost of assessing alternative choices are likely to be significant for T. albipennis. Noise and cost of such assessments are likely to mean that T. albipennis' decision-making mechanism is Pareto-optimal in one crucial regard; increasing the willingness of individuals to change their decisions cannot improve collective accuracy overall without impairing speed. We propose that a decentralized control algorithm based on this emigration behaviour may be derived for applications in engineering domains and specify the characteristics of the problems to which it should be suited, based on our new results. PMID:16849234

Pregnancy as Foreground in Cystic Fibrosis Carrier Testing Decisions in Primary Care

PubMed Central

Williams, Janet K.

2009-01-01

Cystic fibrosis carrier testing (CFCT) is among the first of the DNA tests offered prenatally in primary care settings. This paper from a descriptive qualitative study describes the influence of pregnancy in CFCT decisions by women receiving community-based prenatal care. Twenty-seven women receiving prenatal care in Midwestern U.S. primary care clinics completed semistructured interviews. Audiotaped interviews were analyzed using content analysis. Participants described decision-making influences and strategies from the perspective of “being pregnant.” Patterns of attitudes and beliefs include (1) dealing with emotions, (2) pregnancy is natural, and (3) thinking about the baby. Strategies in the decision-making process included (1) reducing stress, (2) choosing what is relevant, (3) doing everything right, (4) wanting to be prepared, (5) delaying information, and (6) trusting God. While other factors were mentioned by some women, major themes reflect the influence of currently being pregnant on the decision-making process. These findings suggest that pregnancy is a powerful influence on the decision-making process and may not be the optimal time to make fully informed decisions regarding genetic carrier testing. Further understanding of factors influencing the genetic testing decision-making process is needed. Offering CFCT prior to conception is advocated. PMID:19309287

Dynamic Decision Making in Complex Task Environments: Principles and Neural Mechanisms

DTIC Science & Technology

2013-03-01

Dynamical models of cognition . Mathematical models of mental processes. Human performance optimization. U U U U Dr. Jay Myung 703-696-8487 Reset 1...we have continued to develop a neurodynamic theory of decision making, using a combination of computational and experimental approaches, to address...a long history in the field of human cognitive psychology. The theoretical foundations of this research can be traced back to signal detection

Departures From Optimality When Pursuing Multiple Approach or Avoidance Goals

PubMed Central

2016-01-01

This article examines how people depart from optimality during multiple-goal pursuit. The authors operationalized optimality using dynamic programming, which is a mathematical model used to calculate expected value in multistage decisions. Drawing on prospect theory, they predicted that people are risk-averse when pursuing approach goals and are therefore more likely to prioritize the goal in the best position than the dynamic programming model suggests is optimal. The authors predicted that people are risk-seeking when pursuing avoidance goals and are therefore more likely to prioritize the goal in the worst position than is optimal. These predictions were supported by results from an experimental paradigm in which participants made a series of prioritization decisions while pursuing either 2 approach or 2 avoidance goals. This research demonstrates the usefulness of using decision-making theories and normative models to understand multiple-goal pursuit. PMID:26963081

A Decision Support Model and Tool to Assist Financial Decision-Making in Universities

ERIC Educational Resources Information Center

Bhayat, Imtiaz; Manuguerra, Maurizio; Baldock, Clive

2015-01-01

In this paper, a model and tool is proposed to assist universities and other mission-based organisations to ascertain systematically the optimal portfolio of projects, in any year, meeting the organisations risk tolerances and available funds. The model and tool presented build on previous work on university operations and decision support systems…

Modeling human decision making behavior in supervisory control

NASA Technical Reports Server (NTRS)

Tulga, M. K.; Sheridan, T. B.

1977-01-01

An optimal decision control model was developed, which is based primarily on a dynamic programming algorithm which looks at all the available task possibilities, charts an optimal trajectory, and commits itself to do the first step (i.e., follow the optimal trajectory during the next time period), and then iterates the calculation. A Bayesian estimator was included which estimates the tasks which might occur in the immediate future and provides this information to the dynamic programming routine. Preliminary trials comparing the human subject's performance to that of the optimal model show a great similarity, but indicate that the human skips certain movements which require quick change in strategy.

Arthroscopy Journal Prizes Are Major Decisions.

PubMed

Lubowitz, James H; Brand, Jefferson C; Provencher, Matthew T; Rossi, Michael J

2016-01-01

According to the Harvard Business Review, the optimal number of people in a decision-making group is no more than 8. Thus, it is no surprise that 18 Arthroscopy journal associate editors had difficulty making a major decision. In the end, 18 editors did successfully select the 2015 winner of the Best Comparative Study Prize. All studies have limitations, but from a statistical standpoint, the editors believe that the conclusions of the winning study are likely correct. Copyright © 2016 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

Asynchronous decision making in a memorized paddle pressing task

NASA Astrophysics Data System (ADS)

Dankert, James R.; Olson, Byron; Si, Jennie

2008-12-01

This paper presents a method for asynchronous decision making using recorded neural data in a binary decision task. This is a demonstration of a technique for developing motor cortical neural prosthetics that do not rely on external cued timing information. The system presented in this paper uses support vector machines and leaky integrate-and-fire elements to predict directional paddle presses. In addition to the traditional metrics of accuracy, asynchronous systems must also optimize the time needed to make a decision. The system presented is able to predict paddle presses with a median accuracy of 88% and all decisions are made before the time of the actual paddle press. An alternative bit rate measure of performance is defined to show that the system proposed here is able to perform the task with the same efficiency as the rats.

Improved ant colony optimization for optimal crop and irrigation water allocation by incorporating domain knowledge

USDA-ARS?s Scientific Manuscript database

An improved ant colony optimization (ACO) formulation for the allocation of crops and water to different irrigation areas is developed. The formulation enables dynamic adjustment of decision variable options and makes use of visibility factors (VFs, the domain knowledge that can be used to identify ...

Optimizing the response to surveillance alerts in automated surveillance systems.

PubMed

Izadi, Masoumeh; Buckeridge, David L

2011-02-28

Although much research effort has been directed toward refining algorithms for disease outbreak alerting, considerably less attention has been given to the response to alerts generated from statistical detection algorithms. Given the inherent inaccuracy in alerting, it is imperative to develop methods that help public health personnel identify optimal policies in response to alerts. This study evaluates the application of dynamic decision making models to the problem of responding to outbreak detection methods, using anthrax surveillance as an example. Adaptive optimization through approximate dynamic programming is used to generate a policy for decision making following outbreak detection. We investigate the degree to which the model can tolerate noise theoretically, in order to keep near optimal behavior. We also evaluate the policy from our model empirically and compare it with current approaches in routine public health practice for investigating alerts. Timeliness of outbreak confirmation and total costs associated with the decisions made are used as performance measures. Using our approach, on average, 80 per cent of outbreaks were confirmed prior to the fifth day of post-attack with considerably less cost compared to response strategies currently in use. Experimental results are also provided to illustrate the robustness of the adaptive optimization approach and to show the realization of the derived error bounds in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Manipulating memory efficacy affects the behavioral and neural profiles of deterministic learning and decision-making.

PubMed

Tremel, Joshua J; Ortiz, Daniella M; Fiez, Julie A

2018-06-01

When making a decision, we have to identify, collect, and evaluate relevant bits of information to ensure an optimal outcome. How we approach a given choice can be influenced by prior experience. Contextual factors and structural elements of these past decisions can cause a shift in how information is encoded and can in turn influence later decision-making. In this two-experiment study, we sought to manipulate declarative memory efficacy and decision-making in a concurrent discrimination learning task by altering the amount of information to be learned. Subjects learned correct responses to pairs of items across several repetitions of a 50- or 100-pair set and were tested for memory retention. In one experiment, this memory test interrupted learning after an initial encoding experience in order to test for early encoding differences and associate those differences with changes in decision-making. In a second experiment, we used fMRI to probe neural differences between the two list-length groups related to decision-making across learning and assessed subsequent memory retention. We found that a striatum-based system was associated with decision-making patterns when learning a longer list of items, while a medial cortical network was associated with patterns when learning a shorter list. Additionally, the hippocampus was exclusively active for the shorter list group. Altogether, these behavioral, computational, and imaging results provide evidence that multiple types of mnemonic representations contribute to experienced-based decision-making. Moreover, contextual and structural factors of the task and of prior decisions can influence what types of evidence are drawn upon during decision-making. Copyright © 2018 Elsevier Ltd. All rights reserved.

Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach

PubMed Central

Cavagnaro, Daniel R.; Gonzalez, Richard; Myung, Jay I.; Pitt, Mark A.

2014-01-01

Collecting data to discriminate between models of risky choice requires careful selection of decision stimuli. Models of decision making aim to predict decisions across a wide range of possible stimuli, but practical limitations force experimenters to select only a handful of them for actual testing. Some stimuli are more diagnostic between models than others, so the choice of stimuli is critical. This paper provides the theoretical background and a methodological framework for adaptive selection of optimal stimuli for discriminating among models of risky choice. The approach, called Adaptive Design Optimization (ADO), adapts the stimulus in each experimental trial based on the results of the preceding trials. We demonstrate the validity of the approach with simulation studies aiming to discriminate Expected Utility, Weighted Expected Utility, Original Prospect Theory, and Cumulative Prospect Theory models. PMID:24532856

Safety Assessment of Dangerous Goods Transport Enterprise Based on the Relative Entropy Aggregation in Group Decision Making Model

PubMed Central

Wu, Jun; Li, Chengbing; Huo, Yueying

2014-01-01

Safety of dangerous goods transport is directly related to the operation safety of dangerous goods transport enterprise. Aiming at the problem of the high accident rate and large harm in dangerous goods logistics transportation, this paper took the group decision making problem based on integration and coordination thought into a multiagent multiobjective group decision making problem; a secondary decision model was established and applied to the safety assessment of dangerous goods transport enterprise. First of all, we used dynamic multivalue background and entropy theory building the first level multiobjective decision model. Secondly, experts were to empower according to the principle of clustering analysis, and combining with the relative entropy theory to establish a secondary rally optimization model based on relative entropy in group decision making, and discuss the solution of the model. Then, after investigation and analysis, we establish the dangerous goods transport enterprise safety evaluation index system. Finally, case analysis to five dangerous goods transport enterprises in the Inner Mongolia Autonomous Region validates the feasibility and effectiveness of this model for dangerous goods transport enterprise recognition, which provides vital decision making basis for recognizing the dangerous goods transport enterprises. PMID:25477954

Safety assessment of dangerous goods transport enterprise based on the relative entropy aggregation in group decision making model.

PubMed

Wu, Jun; Li, Chengbing; Huo, Yueying

2014-01-01

Safety of dangerous goods transport is directly related to the operation safety of dangerous goods transport enterprise. Aiming at the problem of the high accident rate and large harm in dangerous goods logistics transportation, this paper took the group decision making problem based on integration and coordination thought into a multiagent multiobjective group decision making problem; a secondary decision model was established and applied to the safety assessment of dangerous goods transport enterprise. First of all, we used dynamic multivalue background and entropy theory building the first level multiobjective decision model. Secondly, experts were to empower according to the principle of clustering analysis, and combining with the relative entropy theory to establish a secondary rally optimization model based on relative entropy in group decision making, and discuss the solution of the model. Then, after investigation and analysis, we establish the dangerous goods transport enterprise safety evaluation index system. Finally, case analysis to five dangerous goods transport enterprises in the Inner Mongolia Autonomous Region validates the feasibility and effectiveness of this model for dangerous goods transport enterprise recognition, which provides vital decision making basis for recognizing the dangerous goods transport enterprises.

Functional specialization of the primate frontal cortex during decision making.

PubMed

Lee, Daeyeol; Rushworth, Matthew F S; Walton, Mark E; Watanabe, Masataka; Sakagami, Masamichi

2007-08-01

Economic theories of decision making are based on the principle of utility maximization, and reinforcement-learning theory provides computational algorithms that can be used to estimate the overall reward expected from alternative choices. These formal models not only account for a large range of behavioral observations in human and animal decision makers, but also provide useful tools for investigating the neural basis of decision making. Nevertheless, in reality, decision makers must combine different types of information about the costs and benefits associated with each available option, such as the quality and quantity of expected reward and required work. In this article, we put forward the hypothesis that different subdivisions of the primate frontal cortex may be specialized to focus on different aspects of dynamic decision-making processes. In this hypothesis, the lateral prefrontal cortex is primarily involved in maintaining the state representation necessary to identify optimal actions in a given environment. In contrast, the orbitofrontal cortex and the anterior cingulate cortex might be primarily involved in encoding and updating the utilities associated with different sensory stimuli and alternative actions, respectively. These cortical areas are also likely to contribute to decision making in a social context.

Respiratory therapists' attitudes about participative decision making: relationship between managerial decision-making style and job satisfaction.

PubMed

Blake, Shane S; Kester, Lucy; Stoller, James K

2004-08-01

Studies of non-health-care work environments indicate that non-managerial employee job satisfaction is higher in companies that use participative (as opposed to autocratic) decision making. It has not been determined whether managerial decision-making style influences job satisfaction among respiratory therapists (RTs) and which managerial decision-making style RTs prefer. We surveyed Nebraska RTs' attitudes regarding their job satisfaction, their perceptions of their managers' decision-making styles (autocratic, consultative, and/or delegative), and which decision-making style they would prefer their managers to use. We sought to determine whether there is a significant correlation between RTs' perceptions of their managers' decision-making styles and the RTs' job satisfaction. The study population was 792 licensed and practicing non-managerial RTs in Nebraska, from which we randomly selected 565 RTs to survey. The self-administered, descriptive survey used 2 Likert scales (one for decision-making style and one for job satisfaction) and inquired about 57 items. The survey was mailed on October 1, 1999. On October 28, 1999, we sent a second mailing to RTs who had not responded. We received 271 responses (response rate 47.9%). The respondents were generally satisfied with their jobs (mean +/- SD Minnesota Satisfaction Questionnaire score 73.46 +/- 11.63). The sub-scale scores ranged from 20 ("very dissatisfied") to 100 ("very satisfied"). The respondents did not want autocratic managerial decision making (mean +/- SD autocratic sub-scale score 4.29 +/- 0.60). Autocratic decision making was associated with lower job satisfaction (r = 0.49), whereas consultative and delegative decision making were associated with higher job satisfaction (r = -0.31 and -0.48, respectively). RTs who worked in departments that had < 25 RT employees reported higher job satisfaction than did RTs in larger departments (p = 0.029). Our survey data indicate that (1) RTs prefer delegative and consultative managerial decision making, (2) job satisfaction was highest in departments that had < 25 RTs in the department and in which the manager practiced participative decision making. These findings offer guidance for organizing optimal work environments for RTs.

Integrated Watershed Management using the Watershed Management Optimization Support Tool (WMOST)

EPA Science Inventory

Integrated watershed management is an effective planning strategy to balance tradeoffs between competing water uses within a watershed. WMOST is an Excel-based decision tool to aid planners in making cost effective decisions that meet water quantity and quality regulations. WMOST...

Form and Objective of the Decision Rule in Absolute Identification

NASA Technical Reports Server (NTRS)

Balakrishnan, J. D.

1997-01-01

In several conditions of a line length identification experiment, the subjects' decision making strategies were systematically biased against the responses on the edges of the stimulus range. When the range and number of the stimuli were small, the bias caused the percentage of correct responses to be highest in the center and lowest on the extremes of the range. Two general classes of decision rules that would explain these results are considered. The first class assumes that subjects intend to adopt an optimal decision rule, but systematically misrepresent one or more parameters of the decision making context. The second class assumes that subjects use a different measure of performance than the one assumed by the experimenter: instead of maximizing the chances of a correct response, the subject attempts to minimize the expected size of the response error (a "fidelity criterion"). In a second experiment, extended experience and feedback did not diminish the bias effect, but explicitly penalizing all response errors equally, regardless of their size, did reduce or eliminate it in some subjects. Both results favor the fidelity criterion over the optimal rule.

Response threshold variance as a basis of collective rationality

PubMed Central

Yamamoto, Tatsuhiro

2017-01-01

Determining the optimal choice among multiple options is necessary in various situations, and the collective rationality of groups has recently become a major topic of interest. Social insects are thought to make such optimal choices by collecting individuals' responses relating to an option's value (=a quality-graded response). However, this behaviour cannot explain the collective rationality of brains because neurons can make only ‘yes/no’ responses on the basis of the response threshold. Here, we elucidate the basic mechanism underlying the collective rationality of such simple units and show that an ant species uses this mechanism. A larger number of units respond ‘yes’ to the best option available to a collective decision-maker using only the yes/no mechanism; thus, the best option is always selected by majority decision. Colonies of the ant Myrmica kotokui preferred the better option in a binary choice experiment. The preference of a colony was demonstrated by the workers, which exhibited variable thresholds between two options' qualities. Our results demonstrate how a collective decision-maker comprising simple yes/no judgement units achieves collective rationality without using quality-graded responses. This mechanism has broad applicability to collective decision-making in brain neurons, swarm robotics and human societies. PMID:28484636

Response threshold variance as a basis of collective rationality.

PubMed

Yamamoto, Tatsuhiro; Hasegawa, Eisuke

2017-04-01

Determining the optimal choice among multiple options is necessary in various situations, and the collective rationality of groups has recently become a major topic of interest. Social insects are thought to make such optimal choices by collecting individuals' responses relating to an option's value (=a quality-graded response). However, this behaviour cannot explain the collective rationality of brains because neurons can make only 'yes/no' responses on the basis of the response threshold. Here, we elucidate the basic mechanism underlying the collective rationality of such simple units and show that an ant species uses this mechanism. A larger number of units respond 'yes' to the best option available to a collective decision-maker using only the yes/no mechanism; thus, the best option is always selected by majority decision. Colonies of the ant Myrmica kotokui preferred the better option in a binary choice experiment. The preference of a colony was demonstrated by the workers, which exhibited variable thresholds between two options' qualities. Our results demonstrate how a collective decision-maker comprising simple yes/no judgement units achieves collective rationality without using quality-graded responses. This mechanism has broad applicability to collective decision-making in brain neurons, swarm robotics and human societies.

A Framework for Multi-Stakeholder Decision-Making and ...

EPA Pesticide Factsheets

This contribution describes the implementation of the conditional-value-at-risk (CVaR) metric to create a general multi-stakeholder decision-making framework. It is observed that stakeholder dissatisfactions (distance to their individual ideal solutions) can be interpreted as random variables. We thus shape the dissatisfaction distribution and find an optimal compromise solution by solving a CVaR minimization problem parameterized in the probability level. This enables us to generalize multi-stakeholder settings previously proposed in the literature that minimizes average and worst-case dissatisfactions. We use the concept of the CVaR norm to give a geometric interpretation to this problem and use the properties of this norm to prove that the CVaR minimization problem yields Pareto optimal solutions for any choice of the probability level. We discuss a broad range of potential applications of the framework. We demonstrate the framework in a bio-waste processing facility location case study, where we seek compromise solutions (facility locations) that balance stakeholder priorities on transportation, safety, water quality, and capital costs. This conference presentation abstract explains a new decision-making framework that computes compromise solution alternatives (reach consensus) by mitigating dissatisfactions among stakeholders as needed for SHC Decision Science and Support Tools project.

Decision-Aiding and Optimization for Vertical Navigation of Long-Haul Aircraft

NASA Technical Reports Server (NTRS)

Patrick, Nicholas J. M.; Sheridan, Thomas B.

1996-01-01

Most decisions made in the cockpit are related to safety, and have therefore been proceduralized in order to reduce risk. There are very few which are made on the basis of a value metric such as economic cost. One which can be shown to be value based, however, is the selection of a flight profile. Fuel consumption and flight time both have a substantial effect on aircraft operating cost, but they cannot be minimized simultaneously. In addition, winds, turbulence, and performance vary widely with altitude and time. These factors make it important and difficult for pilots to (a) evaluate the outcomes associated with a particular trajectory before it is flown and (b) decide among possible trajectories. The two elements of this problem considered here are: (1) determining what constitutes optimality, and (2) finding optimal trajectories. Pilots and dispatchers from major u.s. airlines were surveyed to determine which attributes of the outcome of a flight they considered the most important. Avoiding turbulence-for passenger comfort-topped the list of items which were not safety related. Pilots' decision making about the selection of flight profile on the basis of flight time, fuel burn, and exposure to turbulence was then observed. Of the several behavioral and prescriptive decision models invoked to explain the pilots' choices, utility maximization is shown to best reproduce the pilots' decisions. After considering more traditional methods for optimizing trajectories, a novel method is developed using a genetic algorithm (GA) operating on a discrete representation of the trajectory search space. The representation is a sequence of command altitudes, and was chosen to be compatible with the constraints imposed by Air Traffic Control, and with the training given to pilots. Since trajectory evaluation for the GA is performed holistically, a wide class of objective functions can be optimized easily. Also, using the GA it is possible to compare the costs associated with different airspace design and air traffic management policies. A decision aid is proposed which would combine the pilot's notion of optimality with the GA-based optimization, provide the pilot with a number of alternative pareto-optimal trajectories, and allow him to consider unmodelled attributes and constraints in choosing among them. A solution to the problem of displaying alternatives in a multi-attribute decision space is also presented.

Choosing an Advanced Therapy in Parkinson's Disease; is it an Evidence-Based Decision in Current Practice?

PubMed

Nijhuis, Frouke A P; van Heek, Jolien; Bloem, Bastiaan R; Post, Bart; Faber, Marjan J

2016-07-25

In advanced Parkinson's disease (PD), neurologists and patients face a complex decision for an advanced therapy. When choosing a treatment, the best available evidence should be combined with the professional's expertise and the patient's preferences. The objective of this study was to explore current decision-making in advanced PD. We conducted focus group discussions and individual interviews with patients (N = 20) who had received deep brain stimulation, Levodopa-Carbidopa intestinal gel, or subcutaneous apomorphine infusion, and with their caregivers (N = 16). Furthermore, we conducted semi-structured interviews with neurologists (N = 7) and PD nurse specialists (N = 3) to include the perspectives of all key players in this decision-making process. Data were analyzed by two researchers using a qualitative thematic analysis approach. Four themes representing current experiences with the decision-making process were identified: 1) information and information needs, 2) factors influencing treatment choice and individual decision strategies, 3) decision-making roles, and 4) barriers and facilitators to shared decision-making (SDM). Patient preferences were taken into account, however patients were not always provided with adequate information. The professional's expertise influenced the decision-making process in both positive and negative ways. Although professionals and patients considered SDM essential for the decision of an advanced treatment, they mentioned several barriers for the implementation in current practice. In this study we found several factors explaining why in current practice, evidence-based decision-making in advanced PD is not optimal. An important first step would be to develop objective information on all treatment options.

Individual differences in voluntary alcohol intake in rats: relationship with impulsivity, decision making and Pavlovian conditioned approach.

PubMed

Spoelder, Marcia; Flores Dourojeanni, Jacques P; de Git, Kathy C G; Baars, Annemarie M; Lesscher, Heidi M B; Vanderschuren, Louk J M J

2017-07-01

Alcohol use disorder (AUD) has been associated with suboptimal decision making, exaggerated impulsivity, and aberrant responses to reward-paired cues, but the relationship between AUD and these behaviors is incompletely understood. This study aims to assess decision making, impulsivity, and Pavlovian-conditioned approach in rats that voluntarily consume low (LD) or high (HD) amounts of alcohol. LD and HD were tested in the rat gambling task (rGT) or the delayed reward task (DRT). Next, the effect of alcohol (0-1.0 g/kg) was tested in these tasks. Pavlovian-conditioned approach (PCA) was assessed both prior to and after intermittent alcohol access (IAA). Principal component analyses were performed to identify relationships between the most important behavioral parameters. HD showed more optimal decision making in the rGT. In the DRT, HD transiently showed reduced impulsive choice. In both LD and HD, alcohol treatment increased optimal decision making in the rGT and increased impulsive choice in the DRT. PCA prior to and after IAA was comparable for LD and HD. When PCA was tested after IAA only, HD showed a more sign-tracking behavior. The principal component analyses indicated dimensional relationships between alcohol intake, impulsivity, and sign-tracking behavior in the PCA task after IAA. HD showed a more efficient performance in the rGT and DRT. Moreover, alcohol consumption enhanced approach behavior to reward-predictive cues, but sign-tracking did not predict the level of alcohol consumption. Taken together, these findings suggest that high levels of voluntary alcohol intake are associated with enhanced cue- and reward-driven behavior.

Chronic and Acute Stress Promote Overexploitation in Serial Decision Making.

PubMed

Lenow, Jennifer K; Constantino, Sara M; Daw, Nathaniel D; Phelps, Elizabeth A

2017-06-07

Many decisions that humans make resemble foraging problems in which a currently available, known option must be weighed against an unknown alternative option. In such foraging decisions, the quality of the overall environment can be used as a proxy for estimating the value of future unknown options against which current prospects are compared. We hypothesized that such foraging-like decisions would be characteristically sensitive to stress, a physiological response that tracks biologically relevant changes in environmental context. Specifically, we hypothesized that stress would lead to more exploitative foraging behavior. To test this, we investigated how acute and chronic stress, as measured by changes in cortisol in response to an acute stress manipulation and subjective scores on a questionnaire assessing recent chronic stress, relate to performance in a virtual sequential foraging task. We found that both types of stress bias human decision makers toward overexploiting current options relative to an optimal policy. These findings suggest a possible computational role of stress in decision making in which stress biases judgments of environmental quality. SIGNIFICANCE STATEMENT Many of the most biologically relevant decisions that we make are foraging-like decisions about whether to stay with a current option or search the environment for a potentially better one. In the current study, we found that both acute physiological and chronic subjective stress are associated with greater overexploitation or staying at current options for longer than is optimal. These results suggest a domain-general way in which stress might bias foraging decisions through changing one's appraisal of the overall quality of the environment. These novel findings not only have implications for understanding how this important class of foraging decisions might be biologically implemented, but also for understanding the computational role of stress in behavior and cognition more broadly. Copyright © 2017 the authors 0270-6474/17/375681-09$15.00/0.

A nonlinear bi-level programming approach for product portfolio management.

PubMed

Ma, Shuang

2016-01-01

Product portfolio management (PPM) is a critical decision-making for companies across various industries in today's competitive environment. Traditional studies on PPM problem have been motivated toward engineering feasibilities and marketing which relatively pay less attention to other competitors' actions and the competitive relations, especially in mathematical optimization domain. The key challenge lies in that how to construct a mathematical optimization model to describe this Stackelberg game-based leader-follower PPM problem and the competitive relations between them. The primary work of this paper is the representation of a decision framework and the optimization model to leverage the PPM problem of leader and follower. A nonlinear, integer bi-level programming model is developed based on the decision framework. Furthermore, a bi-level nested genetic algorithm is put forward to solve this nonlinear bi-level programming model for leader-follower PPM problem. A case study of notebook computer product portfolio optimization is reported. Results and analyses reveal that the leader-follower bi-level optimization model is robust and can empower product portfolio optimization.

An integrated strategy of knowledge application for optimal e-health implementation: A multi-method study protocol

PubMed Central

Gagnon, Marie-Pierre; Légaré, France; Fortin, Jean-Paul; Lamothe, Lise; Labrecque, Michel; Duplantie, Julie

2008-01-01

Background E-health is increasingly valued for supporting: 1) access to quality health care services for all citizens; 2) information flow and exchange; 3) integrated health care services and 4) interprofessional collaboration. Nevertheless, several questions remain on the factors allowing an optimal integration of e-health in health care policies, organisations and practices. An evidence-based integrated strategy would maximise the efficacy and efficiency of e-health implementation. However, decisions regarding e-health applications are usually not evidence-based, which can lead to a sub-optimal use of these technologies. This study aims at understanding factors influencing the application of scientific knowledge for an optimal implementation of e-health in the health care system. Methods A three-year multi-method study is being conducted in the Province of Quebec (Canada). Decision-making at each decisional level (political, organisational and clinical) are analysed based on specific approaches. At the political level, critical incidents analysis is being used. This method will identify how decisions regarding the implementation of e-health could be influenced or not by scientific knowledge. Then, interviews with key-decision-makers will look at how knowledge was actually used to support their decisions, and what factors influenced its use. At the organisational level, e-health projects are being analysed as case studies in order to explore the use of scientific knowledge to support decision-making during the implementation of the technology. Interviews with promoters, managers and clinicians will be carried out in order to identify factors influencing the production and application of scientific knowledge. At the clinical level, questionnaires are being distributed to clinicians involved in e-health projects in order to analyse factors influencing knowledge application in their decision-making. Finally, a triangulation of the results will be done using mixed methodologies to allow a transversal analysis of the results at each of the decisional levels. Results This study will identify factors influencing the use of scientific evidence and other types of knowledge by decision-makers involved in planning, financing, implementing and evaluating e-health projects. Conclusion These results will be highly relevant to inform decision-makers who wish to optimise the implementation of e-health in the Quebec health care system. This study is extremely relevant given the context of major transformations in the health care system where e-health becomes a must. PMID:18435853

Diffusion Decision Model: Current Issues and History.

PubMed

Ratcliff, Roger; Smith, Philip L; Brown, Scott D; McKoon, Gail

2016-04-01

There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in psychology. They view decision making as a process of noisy accumulation of evidence from a stimulus. The standard model assumes that evidence accumulates at a constant rate during the second or two it takes to make a decision. This process can be linked to the behaviors of populations of neurons and to theories of optimality. Diffusion models have been used successfully in a range of cognitive tasks and as psychometric tools in clinical research to examine individual differences. In this review, we relate the models to both earlier and more recent research in psychology. Copyright © 2016. Published by Elsevier Ltd.

Decision Making in Health and Medicine

NASA Astrophysics Data System (ADS)

Hunink, Myriam; Glasziou, Paul; Siegel, Joanna; Weeks, Jane; Pliskin, Joseph; Elstein, Arthur; Weinstein, Milton C.

2001-11-01

Decision making in health care means navigating through a complex and tangled web of diagnostic and therapeutic uncertainties, patient preferences and values, and costs. In addition, medical therapies may include side effects, surgery may lead to undesirable complications, and diagnostic technologies may produce inconclusive results. In many clinical and health policy decisions it is necessary to counterbalance benefits and risks, and to trade off competing objectives such as maximizing life expectancy vs optimizing quality of life vs minimizing the required resources. This textbook plots a clear course through these complex and conflicting variables. It clearly explains and illustrates tools for integrating quantitative evidence-based data and subjective outcome values in making clinical and health policy decisions. An accompanying CD-ROM features solutions to the exercises, PowerPoint® presentations of the illustrations, and sample models and tables.

Facts and fiction of learning systems. [decision making intelligent control

NASA Technical Reports Server (NTRS)

Saridis, G. N.

1975-01-01

The methodology that will provide the updated precision for the hardware control and the advanced decision making and planning in the software control is called learning systems and intelligent control. It was developed theoretically as an alternative for the nonsystematic heuristic approaches of artificial intelligence experiments and the inflexible formulation of modern optimal control methods. Its basic concepts are discussed and some feasibility studies of some practical applications are presented.

Game theory and risk-based leveed river system planning with noncooperation

NASA Astrophysics Data System (ADS)

Hui, Rui; Lund, Jay R.; Madani, Kaveh

2016-01-01

Optimal risk-based levee designs are usually developed for economic efficiency. However, in river systems with multiple levees, the planning and maintenance of different levees are controlled by different agencies or groups. For example, along many rivers, levees on opposite riverbanks constitute a simple leveed river system with each levee designed and controlled separately. Collaborative planning of the two levees can be economically optimal for the whole system. Independent and self-interested landholders on opposite riversides often are willing to separately determine their individual optimal levee plans, resulting in a less efficient leveed river system from an overall society-wide perspective (the tragedy of commons). We apply game theory to simple leveed river system planning where landholders on each riverside independently determine their optimal risk-based levee plans. Outcomes from noncooperative games are analyzed and compared with the overall economically optimal outcome, which minimizes net flood cost system-wide. The system-wide economically optimal solution generally transfers residual flood risk to the lower-valued side of the river, but is often impractical without compensating for flood risk transfer to improve outcomes for all individuals involved. Such compensation can be determined and implemented with landholders' agreements on collaboration to develop an economically optimal plan. By examining iterative multiple-shot noncooperative games with reversible and irreversible decisions, the costs of myopia for the future in making levee planning decisions show the significance of considering the externalities and evolution path of dynamic water resource problems to improve decision-making.

Structured decision making as a conceptual framework to identify thresholds for conservation and management

USGS Publications Warehouse

Martin, J.; Runge, M.C.; Nichols, J.D.; Lubow, B.C.; Kendall, W.L.

2009-01-01

Thresholds and their relevance to conservation have become a major topic of discussion in the ecological literature. Unfortunately, in many cases the lack of a clear conceptual framework for thinking about thresholds may have led to confusion in attempts to apply the concept of thresholds to conservation decisions. Here, we advocate a framework for thinking about thresholds in terms of a structured decision making process. The purpose of this framework is to promote a logical and transparent process for making informed decisions for conservation. Specification of such a framework leads naturally to consideration of definitions and roles of different kinds of thresholds in the process. We distinguish among three categories of thresholds. Ecological thresholds are values of system state variables at which small changes bring about substantial changes in system dynamics. Utility thresholds are components of management objectives (determined by human values) and are values of state or performance variables at which small changes yield substantial changes in the value of the management outcome. Decision thresholds are values of system state variables at which small changes prompt changes in management actions in order to reach specified management objectives. The approach that we present focuses directly on the objectives of management, with an aim to providing decisions that are optimal with respect to those objectives. This approach clearly distinguishes the components of the decision process that are inherently subjective (management objectives, potential management actions) from those that are more objective (system models, estimates of system state). Optimization based on these components then leads to decision matrices specifying optimal actions to be taken at various values of system state variables. Values of state variables separating different actions in such matrices are viewed as decision thresholds. Utility thresholds are included in the objectives component, and ecological thresholds may be embedded in models projecting consequences of management actions. Decision thresholds are determined by the above-listed components of a structured decision process. These components may themselves vary over time, inducing variation in the decision thresholds inherited from them. These dynamic decision thresholds can then be determined using adaptive management. We provide numerical examples (that are based on patch occupancy models) of structured decision processes that include all three kinds of thresholds. ?? 2009 by the Ecological Society of America.

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

NASA Technical Reports Server (NTRS)

Kirlik, Alex

1993-01-01

Summaries of the four projects completed during the performance of this research are included. The four projects described are: Perceptual Augmentation Aiding for Situation Assessment, Perceptual Augmentation Aiding for Dynamic Decision-Making and Control, Action Advisory Aiding for Dynamic Decision-Making and Control, and Display Design to Support Time-Constrained Route Optimization. Papers based on each of these projects are currently in preparation. The theoretical framework upon which the first three projects are based, Ecological Task Analysis, was also developed during the performance of this research, and is described in a previous report. A project concerned with modeling strategies in human control of a dynamic system was also completed during the performance of this research.

A delicate subject: The impact of cultural factors on neonatal and perinatal decision making.

PubMed

Van McCrary, S; Green, H C; Combs, A; Mintzer, J P; Quirk, J G

2014-01-01

The neonatal intensive care unit (NICU) is a high-stress environment for both families and health care providers that can sometimes make appropriate medical decisions challenging. We present a review article of non-medical barriers to effective decision making in the NICU, including: miscommunication, mixed messages, denial, comparative social and cultural influences, and the possible influence of perceived legal issues and family reliance on information from the Internet. As examples of these barriers, we describe and discuss two cases that occurred simultaneously in the same NICU where decisions were influenced by social and cultural differences that were misunderstood by both medical staff and patients' families. The resulting stress and emotional discomfort created an environment with sub-optimal relationships between patients' families and health care providers. We provide background on the sources of conflict in these particular cases. We also offer suggestions for possible amelioration of similar conflicts with the twin goals of facilitating compassionate decision making in NICU settings and promoting enhanced well-being of both families and providers.

Bi-Level Decision Making for Supporting Energy and Water Nexus

NASA Astrophysics Data System (ADS)

Zhang, X.; Vesselinov, V. V.

2016-12-01

The inseparable relationship between energy production and water resources has led to the emerging energy-water nexus concept, which provides a means for integrated management and decision making of these two critical resources. However, the energy-water nexus frequently involves decision makers with different and competing management objectives. Furthermore, there is a challenge that decision makers and stakeholders might be making decisions sequentially from a higher level to a lower level, instead of at the same decision level, whereby the objective of a decision maker at a higher level should be satisfied first. In this study, a bi-level decision model is advanced to handle such decision-making situations for managing the energy-water nexus. The work represents a unique contribution to developing an integrated decision-support framework/tool to quantify and analyze the tradeoffs between the two-level energy-water nexus decision makers. Here, plans for electricity generation, fuel supply, water supply, capacity expansion of the power plants and environmental impacts are optimized to provide effective decision support. The developed decision-support framework is implemented in Julia (a high-level, high-performance dynamic programming language for technical computing) and is a part of the MADS (Model Analyses & Decision Support) framework (http://mads.lanl.gov). To demonstrate the capabilities of the developed methodology, a series of analyses are performed for synthetic problems consistent with actual real-world energy-water nexus management problems.

Value-based attentional capture influences context-dependent decision-making

PubMed Central

Cha, Kexin; Rangsipat, Napat; Serences, John T.

2015-01-01

Normative theories posit that value-based decision-making is context independent. However, decisions between two high-value options can be suboptimally biased by the introduction of a third low-value option. This context-dependent modulation is consistent with the divisive normalization of the value of each stimulus by the total value of all stimuli. In addition, an independent line of research demonstrates that pairing a stimulus with a high-value outcome can lead to attentional capture that can mediate the efficiency of visual information processing. Here we tested the hypothesis that value-based attentional capture interacts with value-based normalization to influence the optimality of decision-making. We used a binary-choice paradigm in which observers selected between two targets and the color of each target indicated the magnitude of their reward potential. Observers also had to simultaneously ignore a task-irrelevant distractor rendered in a color that was previously associated with a specific reward magnitude. When the color of the task-irrelevant distractor was previously associated with a high reward, observers responded more slowly and less optimally. Moreover, as the learned value of the distractor increased, electrophysiological data revealed an attenuation of the lateralized N1 and N2Pc responses evoked by the relevant choice stimuli and an attenuation of the late positive deflection (LPD). Collectively, these behavioral and electrophysiological data suggest that value-based attentional capture and value-based normalization jointly mediate the influence of context on free-choice decision-making. PMID:25995350

Value-based attentional capture influences context-dependent decision-making.

PubMed

Itthipuripat, Sirawaj; Cha, Kexin; Rangsipat, Napat; Serences, John T

2015-07-01

Normative theories posit that value-based decision-making is context independent. However, decisions between two high-value options can be suboptimally biased by the introduction of a third low-value option. This context-dependent modulation is consistent with the divisive normalization of the value of each stimulus by the total value of all stimuli. In addition, an independent line of research demonstrates that pairing a stimulus with a high-value outcome can lead to attentional capture that can mediate the efficiency of visual information processing. Here we tested the hypothesis that value-based attentional capture interacts with value-based normalization to influence the optimality of decision-making. We used a binary-choice paradigm in which observers selected between two targets and the color of each target indicated the magnitude of their reward potential. Observers also had to simultaneously ignore a task-irrelevant distractor rendered in a color that was previously associated with a specific reward magnitude. When the color of the task-irrelevant distractor was previously associated with a high reward, observers responded more slowly and less optimally. Moreover, as the learned value of the distractor increased, electrophysiological data revealed an attenuation of the lateralized N1 and N2Pc responses evoked by the relevant choice stimuli and an attenuation of the late positive deflection (LPD). Collectively, these behavioral and electrophysiological data suggest that value-based attentional capture and value-based normalization jointly mediate the influence of context on free-choice decision-making. Copyright © 2015 the American Physiological Society.

Structured decision making for conservation of bull trout (Salvelinus confluentus) in Long Creek, Klamath River Basin, south-central Oregon

USGS Publications Warehouse

Benjamin, Joseph R.; McDonnell, Kevin; Dunham, Jason B.; Brignon, William R.; Peterson, James T.

2017-06-21

With the decline of bull trout (Salvelinus confluentus), managers face multiple, and sometimes contradictory, management alternatives for species recovery. Moreover, effective decision-making involves all stakeholders influenced by the decisions (such as Tribal, State, Federal, private, and non-governmental organizations) because they represent diverse objectives, jurisdictions, policy mandates, and opinions of the best management strategy. The process of structured decision making is explicitly designed to address these elements of the decision making process. Here we report on an application of structured decision making to a population of bull trout believed threatened by high densities of nonnative brook trout (S. fontinalis) and habitat fragmentation in Long Creek, a tributary to the Sycan River in the Klamath River Basin, south-central Oregon. This involved engaging stakeholders to identify (1) their fundamental objectives for the conservation of bull trout, (2) feasible management alternatives to achieve their objectives, and (3) biological information and assumptions to incorporate in a decision model. Model simulations suggested an overarching theme among the top decision alternatives, which was a need to simultaneously control brook trout and ensure that the migratory tactic of bull trout can be expressed. More specifically, the optimal management decision, based on the estimated adult abundance at year 10, was to combine the eradication of brook trout from Long Creek with improvement of downstream conditions (for example, connectivity or habitat conditions). Other top decisions included these actions independently, as well as electrofishing removal of brook trout. In contrast, translocating bull trout to a different stream or installing a barrier to prevent upstream spread of brook trout had minimal or negative effects on the bull trout population. Moreover, sensitivity analyses suggested that these actions were consistently identified as optimal across a large range of parameter values. Taken together, these results support the conclusion that management actions focused on controlling brook trout and enhancing migrant bull trout are more likely to yield more adult bull trout within the 10-year time frame specified by stakeholders.

An Integrated DEMATEL-VIKOR Method-Based Approach for Cotton Fibre Selection and Evaluation

NASA Astrophysics Data System (ADS)

Chakraborty, Shankar; Chatterjee, Prasenjit; Prasad, Kanika

2018-01-01

Selection of the most appropriate cotton fibre type for yarn manufacturing is often treated as a multi-criteria decision-making (MCDM) problem as the optimal selection decision needs to be taken in presence of several conflicting fibre properties. In this paper, two popular MCDM methods in the form of decision making trial and evaluation laboratory (DEMATEL) and VIse Kriterijumska Optimizacija kompromisno Resenje (VIKOR) are integrated to aid the cotton fibre selection decision. DEMATEL method addresses the interrelationships between various physical properties of cotton fibres while segregating them into cause and effect groups, whereas, VIKOR method helps in ranking all the considered 17 cotton fibres from the best to the worst. The derived ranking of cotton fibre alternatives closely matches with that obtained by the past researchers. This model can assist the spinning industry personnel in the blending process while making accurate fibre selection decision when cotton fibre properties are numerous and interrelated.

An Integrated DEMATEL-VIKOR Method-Based Approach for Cotton Fibre Selection and Evaluation

NASA Astrophysics Data System (ADS)

Chakraborty, Shankar; Chatterjee, Prasenjit; Prasad, Kanika

2018-06-01

Selection of the most appropriate cotton fibre type for yarn manufacturing is often treated as a multi-criteria decision-making (MCDM) problem as the optimal selection decision needs to be taken in presence of several conflicting fibre properties. In this paper, two popular MCDM methods in the form of decision making trial and evaluation laboratory (DEMATEL) and VIse Kriterijumska Optimizacija kompromisno Resenje (VIKOR) are integrated to aid the cotton fibre selection decision. DEMATEL method addresses the interrelationships between various physical properties of cotton fibres while segregating them into cause and effect groups, whereas, VIKOR method helps in ranking all the considered 17 cotton fibres from the best to the worst. The derived ranking of cotton fibre alternatives closely matches with that obtained by the past researchers. This model can assist the spinning industry personnel in the blending process while making accurate fibre selection decision when cotton fibre properties are numerous and interrelated.

Linking Effective Project Management to Business Strategy in Oil and Gas Industry through Decision-making Processes

NASA Astrophysics Data System (ADS)

Adeleke, Adeyinka

The construction project in the oil and gas industry covers the entire spectrum of hydrocarbon production from the wellhead (upstream) to downstream facilities. In each of these establishments, the activities in a construction project include: consulting, studies, front-end engineering, detail engineering, procurement, program management, construction, installation, commissioning and start-up. Efficient management of each of the activities involved in construction projects is one of the driving forces for the successful completion of the project. Optimizing the crucial factors in project management during each phase of a project in an oil and gas industry can assist managers to maximize the use of available resources and drive the project to successful conclusions. One of these factors is the decision-making process in the construction project. Current research effort investigated the relationship between decision-making processes and business strategy in oil and gas industry using employee surveys. I recruited employees of different races, age group, genders, and years of experience in order understand their influence on the implementation of the decision-making process in oil and gas industry through a quantitative survey. Decision-making was assessed using five decision measures: (a) rational, (b) intuitive, (c) dependent, (d) avoidant, and (e) spontaneous. The findings indicated gender, age, years of work experience and job titles as primary variables with a negative relationship with decision-making approach for employees working in a major oil and gas industry. The study results revealed that the two most likely decision-making methods in oil and gas industry include: making a decision in a logical and systematic way and seek assistance from others when making a decision. Additionally, the two leading management approaches to decision-making in the oil and gas industry include: decision analysis is part of organization culture and management is committed to the decision-making process. Some recommendations for future studies were presented based on the need to intensify the importance of the current study and enlarge the body of knowledge regarding decision-making process in oil and gas industry.

Flexible modulation of risk attitude during decision-making under quota.

PubMed

Fujimoto, Atsushi; Takahashi, Hidehiko

2016-10-01

Risk attitude is often regarded as an intrinsic parameter in the individual personality. However, ethological studies reported state-dependent strategy optimization irrespective of individual preference. To synthesize the two contrasting literatures, we developed a novel gambling task that dynamically manipulated the quota severity (required outcome to clear the task) in a course of choice trials and conducted a task-fMRI study in human participants. The participants showed their individual risk preference when they had no quota constraint ('individual-preference mode'), while they adopted state-dependent optimal strategy when they needed to achieve a quota ('strategy-optimization mode'). fMRI analyses illustrated that the interplay among prefrontal areas and salience-network areas reflected the quota severity and the utilization of the optimal strategy, shedding light on the neural substrates of the quota-dependent risk attitude. Our results demonstrated the complex nature of risk-sensitive decision-making and may provide a new perspective for the understanding of problematic risky behaviors in human. Copyright © 2016 Elsevier Inc. All rights reserved.

A normative inference approach for optimal sample sizes in decisions from experience

PubMed Central

Ostwald, Dirk; Starke, Ludger; Hertwig, Ralph

2015-01-01

“Decisions from experience” (DFE) refers to a body of work that emerged in research on behavioral decision making over the last decade. One of the major experimental paradigms employed to study experience-based choice is the “sampling paradigm,” which serves as a model of decision making under limited knowledge about the statistical structure of the world. In this paradigm respondents are presented with two payoff distributions, which, in contrast to standard approaches in behavioral economics, are specified not in terms of explicit outcome-probability information, but by the opportunity to sample outcomes from each distribution without economic consequences. Participants are encouraged to explore the distributions until they feel confident enough to decide from which they would prefer to draw from in a final trial involving real monetary payoffs. One commonly employed measure to characterize the behavior of participants in the sampling paradigm is the sample size, that is, the number of outcome draws which participants choose to obtain from each distribution prior to terminating sampling. A natural question that arises in this context concerns the “optimal” sample size, which could be used as a normative benchmark to evaluate human sampling behavior in DFE. In this theoretical study, we relate the DFE sampling paradigm to the classical statistical decision theoretic literature and, under a probabilistic inference assumption, evaluate optimal sample sizes for DFE. In our treatment we go beyond analytically established results by showing how the classical statistical decision theoretic framework can be used to derive optimal sample sizes under arbitrary, but numerically evaluable, constraints. Finally, we critically evaluate the value of deriving optimal sample sizes under this framework as testable predictions for the experimental study of sampling behavior in DFE. PMID:26441720

Motor planning under temporal uncertainty is suboptimal when the gain function is asymmetric

PubMed Central

Ota, Keiji; Shinya, Masahiro; Kudo, Kazutoshi

2015-01-01

For optimal action planning, the gain/loss associated with actions and the variability in motor output should both be considered. A number of studies make conflicting claims about the optimality of human action planning but cannot be reconciled due to their use of different movements and gain/loss functions. The disagreement is possibly because of differences in the experimental design and differences in the energetic cost of participant motor effort. We used a coincident timing task, which requires decision making with constant energetic cost, to test the optimality of participant's timing strategies under four configurations of the gain function. We compared participant strategies to an optimal timing strategy calculated from a Bayesian model that maximizes the expected gain. We found suboptimal timing strategies under two configurations of the gain function characterized by asymmetry, in which higher gain is associated with higher risk of zero gain. Participants showed a risk-seeking strategy by responding closer than optimal to the time of onset/offset of zero gain. Meanwhile, there was good agreement of the model with actual performance under two configurations of the gain function characterized by symmetry. Our findings show that human ability to make decisions that must reflect uncertainty in one's own motor output has limits that depend on the configuration of the gain function. PMID:26236227

How do persons with dementia participate in decision making related to health and daily care? A multi-case study

PubMed Central

2012-01-01

Background Many countries have passed laws giving patients the right to participate in decisions about health care. People with dementia cannot be assumed to be incapable of making decisions on their diagnosis alone as they may have retained cognitive abilities. The purpose of this study was to gain a better understanding of how persons with dementia participated in making decisions about health care and how their family carers and professional caregivers influenced decision making. Methods This Norwegian study had a qualitative multi-case design. The triad in each of the ten cases consisted of the person with dementia, the family carer and the professional caregiver, in all 30 participants. Inclusion criteria for the persons with dementia were: (1) 67 years or older (2) diagnosed with dementia (3) Clinical Dementia Rating score 2, moderate dementia; (3) able to communicate verbally. The family carers and professional caregivers were then asked to participate. A semi-structured interview guide was used in interviews with family carers and professional caregivers. Field notes were written after participant observation of interactions between persons with dementia and professional caregivers during morning care or activities at a day centre. How the professional caregivers facilitated decision making was the focus of the observations that varied in length from 30 to 90 minutes. The data were analyzed using framework analysis combined with a hermeneutical interpretive approach. Results Professional caregivers based their assessment of mental competence on experience and not on standardized tests. Persons with dementia demonstrated variability in how they participated in decision making. Pseudo-autonomous decision making and delegating decision making were new categories that emerged. Autonomous decision making did occur but shared decision making was the most typical pattern. Reduced mental capacity, lack of available choices or not being given the opportunity to participate led to non-involvement. Not all decisions were based on logic; personal values and relationships were also considered. Conclusions Persons with moderate dementia demonstrated variability in how they participated in decision making. Optimal involvement was facilitated by positioning them as capable of influencing decisions, assessing decision-specific competence, clarifying values and understanding the significance of relationships and context. PMID:22870952

Multi-alternative decision-making with non-stationary inputs.

PubMed

Nunes, Luana F; Gurney, Kevin

2016-08-01

One of the most widely implemented models for multi-alternative decision-making is the multihypothesis sequential probability ratio test (MSPRT). It is asymptotically optimal, straightforward to implement, and has found application in modelling biological decision-making. However, the MSPRT is limited in application to discrete ('trial-based'), non-time-varying scenarios. By contrast, real world situations will be continuous and entail stimulus non-stationarity. In these circumstances, decision-making mechanisms (like the MSPRT) which work by accumulating evidence, must be able to discard outdated evidence which becomes progressively irrelevant. To address this issue, we introduce a new decision mechanism by augmenting the MSPRT with a rectangular integration window and a transparent decision boundary. This allows selection and de-selection of options as their evidence changes dynamically. Performance was enhanced by adapting the window size to problem difficulty. Further, we present an alternative windowing method which exponentially decays evidence and does not significantly degrade performance, while greatly reducing the memory resources necessary. The methods presented have proven successful at allowing for the MSPRT algorithm to function in a non-stationary environment.

Doing our best: optimization and the management of risk.

PubMed

Ben-Haim, Yakov

2012-08-01

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

Processing Technology Selection for Municipal Sewage Treatment Based on a Multi-Objective Decision Model under Uncertainty.

PubMed

Chen, Xudong; Xu, Zhongwen; Yao, Liming; Ma, Ning

2018-03-05

This study considers the two factors of environmental protection and economic benefits to address municipal sewage treatment. Based on considerations regarding the sewage treatment plant construction site, processing technology, capital investment, operation costs, water pollutant emissions, water quality and other indicators, we establish a general multi-objective decision model for optimizing municipal sewage treatment plant construction. Using the construction of a sewage treatment plant in a suburb of Chengdu as an example, this paper tests the general model of multi-objective decision-making for the sewage treatment plant construction by implementing a genetic algorithm. The results show the applicability and effectiveness of the multi-objective decision model for the sewage treatment plant. This paper provides decision and technical support for the optimization of municipal sewage treatment.

Generalized railway tank car safety design optimization for hazardous materials transport: addressing the trade-off between transportation efficiency and safety.

PubMed

Saat, Mohd Rapik; Barkan, Christopher P L

2011-05-15

North America railways offer safe and generally the most economical means of long distance transport of hazardous materials. Nevertheless, in the event of a train accident releases of these materials can pose substantial risk to human health, property or the environment. The majority of railway shipments of hazardous materials are in tank cars. Improving the safety design of these cars to make them more robust in accidents generally increases their weight thereby reducing their capacity and consequent transportation efficiency. This paper presents a generalized tank car safety design optimization model that addresses this tradeoff. The optimization model enables evaluation of each element of tank car safety design, independently and in combination with one another. We present the optimization model by identifying a set of Pareto-optimal solutions for a baseline tank car design in a bicriteria decision problem. This model provides a quantitative framework for a rational decision-making process involving tank car safety design enhancements to reduce the risk of transporting hazardous materials. Copyright © 2011 Elsevier B.V. All rights reserved.

A model of interaction between anticorruption authority and corruption groups

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

Neverova, Elena G.; Malafeyef, Oleg A.

The paper provides a model of interaction between anticorruption unit and corruption groups. The main policy functions of the anticorruption unit involve reducing corrupt practices in some entities through an optimal approach to resource allocation and effective anticorruption policy. We develop a model based on Markov decision-making process and use Howard’s policy-improvement algorithm for solving an optimal decision strategy. We examine the assumption that corruption groups retaliate against the anticorruption authority to protect themselves. This model was implemented through stochastic game.

Use of Classification Agreement Analyses to Evaluate RTI Implementation

ERIC Educational Resources Information Center

VanDerHeyden, Amanda

2010-01-01

RTI as a framework for decision making has implications for the diagnosis of specific learning disabilities. Any diagnostic tool must meet certain standards to demonstrate that its use leads to predictable decisions with minimal risk. Classification agreement analyses are described as optimal for demonstrating the technical adequacy of RTI…

Working-memory load and temporal myopia in dynamic decision making.

PubMed

Worthy, Darrell A; Otto, A Ross; Maddox, W Todd

2012-11-01

We examined the role of working memory (WM) in dynamic decision making by having participants perform decision-making tasks under single-task or dual-task conditions. In 2 experiments participants performed dynamic decision-making tasks in which they chose 1 of 2 options on each trial. The decreasing option always gave a larger immediate reward but caused future rewards for both options to decrease. The increasing option always gave a smaller immediate reward but caused future rewards for both options to increase. In each experiment we manipulated the reward structure such that the decreasing option was the optimal choice in 1 condition and the increasing option was the optimal choice in the other condition. Behavioral results indicated that dual-task participants selected the immediately rewarding decreasing option more often, and single-task participants selected the increasing option more often, regardless of which option was optimal. Thus, dual-task participants performed worse on 1 type of task but better on the other type. Modeling results showed that single-task participants' data were most often best fit by a win-stay, lose-shift (WSLS) rule-based model that tracked differences across trials, and dual-task participants' data were most often best fit by a Softmax reinforcement learning model that tracked recency-weighted average rewards for each option. This suggests that manipulating WM load affects the degree to which participants focus on the immediate versus delayed consequences of their actions and whether they employ a rule-based WSLS strategy, but it does not necessarily affect how well people weigh the immediate versus delayed benefits when determining the long-term utility of each option.

Maximizing gait and balance: behaviors and decision-making processes of persons with multiple sclerosis and physical therapists.

PubMed

Held Bradford, Elissa; Finlayson, Marcia; White Gorman, Andrea; Wagner, Joanne

2018-05-01

To describe the behavioral decisions used by persons with multiple sclerosis (MS) and physical therapists to maximize gait and balance following outpatient physical therapy. A multi-method case series with seven matched pairs (persons with MS-physical therapists). Quota sampling maximized variability among persons with MS (disease steps score range 3-6). Three of the four physical therapists were MS or neurology certified. Persons with MS completed a phone survey, follow-up interview, and standardized questionnaires. Physical therapists completed an interview. Data were collected 2-8 weeks following discharge. Content and constant comparison analyses were used for thematic development and triangulation. Core themes arose exemplifying the decision-making processes and actions of persons with MS (challenging self by pushing but respecting limits) and physical therapists (finding the right fit). One overarching theme, keeping their lived world large, or participation in valued life roles, emerged integrating both perspectives driving decision-making. Participants have a shared goal of maximizing gait and balance so persons with MS can participate in valued life roles. Understanding the differences in the behavioral decisions and optimizing skill sets in shared decision-making and self-management may enhance the therapeutic partnership and engagement in gait- and balance-enhancing behaviors. Implications for Rehabilitation Persons with MS and physical therapists have a shared goal of maximizing gait and balance so persons with MS can participate in valued activities and life roles, or more poetically, keep their lived world large. Knowledge that persons with MS aim to challenge themselves by pushing but respecting limits can provide physical therapists with greater insight in helping persons with MS resolve uncertainty, set meaningful goals, and build the routines and resilience needed for engagement in gait- and balance-enhancing behaviors. Enriching skill sets in shared decision-making, behavior change and self-management may optimize the physical therapist toolbox.

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

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

Dynamical foundations of the neural circuit for bayesian decision making.

PubMed

Morita, Kenji

2009-07-01

On the basis of accumulating behavioral and neural evidences, it has recently been proposed that the brain neural circuits of humans and animals are equipped with several specific properties, which ensure that perceptual decision making implemented by the circuits can be nearly optimal in terms of Bayesian inference. Here, I introduce the basic ideas of such a proposal and discuss its implications from the standpoint of biophysical modeling developed in the framework of dynamical systems.

Uncertainty quantification and optimal decisions

PubMed Central

2017-01-01

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

A qualitative metasynthesis: family involvement in decision making for people with dementia in residential aged care.

PubMed

Petriwskyj, Andrea; Gibson, Alexandra; Parker, Deborah; Banks, Susan; Andrews, Sharon; Robinson, Andrew

2014-06-01

Involving people in decisions about their care is good practice and ensures optimal outcomes. Despite considerable research, in practice family involvement in decision making can be challenging for both care staff and families. The aim of this review was to identify and appraise existing knowledge about family involvement in decision making for people with dementia living in residential aged care. The present Joanna Briggs Institute meta-synthesis considered studies that investigate involvement of family members in decision making for people with dementia in residential aged care settings. While quantitative and qualitative studies were included in the review, this article presents the qualitative findings. A comprehensive search of studies was conducted in 15 electronic databases. The search was limited to papers published in English, from 1990 to 2013. Twenty-six studies were identified as relevant for this review; 16 were qualitative papers reporting on 15 studies. Two independent reviewers assessed the studies for methodological validity and extracted the data using the standardized Joanna Briggs Institute Qualitative Assessment and Review Instrument (JBI-QARI). The findings were synthesized using JBI-QARI. The findings related to the decisions encountered and made by family surrogates, family perceptions of, and preferences for, their role/s, factors regarding treatment decisions and the collaborative decision-making process, and outcomes for family decision makers. Results indicate varied and complex experiences and multiple factors influencing decision making. Communication and contacts between staff and families and the support available for families should be addressed, as well as the role of different stakeholders in decisions.

Reconsidering the Gatekeeper Paradigm for Percutaneous Coronary Intervention in Stable Coronary Disease Management.

PubMed

Schulman-Marcus, Joshua; Weintraub, William S; Boden, William E

2017-10-15

Major randomized clinical trials over the last decade support the role of optimal medical therapy for the initial management approach for patients with stable coronary artery disease (CAD), whereas percutaneous coronary intervention (PCI) ought to be reserved for patients with persistent symptoms despite optimal medical therapy. Likewise, several studies have continued to demonstrate the superiority of coronary artery bypass grafting surgery over PCI in many patients with extensive multivessel CAD, especially those with diabetes. Nevertheless, the decision-making paradigm for patients with stable CAD often continues to propagate the upfront use of "ad hoc PCI" and disadvantages alternative therapeutic approaches. In our editorial, we discuss how multiple systemic and interpersonal factors continue to favor early revascularization with PCI in stable patients. We discuss whether the interventional cardiologist can be an unbiased "gatekeeper" for the use of PCI or whether other physicians should also be involved with the patient in decision-making. Finally, we offer suggestions that can redefine the gatekeeper role to facilitate an evidence-based approach that embraces shared decision-making. Copyright © 2017 Elsevier Inc. All rights reserved.

Informed multi-objective decision-making in environmental management using Pareto optimality

Treesearch

Maureen C. Kennedy; E. David Ford; Peter Singleton; Mark Finney; James K. Agee

2008-01-01

Effective decisionmaking in environmental management requires the consideration of multiple objectives that may conflict. Common optimization methods use weights on the multiple objectives to aggregate them into a single value, neglecting valuable insight into the relationships among the objectives in the management problem.

Decision-Aiding and Optimization for Vertical Navigation of Long-Haul Aircraft

NASA Technical Reports Server (NTRS)

Patrick, Nicholas J. M.; Sheridan, Thomas B.

1996-01-01

Most decisions made in the cockpit are related to safety, and have therefore been proceduralized in order to reduce risk. There are very few which are made on the basis of a value metric such as economic cost. One which can be shown to be value based, however, is the selection of a flight profile. Fuel consumption and flight time both have a substantial effect on aircraft operating cost, but they cannot be minimized simultaneously. In addition, winds, turbulence, and performance x,ary widely with altitude and time. These factors make it important and difficult for pilots to (a) evaluate the outcomes associated with a particular trajectory before it is flown and (b) decide among possible trajectories. The two elements of this problem considered here are (1) determining, what constitutes optimality, and (2) finding optimal trajectories. Pilots and dispatchers from major U.S. airlines were surveyed to determine which attributes of the outcome of a flight they considered the most important. Avoiding turbulence-for passenger comfort topped the list of items which were not safety related. Pilots' decision making about the selection of flight profile on the basis of flight time, fuel burn, and exposure to turbulence was then observed. Of the several behavioral and prescriptive decision models invoked to explain the pilots' choices, utility maximization is shown to best reproduce the pilots' decisions. After considering more traditional methods for optimizing trajectories, a novel method is developed using a genetic algorithm (GA) operating on a discrete representation of the trajectory search space. The representation is a sequence of command altitudes, and was chosen to be compatible with the constraints imposed by Air Traffic Control, and with the training given to pilots. Since trajectory evaluation for the GA is performed holistically, a wide class of objective functions can be optimized easily. Also, using the GA it is possible to compare the costs associated with different airspace design and air traffic management policies. A decision aid is proposed which would combine the pilot's notion of optimility with the GA-based optimization, provide the pilot with a number of alternative pareto-optimal trajectories, and allow him to consider un-modelled attributes and constraints in choosing among them. A solution to the problem of displaying alternatives in a multi-attribute decision space is also presented.

Optimized model tuning in medical systems.

PubMed

Kléma, Jirí; Kubalík, Jirí; Lhotská, Lenka

2005-12-01

In medical systems it is often advantageous to utilize specific problem situations (cases) in addition to or instead of a general model. Decisions are then based on relevant past cases retrieved from a case memory. The reliability of such decisions depends directly on the ability to identify cases of practical relevance to the current situation. This paper discusses issues of automated tuning in order to obtain a proper definition of mutual case similarity in a specific medical domain. The main focus is on a reasonably time-consuming optimization of the parameters that determine case retrieval and further utilization in decision making/ prediction. The two case studies - mortality prediction after cardiological intervention, and resource allocation at a spa - document that the optimization process is influenced by various characteristics of the problem domain.

Project evaluation and selection using fuzzy Delphi method and zero - one goal programming

NASA Astrophysics Data System (ADS)

Alias, Suriana; Adna, Nofarziah; Arsad, Roslah; Soid, Siti Khuzaimah; Ali, Zaileha Md

2014-12-01

Project evaluation and selection is a factor affecting the impotence of board director in which is trying to maximize all the possible goals. Assessment of the problem occurred in organization plan is the first phase for decision making process. The company needs a group of expert to evaluate the problems. The Fuzzy Delphi Method (FDM) is a systematic procedure to evoke the group's opinion in order to get the best result to evaluate the project performance. This paper proposes an evaluation and selection of the best alternative project based on combination of FDM and Zero - One Goal Programming (ZOGP) formulation. ZOGP is used to solve the multi-criteria decision making for final decision part by using optimization software LINDO 6.1. An empirical example on an ongoing decision making project in Johor, Malaysia is implemented for case study.

Developing an Advanced Environment for Collaborative Computing

NASA Technical Reports Server (NTRS)

Becerra-Fernandez, Irma; Stewart, Helen; DelAlto, Martha; DelAlto, Martha; Knight, Chris

1999-01-01

Knowledge management in general tries to organize and make available important know-how, whenever and where ever is needed. Today, organizations rely on decision-makers to produce "mission critical" decisions that am based on inputs from multiple domains. The ideal decision-maker has a profound understanding of specific domains that influence the decision-making process coupled with the experience that allows them to act quickly and decisively on the information. In addition, learning companies benefit by not repeating costly mistakes, and by reducing time-to-market in Research & Development projects. Group-decision making tools can help companies make better decisions by capturing the knowledge from groups of experts. Furthermore, companies that capture their customers preferences can improve their customer service, which translates to larger profits. Therefore collaborative computing provides a common communication space, improves sharing of knowledge, provides a mechanism for real-time feedback on the tasks being performed, helps to optimize processes, and results in a centralized knowledge warehouse. This paper presents the research directions. of a project which seeks to augment an advanced collaborative web-based environment called Postdoc, with workflow capabilities. Postdoc is a "government-off-the-shelf" document management software developed at NASA-Ames Research Center (ARC).

Mechanisms Underlying Decision-Making as Revealed by Deep-Brain Stimulation in Patients with Parkinson's Disease.

PubMed

Herz, Damian M; Little, Simon; Pedrosa, David J; Tinkhauser, Gerd; Cheeran, Binith; Foltynie, Tom; Bogacz, Rafal; Brown, Peter

2018-04-23

To optimally balance opposing demands of speed and accuracy during decision-making, we must flexibly adapt how much evidence we require before making a choice. Such adjustments in decision thresholds have been linked to the subthalamic nucleus (STN), and therapeutic STN deep-brain stimulation (DBS) has been shown to interfere with this function. Here, we performed continuous as well as closed-loop DBS of the STN while Parkinson's disease patients performed a perceptual decision-making task. Closed-loop STN DBS allowed temporally patterned STN stimulation and simultaneous recordings of STN activity. This revealed that DBS only affected patients' ability to adjust decision thresholds if applied in a specific temporally confined time window during deliberation. Only stimulation in that window diminished the normal slowing of response times that occurred on difficult trials when DBS was turned off. Furthermore, DBS eliminated a relative, time-specific increase in STN beta oscillations and compromised its functional relationship with trial-by-trial adjustments in decision thresholds. Together, these results provide causal evidence that the STN is involved in adjusting decision thresholds in distinct, time-limited processing windows during deliberation. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Optimizing in a complex world: A statistician's role in decision making

DOE PAGES

Anderson-Cook, Christine M.

2016-08-09

As applied statisticians increasingly participate as active members of problem-solving and decision-making teams, our role continues to evolve. Historically, we may have been seen as those who can help with data collection strategies or answer a specific question from a set of data. Nowadays, we are or strive to be more deeply involved throughout the entire problem-solving process. An emerging role is to provide a set of leading choices from which subject matter experts and managers can choose to make informed decisions. A key to success is to provide vehicles for understanding the trade-offs between candidates and interpreting the meritsmore » of each choice in the context of the decision-makers priorities. To achieve this objective, it is helpful to be able (a) to help subject matter experts identify quantitative criteria that match their priorities, (b) eliminate non-competitive choices through the use of a Pareto front, and (c) provide summary tools from which the trade-offs between alternatives can be quantitatively evaluated and discussed. A structured but flexible process for contributing to team decisions is described for situations when all choices can easily be enumerated as well as when a search algorithm to explore a vast number of potential candidates is required. In conclusion, a collection of diverse examples ranging from model selection, through multiple response optimization, and designing an experiment illustrate the approach.« less

Optimizing in a complex world: A statistician's role in decision making

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

Anderson-Cook, Christine M.

As applied statisticians increasingly participate as active members of problem-solving and decision-making teams, our role continues to evolve. Historically, we may have been seen as those who can help with data collection strategies or answer a specific question from a set of data. Nowadays, we are or strive to be more deeply involved throughout the entire problem-solving process. An emerging role is to provide a set of leading choices from which subject matter experts and managers can choose to make informed decisions. A key to success is to provide vehicles for understanding the trade-offs between candidates and interpreting the meritsmore » of each choice in the context of the decision-makers priorities. To achieve this objective, it is helpful to be able (a) to help subject matter experts identify quantitative criteria that match their priorities, (b) eliminate non-competitive choices through the use of a Pareto front, and (c) provide summary tools from which the trade-offs between alternatives can be quantitatively evaluated and discussed. A structured but flexible process for contributing to team decisions is described for situations when all choices can easily be enumerated as well as when a search algorithm to explore a vast number of potential candidates is required. In conclusion, a collection of diverse examples ranging from model selection, through multiple response optimization, and designing an experiment illustrate the approach.« less

The application of defaults to optimize parents' health-based choices for children.

PubMed

Loeb, Katharine L; Radnitz, Cynthia; Keller, Kathleen; Schwartz, Marlene B; Marcus, Sue; Pierson, Richard N; Shannon, Michael; DeLaurentis, Danielle

2017-06-01

Optimal defaults is a compelling model from behavioral economics and the psychology of human decision-making, designed to shape or "nudge" choices in a positive direction without fundamentally restricting options. The current study aimed to test the effectiveness of optimal (less obesogenic) defaults and parent empowerment priming on health-based decisions with parent-child (ages 3-8) dyads in a community-based setting. Two proof-of-concept experiments (one on breakfast food selections and one on activity choice) were conducted comparing the main and interactive effects of optimal versus suboptimal defaults, and parent empowerment priming versus neutral priming, on parents' health-related choices for their children. We hypothesized that in each experiment, making the default option more optimal will lead to more frequent health-oriented choices, and that priming parents to be the ultimate decision-makers on behalf of their child's health will potentiate this effect. Results show that in both studies, default condition, but not priming condition or the interaction between default and priming, significantly predicted choice (healthier vs. less healthy option). There was also a significant main effect for default condition (and no effect for priming condition or the interaction term) on the quantity of healthier food children consumed in the breakfast experiment. These pilot studies demonstrate that optimal defaults can be practicably implemented to improve parents' food and activity choices for young children. Results can inform policies and practices pertaining to obesogenic environmental factors in school, restaurant, and home environments. Copyright © 2017 Elsevier Ltd. All rights reserved.

Design and implementation of intelligent electronic warfare decision making algorithm

NASA Astrophysics Data System (ADS)

Peng, Hsin-Hsien; Chen, Chang-Kuo; Hsueh, Chi-Shun

2017-05-01

Electromagnetic signals and the requirements of timely response have been a rapid growth in modern electronic warfare. Although jammers are limited resources, it is possible to achieve the best electronic warfare efficiency by tactical decisions. This paper proposes the intelligent electronic warfare decision support system. In this work, we develop a novel hybrid algorithm, Digital Pheromone Particle Swarm Optimization, based on Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Shuffled Frog Leaping Algorithm (SFLA). We use PSO to solve the problem and combine the concept of pheromones in ACO to accumulate more useful information in spatial solving process and speed up finding the optimal solution. The proposed algorithm finds the optimal solution in reasonable computation time by using the method of matrix conversion in SFLA. The results indicated that jammer allocation was more effective. The system based on the hybrid algorithm provides electronic warfare commanders with critical information to assist commanders in effectively managing the complex electromagnetic battlefield.

A Briefing on Metrics and Risks for Autonomous Decision-Making in Aerospace Applications

NASA Technical Reports Server (NTRS)

Frost, Susan; Goebel, Kai Frank; Galvan, Jose Ramon

2012-01-01

Significant technology advances will enable future aerospace systems to safely and reliably make decisions autonomously, or without human interaction. The decision-making may result in actions that enable an aircraft or spacecraft in an off-nominal state or with slightly degraded components to achieve mission performance and safety goals while reducing or avoiding damage to the aircraft or spacecraft. Some key technology enablers for autonomous decision-making include: a continuous state awareness through the maturation of the prognostics health management field, novel sensor development, and the considerable gains made in computation power and data processing bandwidth versus system size. Sophisticated algorithms and physics based models coupled with these technological advances allow reliable assessment of a system, subsystem, or components. Decisions that balance mission objectives and constraints with remaining useful life predictions can be made autonomously to maintain safety requirements, optimal performance, and ensure mission objectives. This autonomous approach to decision-making will come with new risks and benefits, some of which will be examined in this paper. To start, an account of previous work to categorize or quantify autonomy in aerospace systems will be presented. In addition, a survey of perceived risks in autonomous decision-making in the context of piloted aircraft and remotely piloted or completely autonomous unmanned autonomous systems (UAS) will be presented based on interviews that were conducted with individuals from industry, academia, and government.

What is a hospital bed day worth? A contingent valuation study of hospital Chief Executive Officers.

PubMed

Page, Katie; Barnett, Adrain G; Graves, Nicholas

2017-02-14

Decreasing hospital length of stay, and so freeing up hospital beds, represents an important cost saving which is often used in economic evaluations. The savings need to be accurately quantified in order to make optimal health care resource allocation decisions. Traditionally the accounting cost of a bed is used. We argue instead that the economic cost of a bed day is the better value for making resource decisions, and we describe our valuation method and estimations for costing this important resource. We performed a contingent valuation using 37 Australian Chief Executive Officers' (CEOs) willingness to pay (WTP) to release bed days in their hospitals, both generally and using specific cases. We provide a succinct thematic analysis from qualitative interviews post survey completion, which provide insight into the decision making process. On average CEOs are willing to pay a marginal rate of $216 for a ward bed day and $436 for an Intensive Care Unit (ICU) bed day, with estimates of uncertainty being greater for ICU beds. These estimates are significantly lower (four times for ward beds and seven times for ICU beds) than the traditional accounting costs often used. Key themes to emerge from the interviews include the importance of national funding and targets, and their associated incentive structures, as well as the aversion to discuss bed days as an economic resource. This study highlights the importance for valuing bed days as an economic resource to inform cost effectiveness models and thus improve hospital decision making and resource allocation. Significantly under or over valuing the resource is very likely to result in sub-optimal decision making. We discuss the importance of recognising the opportunity costs of this resource and highlight areas for future research.

Pilot interaction with automated airborne decision making systems

NASA Technical Reports Server (NTRS)

Rouse, W. B.; Chu, Y. Y.; Greenstein, J. S.; Walden, R. S.

1976-01-01

An investigation was made of interaction between a human pilot and automated on-board decision making systems. Research was initiated on the topic of pilot problem solving in automated and semi-automated flight management systems and attempts were made to develop a model of human decision making in a multi-task situation. A study was made of allocation of responsibility between human and computer, and discussed were various pilot performance parameters with varying degrees of automation. Optimal allocation of responsibility between human and computer was considered and some theoretical results found in the literature were presented. The pilot as a problem solver was discussed. Finally the design of displays, controls, procedures, and computer aids for problem solving tasks in automated and semi-automated systems was considered.

Intelligent Work Process Engineering System

NASA Technical Reports Server (NTRS)

Williams, Kent E.

2003-01-01

Optimizing performance on work activities and processes requires metrics of performance for management to monitor and analyze in order to support further improvements in efficiency, effectiveness, safety, reliability and cost. Information systems are therefore required to assist management in making timely, informed decisions regarding these work processes and activities. Currently information systems regarding Space Shuttle maintenance and servicing do not exist to make such timely decisions. The work to be presented details a system which incorporates various automated and intelligent processes and analysis tools to capture organize and analyze work process related data, to make the necessary decisions to meet KSC organizational goals. The advantages and disadvantages of design alternatives to the development of such a system will be discussed including technologies, which would need to bedesigned, prototyped and evaluated.

Final findings on the development and evaluation of an en-route fuel optimal conflict resolution algorithm to support strategic decision-making.

DOT National Transportation Integrated Search

2012-01-01

The novel strategic conflict-resolution algorithm for fuel minimization that is documented in this report : provides air traffic controllers and/or pilots with fuel-optimal heading, speed, and altitude : recommendations in the en route flight phase, ...

Optimum stand prescriptions for ponderosa pine

Treesearch

David W. Hann; J. Douglas Brodie; Kurt H. Riitters

1983-01-01

Two examples for a northern Arizona ponderosa pine stand illustrate the usefulness of dynamic programming in making silvicultural decisions. The first example analyzes the optimal planting density for bare land, while the second examines the optimal precommercial thinning intensity for a 43-year-old stand. Bot hexamples assume that the manager's primary objective...

Biased and less sensitive: A gamified approach to delay discounting in heroin addiction.

PubMed

Scherbaum, Stefan; Haber, Paul; Morley, Kirsten; Underhill, Dylan; Moustafa, Ahmed A

2018-03-01

People with addiction will continue to use drugs despite adverse long-term consequences. We hypothesized (a) that this deficit persists during substitution treatment, and (b) that this deficit might be related not only to a desire for immediate gratification, but also to a lower sensitivity for optimal decision making. We investigated how individuals with a history of heroin addiction perform (compared to healthy controls) in a virtual reality delay discounting task. This novel task adds to established measures of delay discounting an assessment of the optimality of decisions, especially in how far decisions are influenced by a general choice bias and/or a reduced sensitivity to the relative value of the two alternative rewards. We used this measure of optimality to apply diffusion model analysis to the behavioral data to analyze the interaction between decision optimality and reaction time. The addiction group consisted of 25 patients with a history of heroin dependency currently participating in a methadone maintenance program; the control group consisted of 25 healthy participants with no history of substance abuse, who were recruited from the Western Sydney community. The patient group demonstrated greater levels of delay discounting compared to the control group, which is broadly in line with previous observations. Diffusion model analysis yielded a reduced sensitivity for the optimality of a decision in the patient group compared to the control group. This reduced sensitivity was reflected in lower rates of information accumulation and higher decision criteria. Increased discounting in individuals with heroin addiction is related not only to a generally increased bias to immediate gratification, but also to reduced sensitivity for the optimality of a decision. This finding is in line with other findings about the sensitivity of addicts in distinguishing optimal from nonoptimal choice options.

Novel Statistical Approach to Determine Inflammatory Bowel Disease: Patients' Perspectives on Shared Decision Making.

PubMed

Siegel, Corey A; Lofland, Jennifer H; Naim, Ahmad; Gollins, Jan; Walls, Danielle M; Rudder, Laura E; Reynolds, Chuck

2016-02-01

Limited information is available on patients' perspectives of shared decision-making practices used in inflammatory bowel disease (IBD). The aim of this study was to examine patient insights regarding shared decision making among patients with IBD using novel statistical technology to analyze qualitative data. Two 10-patient focus groups (10 ulcerative colitis patients and 10 Crohn's disease patients) were conducted in Chicago in January 2012 to explore patients' experiences, concerns, and preferences related to shared decision making. Key audio excerpts of focus group insights were embedded within a 25-min online patient survey and used for moment-to-moment affect trace analysis. A total of 355 IBD patients completed the survey (ulcerative colitis 51 %; Crohn's disease 49 %; female 54 %; 18-50 years of age 50 %). The majority of patients (66 %) reported increased satisfaction when they participated in shared decision making. Three unique patient clusters were identified based on their involvement in shared decision making: satisfied, content, and dissatisfied. Satisfied patients (18 %) had a positive physician relationship and a high level of trust with their physician. Content patients (48 %) had a moderate level of trust with their physician. Dissatisfied patients (34 %) had a life greatly affected by IBD, a low level of trust of their physician, a negative relationship with their physician, were skeptical of decisions, and did not rely on their physician for assistance. This study provides valuable insights regarding patients' perceptions of the shared decision-making process in IBD treatment using a novel moment-to-moment hybrid technology approach. Patient perspectives in this study indicate an increased desire for shared decision making in determining an optimal IBD treatment plan.

Structured decision making as a framework for large-scale wildlife harvest management decisions

USGS Publications Warehouse

Robinson, Kelly F.; Fuller, Angela K.; Hurst, Jeremy E.; Swift, Bryan L.; Kirsch, Arthur; Farquhar, James F.; Decker, Daniel J.; Siemer, William F.

2016-01-01

Fish and wildlife harvest management at large spatial scales often involves making complex decisions with multiple objectives and difficult tradeoffs, population demographics that vary spatially, competing stakeholder values, and uncertainties that might affect management decisions. Structured decision making (SDM) provides a formal decision analytic framework for evaluating difficult decisions by breaking decisions into component parts and separating the values of stakeholders from the scientific evaluation of management actions and uncertainty. The result is a rigorous, transparent, and values-driven process. This decision-aiding process provides the decision maker with a more complete understanding of the problem and the effects of potential management actions on stakeholder values, as well as how key uncertainties can affect the decision. We use a case study to illustrate how SDM can be used as a decision-aiding tool for management decision making at large scales. We evaluated alternative white-tailed deer (Odocoileus virginianus) buck-harvest regulations in New York designed to reduce harvest of yearling bucks, taking into consideration the values of the state wildlife agency responsible for managing deer, as well as deer hunters. We incorporated tradeoffs about social, ecological, and economic management concerns throughout the state. Based on the outcomes of predictive models, expert elicitation, and hunter surveys, the SDM process identified management alternatives that optimized competing objectives. The SDM process provided biologists and managers insight about aspects of the buck-harvest decision that helped them adopt a management strategy most compatible with diverse hunter values and management concerns.

Optimal Decision Making in a Class of Uncertain Systems Based on Uncertain Variables

NASA Astrophysics Data System (ADS)

Bubnicki, Z.

2006-06-01

The paper is concerned with a class of uncertain systems described by relational knowledge representations with unknown parameters which are assumed to be values of uncertain variables characterized by a user in the form of certainty distributions. The first part presents the basic optimization problem consisting in finding the decision maximizing the certainty index that the requirement given by a user is satisfied. The main part is devoted to the description of the optimization problem with the given certainty threshold. It is shown how the approach presented in the paper may be applied to some problems for anticipatory systems.

Preferences for Shared Decision Making in Older Adult Patients With Orthopedic Hand Conditions.

PubMed

Dardas, Agnes Z; Stockburger, Christopher; Boone, Sean; An, Tonya; Calfee, Ryan P

2016-10-01

The practice of medicine is shifting from a paternalistic doctor-patient relationship to a model in which the doctor and patient collaborate to decide optimal treatment. This study aims to determine whether the older orthopedic population desires a shared decision-making approach to care and to identify patient predictors for the preferred type of approach. This cross-sectional investigation enrolled 99 patients, minimum age 65 years, at a tertiary hand specialty practice between March and June 2015. All patients completed the Control Preferences Scale, a validated system that distinguishes among patient preferences for patient-directed, collaborative, or physician-directed decision making. Bivariate and logistic regression analyses assessed associations among demographic data; clinic encounter variables such as familiarity with provider, trauma, diagnosis, and treatment decision; and the primary outcome of Control Preferences Scale preferences. A total of 81% of patients analyzed preferred a more patient-directed role in decision making; 46% of the total cohort cited a collaborative approach as their most preferred treatment approach. Sixty-seven percent cited the most physician-directed approach as their least preferred model of decision making. In addition, 49% reported that spending more time with their physician to address questions and explain the diagnosis would be most useful when making a health care decision and 73% preferred additional written informational material. Familiarity with the provider was associated with being more likely to prefer a collaborative approach. Older adult patients with symptomatic upper-extremity conditions desire more patient-directed roles in treatment decision making. Given the limited amount of reliable information obtained independently outside the office visit, our data suggest that written decision aids offer an approach to shared decision making that is most consistent with the preferences of the older orthopedic patient. This study quantifies older adults' desire to participate in decision making when choosing among treatments for hand conditions. Copyright © 2016 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.

Soak Up the Rain New England Webinar Series: National ...

EPA Pesticide Factsheets

Presenters will provide an introduction to the most recent EPA green infrastructure tools to R1 stakeholders; and their use in making decisions about implementing green infrastructure. We will discuss structuring your green infrastructure decision, finding appropriate information and tools, evaluating options and selecting the right Best Management Practices mix for your needs.WMOST (Watershed Management Optimization Support Tool)- for screening a wide range of practices for cost-effectiveness in achieving watershed or water utilities management goals.GIWiz (Green Infrastructure Wizard)- a web application connecting communities to EPA Green Infrastructure tools and resources.Opti-Tool-designed to assist in developing technically sound and optimized cost-effective Stormwater management plans. National Stormwater Calculator- a desktop application for estimating the impact of land cover change and green infrastructure controls on stormwater runoff. DASEES-GI (Decision Analysis for a Sustainable Environment, Economy, and Society) – a framework for linking objectives and measures with green infrastructure methods. Presenters will provide an introduction to the most recent EPA green infrastructure tools to R1 stakeholders; and their use in making decisions about implementing green infrastructure. We will discuss structuring your green infrastructure decision, finding appropriate information and tools, evaluating options and selecting the right Best Management Pr

Considerations of net present value in policy making regarding diagnostic and therapeutic technologies.

PubMed

Califf, Robert M; Rasiel, Emma B; Schulman, Kevin A

2008-11-01

The pharmaceutical and medical device industries function in a business environment in which shareholders expect companies to optimize profit within legal and ethical standards. A fundamental tool used to optimize decision making is the net present value calculation, which estimates the current value of cash flows relating to an investment. We examined 3 prototypical research investment decisions that have been the source of public scrutiny to illustrate how policy decisions can be better understood when their impact on societally desirable investments by industry are viewed from the standpoint of their impact on net present value. In the case of direct, comparative clinical trials, a simple net present value calculation provides insight into why companies eschew such investments. In the case of pediatric clinical trials, the Pediatric Extension Rule changed the net present value calculation from unattractive to potentially very attractive by allowing patent extensions; thus, the dramatic increase in pediatric clinical trials can be explained by the financial return on investment. In the case of products for small markets, the fixed costs of development make this option financially unattractive. Policy decisions can be better understood when their impact on societally desirable investments by the pharmaceutical and medical device industries are viewed from the standpoint of their impact on net present value.

Educators' Experiences with and Attitudes toward Accessibility Features and Accommodations

ERIC Educational Resources Information Center

Thurlow, Martha L.; Larson, Erik D.; Lazarus, Sheryl S.; Shyyan, Vitaliy V.; Christensen, Laurene L.

2017-01-01

To evaluate the experiences that teachers and other decision makers were having with accessibility features and accommodations, as well as their attitudes toward them, an online survey was conducted with educators in nine states. These states were part of an Enhanced Assessment Initiative grant project to promote optimal decision making about…

Optimal decision making and matching are tied through diminishing returns

PubMed Central

2017-01-01

How individuals make decisions has been a matter of long-standing debate among economists and researchers in the life sciences. In economics, subjects are viewed as optimal decision makers who maximize their overall reward income. This framework has been widely influential, but requires a complete knowledge of the reward contingencies associated with a given choice situation. Psychologists and ecologists have observed that individuals tend to use a simpler “matching” strategy, distributing their behavior in proportion to relative rewards associated with their options. This article demonstrates that the two dominant frameworks of choice behavior are linked through the law of diminishing returns. The relatively simple matching can in fact provide maximal reward when the rewards associated with decision makers’ options saturate with the invested effort. Such saturating relationships between reward and effort are hallmarks of the law of diminishing returns. Given the prevalence of diminishing returns in nature and social settings, this finding can explain why humans and animals so commonly behave according to the matching law. The article underscores the importance of the law of diminishing returns in choice behavior. PMID:28739920

Optimal decision making and matching are tied through diminishing returns.

PubMed

Kubanek, Jan

2017-08-08

How individuals make decisions has been a matter of long-standing debate among economists and researchers in the life sciences. In economics, subjects are viewed as optimal decision makers who maximize their overall reward income. This framework has been widely influential, but requires a complete knowledge of the reward contingencies associated with a given choice situation. Psychologists and ecologists have observed that individuals tend to use a simpler "matching" strategy, distributing their behavior in proportion to relative rewards associated with their options. This article demonstrates that the two dominant frameworks of choice behavior are linked through the law of diminishing returns. The relatively simple matching can in fact provide maximal reward when the rewards associated with decision makers' options saturate with the invested effort. Such saturating relationships between reward and effort are hallmarks of the law of diminishing returns. Given the prevalence of diminishing returns in nature and social settings, this finding can explain why humans and animals so commonly behave according to the matching law. The article underscores the importance of the law of diminishing returns in choice behavior.

Merkel cell carcinoma: An algorithm for multidisciplinary management and decision-making.

PubMed

Prieto, Isabel; Pérez de la Fuente, Teresa; Medina, Susana; Castelo, Beatriz; Sobrino, Beatriz; Fortes, Jose R; Esteban, David; Cassinello, Fernando; Jover, Raquel; Rodríguez, Nuria

2016-02-01

Merkel cell carcinoma (MCC) is a rare and aggressive neuroendocrine tumor of the skin. Therapeutic approach is often unclear, and considerable controversy exists regarding MCC pathogenesis and optimal management. Due to its rising incidence and poor prognosis, it is imperative to establish the optimal therapy for both the tumor and the lymph node basin, and for treatment to include sentinel node biopsy. Sentinel node biopsy is currently the most consistent predictor of survival for MCC patients, although there are conflicting views and a lack of awareness regarding node management. Tumor and node management involve different specialists, and their respective decisions and interventions are interrelated. No effective systemic treatment has been made available to date, and therefore patients continue to experience distant failure, often without local failure. This review aims to improve multidisciplinary decision-making by presenting scientific evidence of the contributions of each team member implicated in MCC management. Following this review of previously published research, the authors conclude that multidisciplinary team management is beneficial for care, and propose a multidisciplinary decision algorithm for managing this tumor. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Modeling uncertainty in producing natural gas from tight sands

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

Chermak, J.M.; Dahl, C.A.; Patrick, R.H

1995-12-31

Since accurate geologic, petroleum engineering, and economic information are essential ingredients in making profitable production decisions for natural gas, we combine these ingredients in a dynamic framework to model natural gas reservoir production decisions. We begin with the certainty case before proceeding to consider how uncertainty might be incorporated in the decision process. Our production model uses dynamic optimal control to combine economic information with geological constraints to develop optimal production decisions. To incorporate uncertainty into the model, we develop probability distributions on geologic properties for the population of tight gas sand wells and perform a Monte Carlo study tomore » select a sample of wells. Geological production factors, completion factors, and financial information are combined into the hybrid economic-petroleum reservoir engineering model to determine the optimal production profile, initial gas stock, and net present value (NPV) for an individual well. To model the probability of the production abandonment decision, the NPV data is converted to a binary dependent variable. A logit model is used to model this decision as a function of the above geological and economic data to give probability relationships. Additional ways to incorporate uncertainty into the decision process include confidence intervals and utility theory.« less

New Directions for Military Decision Making Research in Combat and Operational Settings

DTIC Science & Technology

1991-12-01

information; their search rules emphasize feasibility more than optimality; decisions depend on the order in which alternatives are presented...12:76-90. Easterbrook, J.A. "The effect of Emotion on Cue Utilization and the Organization of Behaviour." Psychological Review, 1959, 66:183-201...Acquisition and Affective State on Halo, Accuracy, Information Retrival , and Evaluations." Organizational Behavior and Human Decision Processes, 1988, 42:22

Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data.

PubMed

Hager, Rebecca; Tsiatis, Anastasios A; Davidian, Marie

2018-05-18

Clinicians often make multiple treatment decisions at key points over the course of a patient's disease. A dynamic treatment regime is a sequence of decision rules, each mapping a patient's observed history to the set of available, feasible treatment options at each decision point, and thus formalizes this process. An optimal regime is one leading to the most beneficial outcome on average if used to select treatment for the patient population. We propose a method for estimation of an optimal regime involving two decision points when the outcome of interest is a censored survival time, which is based on maximizing a locally efficient, doubly robust, augmented inverse probability weighted estimator for average outcome over a class of regimes. By casting this optimization as a classification problem, we exploit well-studied classification techniques such as support vector machines to characterize the class of regimes and facilitate implementation via a backward iterative algorithm. Simulation studies of performance and application of the method to data from a sequential, multiple assignment randomized clinical trial in acute leukemia are presented. © 2018, The International Biometric Society.

Many faces of rationality: Implications of the great rationality debate for clinical decision-making.

PubMed

Djulbegovic, Benjamin; Elqayam, Shira

2017-10-01

Given that more than 30% of healthcare costs are wasted on inappropriate care, suboptimal care is increasingly connected to the quality of medical decisions. It has been argued that personal decisions are the leading cause of death, and 80% of healthcare expenditures result from physicians' decisions. Therefore, improving healthcare necessitates improving medical decisions, ie, making decisions (more) rational. Drawing on writings from The Great Rationality Debate from the fields of philosophy, economics, and psychology, we identify core ingredients of rationality commonly encountered across various theoretical models. Rationality is typically classified under umbrella of normative (addressing the question how people "should" or "ought to" make their decisions) and descriptive theories of decision-making (which portray how people actually make their decisions). Normative theories of rational thought of relevance to medicine include epistemic theories that direct practice of evidence-based medicine and expected utility theory, which provides the basis for widely used clinical decision analyses. Descriptive theories of rationality of direct relevance to medical decision-making include bounded rationality, argumentative theory of reasoning, adaptive rationality, dual processing model of rationality, regret-based rationality, pragmatic/substantive rationality, and meta-rationality. For the first time, we provide a review of wide range of theories and models of rationality. We showed that what is "rational" behaviour under one rationality theory may be irrational under the other theory. We also showed that context is of paramount importance to rationality and that no one model of rationality can possibly fit all contexts. We suggest that in context-poor situations, such as policy decision-making, normative theories based on expected utility informed by best research evidence may provide the optimal approach to medical decision-making, whereas in the context-rich circumstances other types of rationality, informed by human cognitive architecture and driven by intuition and emotions such as the aim to minimize regret, may provide better solution to the problem at hand. The choice of theory under which we operate is important as it determines both policy and our individual decision-making. © 2017 The Authors Journal of Evaluation in Clinical Practice Published by John Wiley & Sons Ltd.

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

PubMed

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

2018-07-01

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

Optimal healthcare decision making under multiple mathematical models: application in prostate cancer screening.

PubMed

Bertsimas, Dimitris; Silberholz, John; Trikalinos, Thomas

2018-03-01

Important decisions related to human health, such as screening strategies for cancer, need to be made without a satisfactory understanding of the underlying biological and other processes. Rather, they are often informed by mathematical models that approximate reality. Often multiple models have been made to study the same phenomenon, which may lead to conflicting decisions. It is natural to seek a decision making process that identifies decisions that all models find to be effective, and we propose such a framework in this work. We apply the framework in prostate cancer screening to identify prostate-specific antigen (PSA)-based strategies that perform well under all considered models. We use heuristic search to identify strategies that trade off between optimizing the average across all models' assessments and being "conservative" by optimizing the most pessimistic model assessment. We identified three recently published mathematical models that can estimate quality-adjusted life expectancy (QALE) of PSA-based screening strategies and identified 64 strategies that trade off between maximizing the average and the most pessimistic model assessments. All prescribe PSA thresholds that increase with age, and 57 involve biennial screening. Strategies with higher assessments with the pessimistic model start screening later, stop screening earlier, and use higher PSA thresholds at earlier ages. The 64 strategies outperform 22 previously published expert-generated strategies. The 41 most "conservative" ones remained better than no screening with all models in extensive sensitivity analyses. We augment current comparative modeling approaches by identifying strategies that perform well under all models, for various degrees of decision makers' conservativeness.

The online community based decision making support system for mitigating biased decision making

NASA Astrophysics Data System (ADS)

Kang, Sunghyun; Seo, Jiwan; Choi, Seungjin; Kim, Junho; Han, Sangyong

2016-10-01

As the Internet technology and social media advance, various information and opinions are shared and distributed through the online communities. However, the existence of implicit and explicit bias of opinions may have a potential influence on the outcomes. Compared to the importance of mitigating biased information, the study in this field is relatively young and does not address many important issues. In this paper we propose the noble approach to mitigate the biased opinions using conventional machine learning methods. The proposed method extracts the useful features such as inclination and sentiment of the community members. They are classified based on their previous behavior, and the propensity of the members is understood. This information on each community and its members is very useful and improve the ability to make an unbiased decision. The proposed method presented in this paper is shown to have the ability to assist optimal, fair and good decision making while also reducing the influence of implicit bias.

Overcoming Indecision by Changing the Decision Boundary

PubMed Central

2017-01-01

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

Self-reported strategies in decisions under risk: role of feedback, reasoning abilities, executive functions, short-term-memory, and working memory.

PubMed

Schiebener, Johannes; Brand, Matthias

2015-11-01

In decisions under objective risk conditions information about the decision options' possible outcomes and the rules for outcomes' occurrence are provided. Thus, deciders can base decision-making strategies on probabilistic laws. In many laboratory decision-making tasks, choosing the option with the highest winning probability in all trials (=maximization strategy) is probabilistically regarded the most rational behavior. However, individuals often behave less optimal, especially in case the individuals have lower cognitive functions or in case no feedback about consequences is provided in the situation. It is still unclear which cognitive functions particularly predispose individuals for using successful strategies and which strategies profit from feedback. We investigated 195 individuals with two decision-making paradigms, the Game of Dice Task (GDT) (with and without feedback), and the Card Guessing Game. Thereafter, participants reported which strategies they had applied. Interaction effects (feedback × strategy), effect sizes, and uncorrected single group comparisons suggest that feedback in the GDT tended to be more beneficial to individuals reporting exploratory strategies (e.g., use intuition). In both tasks, the self-reported use of more principled and more rational strategies was accompanied by better decision-making performance and better performances in reasoning and executive functioning tasks. The strategy groups did not significantly differ in most short-term and working-memory tasks. Thus, particularly individual differences in reasoning and executive functions seem to predispose individuals toward particular decision-making strategies. Feedback seems to be useful for individuals who rather explore the decision-making situation instead of following a certain plan.

Multi-Criteria Decision Making for a Spatial Decision Support System on the Analysis of Changing Risk

NASA Astrophysics Data System (ADS)

Olyazadeh, Roya; van Westen, Cees; Bakker, Wim H.; Aye, Zar Chi; Jaboyedoff, Michel; Derron, Marc-Henri

2014-05-01

Natural hazard risk management requires decision making in several stages. Decision making on alternatives for risk reduction planning starts with an intelligence phase for recognition of the decision problems and identifying the objectives. Development of the alternatives and assigning the variable by decision makers to each alternative are employed to the design phase. Final phase evaluates the optimal choice by comparing the alternatives, defining indicators, assigning a weight to each and ranking them. This process is referred to as Multi-Criteria Decision Making analysis (MCDM), Multi-Criteria Evaluation (MCE) or Multi-Criteria Analysis (MCA). In the framework of the ongoing 7th Framework Program "CHANGES" (2011-2014, Grant Agreement No. 263953) of the European Commission, a Spatial Decision Support System is under development, that has the aim to analyse changes in hydro-meteorological risk and provide support to selecting the best risk reduction alternative. This paper describes the module for Multi-Criteria Decision Making analysis (MCDM) that incorporates monetary and non-monetary criteria in the analysis of the optimal alternative. The MCDM module consists of several components. The first step is to define criteria (or Indicators) which are subdivided into disadvantages (criteria that indicate the difficulty for implementing the risk reduction strategy, also referred to as Costs) and advantages (criteria that indicate the favorability, also referred to as benefits). In the next step the stakeholders can use the developed web-based tool for prioritizing criteria and decision matrix. Public participation plays a role in decision making and this is also planned through the use of a mobile web-version where the general local public can indicate their agreement on the proposed alternatives. The application is being tested through a case study related to risk reduction of a mountainous valley in the Alps affected by flooding. Four alternatives are evaluated in this case study namely: construction of defense structures, relocation, implementation of an early warning system and spatial planning regulations. Some of the criteria are determined partly in other modules of the CHANGES SDSS, such as the costs for implementation, the risk reduction in monetary values, and societal risk. Other criteria, which could be environmental, economic, cultural, perception in nature, are defined by different stakeholders such as local authorities, expert organizations, private sector, and local public. In the next step, the stakeholders weight the importance of the criteria by pairwise comparison and visualize the decision matrix, which is a matrix based on criteria versus alternatives values. Finally alternatives are ranked by Analytic Hierarchy Process (AHP) method. We expect that this approach will help the decision makers to ease their works and reduce their costs, because the process is more transparent, more accurate and involves a group decision. In that way there will be more confidence in the overall decision making process. Keywords: MCDM, Analytic Hierarchy Process (AHP), SDSS, Natural Hazard Risk Management

The impact of uncertainty on optimal emission policies

NASA Astrophysics Data System (ADS)

Botta, Nicola; Jansson, Patrik; Ionescu, Cezar

2018-05-01

We apply a computational framework for specifying and solving sequential decision problems to study the impact of three kinds of uncertainties on optimal emission policies in a stylized sequential emission problem.We find that uncertainties about the implementability of decisions on emission reductions (or increases) have a greater impact on optimal policies than uncertainties about the availability of effective emission reduction technologies and uncertainties about the implications of trespassing critical cumulated emission thresholds. The results show that uncertainties about the implementability of decisions on emission reductions (or increases) call for more precautionary policies. In other words, delaying emission reductions to the point in time when effective technologies will become available is suboptimal when these uncertainties are accounted for rigorously. By contrast, uncertainties about the implications of exceeding critical cumulated emission thresholds tend to make early emission reductions less rewarding.

Intelligent data management for real-time spacecraft monitoring

NASA Technical Reports Server (NTRS)

Schwuttke, Ursula M.; Gasser, Les; Abramson, Bruce

1992-01-01

Real-time AI systems have begun to address the challenge of restructuring problem solving to meet real-time constraints by making key trade-offs that pursue less than optimal strategies with minimal impact on system goals. Several approaches for adapting to dynamic changes in system operating conditions are known. However, simultaneously adapting system decision criteria in a principled way has been difficult. Towards this end, a general technique for dynamically making such trade-offs using a combination of decision theory and domain knowledge has been developed. Multi-attribute utility theory (MAUT), a decision theoretic approach for making one-time decisions is discussed and dynamic trade-off evaluation is described as a knowledge-based extension of MAUT that is suitable for highly dynamic real-time environments, and provides an example of dynamic trade-off evaluation applied to a specific data management trade-off in a real-world spacecraft monitoring application.

Training surgeons in shared decision-making with cancer patients aged 65 years and older: a pilot study.

PubMed

Geessink, Noralie H; Schoon, Yvonne; Olde Rikkert, Marcel Gm; van Goor, Harry

2017-01-01

Treatment decision-making in older patients with colorectal (CRC) or pancreatic cancer (PC) needs improvement. We introduced the EASYcare in Geriatric Onco-surgery (EASY-GO) intervention to optimize the shared decision-making (SDM) process among these patients. The EASY-GO intervention comprised a working method with geriatric assessment and SDM training for surgeons. A non-equivalent control group design was used. Newly diagnosed CRC/PC patients aged ≥65 years were included. Primary patient-reported experiences were the quality of SDM (SDM-Q-9, range 0-100), involvement in decision-making (Visual Analog Scale for Involvement in the decision-making process [range 0-10]), satisfaction about decision-making (Visual Analog Scale for Satisfaction concerning the decision-making process [range 0-10]), and decisional regret (Decisional Regret Scale [DRS], range 0-100). Only for DRS, lower scores are better. A total of 71.4% of the involved consultants and 42.9% of the involved residents participated in the EASY-GO training. Only 4 trained surgeons consulted patients both before (n=19) and after (n=19) training and were consequently included in the analyses. All patient-reported experience measures showed a consistent but non-significant change in the direction of improved decision-making after training. According to surgeons, decisions were significantly more often made together with the patient after training (before, 38.9% vs after, 73.7%, p =0.04). Sub-analyses per diagnosis showed that patient experiences among older PC patients consistent and clinically relevant changed in the direction of improved decision-making after training (SDM-Q-9 +13.4 [95% CI -7.9; 34.6], VAS-I +0.27 [95% CI -1.1; 1.6], VAS-S +0.88 [95% CI -0.5; 2.2], DRS -10.3 [95% CI -27.8; 7.1]). This pilot study strengthens the practical potential of the intervention's concept among older surgical cancer patients.

Using future thinking to reduce temporal discounting: Under what circumstances are the medial temporal lobes critical?

PubMed

Palombo, D J; Keane, M M; Verfaellie, M

2016-08-01

The capacity to envision the future plays an important role in many aspects of cognition, including our ability to make optimal, adaptive choices. Past work has shown that the medial temporal lobe (MTL) is necessary for decisions that draw on episodic future thinking. By contrast, little is known about the role of the MTL in decisions that draw on semantic future thinking. Accordingly, the present study investigated whether the MTL contributes to one form of decision making, namely intertemporal choice, when such decisions depend on semantic consideration of the future. In an intertemporal choice task, participants must select either a smaller amount of money that is available in the present or a larger amount of money that would be available at a future date. Amnesic individuals with MTL damage and healthy control participants performed such a task in which, prior to making a choice, they engaged in a semantic generation exercise, wherein they generated items that they would purchase with the future reward. In experiment 1, we found that, relative to a baseline condition involving standard intertemporal choice, healthy individuals were more inclined to select a larger, later reward over a smaller, present reward after engaging in semantic future thinking. By contrast, amnesic participants were paradoxically less inclined to wait for a future reward following semantic future thinking. This finding suggests that amnesics may have had difficulty "tagging" the generated item(s) as belonging to the future. Critically, experiment 2 showed that when the generated items were presented alongside the intertemporal choices, both controls and amnesic participants shifted to more patient choices. These findings suggest that the MTL is not needed for making optimal decisions that draw on semantic future thinking as long as scaffolding is provided to support accurate time tagging. Together, these findings stand to better clarify the role of the MTL in decision making. Published by Elsevier Ltd.

Can Sophie's Choice Be Adequately Captured by Cold Computation of Minimizing Losses? An fMRI Study of Vital Loss Decisions

PubMed Central

Li, Qi; Qin, Shaozheng; Rao, Li-Lin; Zhang, Wencai; Ying, Xiaoping; Guo, Xiuyan; Guo, Chunyan; Ding, Jinghong; Li, Shu; Luo, Jing

2011-01-01

The vast majority of decision-making research is performed under the assumption of the value maximizing principle. This principle implies that when making decisions, individuals try to optimize outcomes on the basis of cold mathematical equations. However, decisions are emotion-laden rather than cool and analytic when they tap into life-threatening considerations. Using functional magnetic resonance imaging (fMRI), this study investigated the neural mechanisms underlying vital loss decisions. Participants were asked to make a forced choice between two losses across three conditions: both losses are trivial (trivial-trivial), both losses are vital (vital-vital), or one loss is trivial and the other is vital (vital-trivial). Our results revealed that the amygdala was more active and correlated positively with self-reported negative emotion associated with choice during vital-vital loss decisions, when compared to trivial-trivial loss decisions. The rostral anterior cingulate cortex was also more active and correlated positively with self-reported difficulty of choice during vital-vital loss decisions. Compared to the activity observed during trivial-trivial loss decisions, the orbitofrontal cortex and ventral striatum were more active and correlated positively with self-reported positive emotion of choice during vital-trivial loss decisions. Our findings suggest that vital loss decisions involve emotions and cannot be adequately captured by cold computation of minimizing losses. This research will shed light on how people make vital loss decisions. PMID:21412428

Multicriteria hierarchical iterative interactive algorithm for organizing operational modes of large heat supply systems

NASA Astrophysics Data System (ADS)

Korotkova, T. I.; Popova, V. I.

2017-11-01

The generalized mathematical model of decision-making in the problem of planning and mode selection providing required heat loads in a large heat supply system is considered. The system is multilevel, decomposed into levels of main and distribution heating networks with intermediate control stages. Evaluation of the effectiveness, reliability and safety of such a complex system is carried out immediately according to several indicators, in particular pressure, flow, temperature. This global multicriteria optimization problem with constraints is decomposed into a number of local optimization problems and the coordination problem. An agreed solution of local problems provides a solution to the global multicriterion problem of decision making in a complex system. The choice of the optimum operational mode of operation of a complex heat supply system is made on the basis of the iterative coordination process, which converges to the coordinated solution of local optimization tasks. The interactive principle of multicriteria task decision-making includes, in particular, periodic adjustment adjustments, if necessary, guaranteeing optimal safety, reliability and efficiency of the system as a whole in the process of operation. The degree of accuracy of the solution, for example, the degree of deviation of the internal air temperature from the required value, can also be changed interactively. This allows to carry out adjustment activities in the best way and to improve the quality of heat supply to consumers. At the same time, an energy-saving task is being solved to determine the minimum required values of heads at sources and pumping stations.

Do not fear your opponent: suboptimal changes of a prevention strategy when facing stronger opponents.

PubMed

Slezak, Diego Fernandez; Sigman, Mariano

2012-08-01

The time spent making a decision and its quality define a widely studied trade-off. Some models suggest that the time spent is set to optimize reward, as verified empirically in simple-decision making experiments. However, in a more complex perspective compromising components of regulation focus, ambitions, fear, risk and social variables, adjustment of the speed-accuracy trade-off may not be optimal. Specifically, regulatory focus theory shows that people can be set in a promotion mode, where focus is on seeking to approach a desired state (to win), or in a prevention mode, focusing to avoid undesired states (not to lose). In promotion, people are eager to take risks increasing speed and decreasing accuracy. In prevention, strategic vigilance increases, decreasing speed and improving accuracy. When time and accuracy have to be compromised, one can ask which of these 2 strategies optimizes reward, leading to optimal performance. This is investigated here in a unique experimental environment. Decision making is studied in rapid-chess (180 s per game), in which the goal of a player is to mate the opponent in a finite amount of time or, alternatively, time-out of the opponent with sufficient material to mate. In different games, players face strong and weak opponents. It was observed that (a) players adopt a more conservative strategy when facing strong opponents, with slower and more accurate moves, and (b) this strategy is suboptimal: Players increase their winning likelihood against strong opponents using the policy they adopt when confronting opponents with similar strength. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Sustainability is possible despite greed - Exploring the nexus between profitability and sustainability in common pool resource systems.

PubMed

Osten, Friedrich Burkhard von der; Kirley, Michael; Miller, Tim

2017-05-23

The sustainable use of common pool resources has become a significant global challenge. It is now widely accepted that specific mechanisms such as community-based management strategies, institutional responses such as resource privatization, information availability and emergent social norms can be used to constrain individual 'harvesting' to socially optimal levels. However, there is a paucity of research focused specifically on aligning profitability and sustainability goals. In this paper, an integrated mathematical model of a common pool resource game is developed to explore the nexus between the underlying costs and benefits of harvesting decisions and the sustainable level of a shared, dynamic resource. We derive optimal harvesting efforts analytically and then use numerical simulations to show that individuals in a group can learn to make harvesting decisions that lead to the globally optimal levels. Individual agents make their decision based on signals received and a trade-off between economic and ecological sustainability. When the balance is weighted towards profitability, acceptable economic and social outcomes emerge. However, if individual agents are solely driven by profit, the shared resource is depleted in the long run - sustainability is possible despite some greed, but too much will lead to over-exploitation.

Patient perspectives on informed decision-making surrounding dialysis initiation

PubMed Central

Song, Mi-Kyung; Lin, Feng-Chang; Gilet, Constance A.; Arnold, Robert M.; Bridgman, Jessica C.; Ward, Sandra E.

2013-01-01

Background Careful patient–clinician shared decision-making about dialysis initiation has been promoted, but few studies have addressed patient perspectives on the extent of information provided and how decisions to start dialysis are made. Methods Ninety-nine maintenance dialysis patients recruited from 15 outpatient dialysis centers in North Carolina completed semistructured interviews on information provision and communication about the initiation of dialysis. These data were examined with content analysis. In addition, informed decision-making (IDM) scores were created by summing patient responses (yes/no) to 10 questions about the decision-making. Results The mean IDM score was 4.4 (of 10; SD = 2.0); 67% scored 5 or lower. Age at the time of decision-making (r = −0.27, P = 0.006), years of education (r = 0.24, P = 0.02) and presence of a warning about progressing to end-stage kidney disease (t = 2.9, P = 0.005) were significantly associated with IDM scores. Nearly 70% said that the risks and burdens of dialysis were not mentioned at all, and only one patient recalled that the doctor offered the option of not starting dialysis. While a majority (67%) said that they felt they had no choice about starting dialysis (because the alternative would be death) or about dialysis modality, only 21.2% said that they had felt rushed to make a decision. About one-third of the patients perceived that the decision to start dialysis and modality was already made by the doctor. Conclusions A majority of patients felt unprepared and ill-informed about the initiation of dialysis. Improving the extent of IDM about dialysis may optimize patient preparation prior to starting treatment and their perceptions about the decision-making process. PMID:23901048

A brief history of decision making.

PubMed

Buchanan, Leigh; O'Connell, Andrew

2006-01-01

Sometime around the middle of the past century, telephone executive Chester Barnard imported the term decision making from public administration into the business world. There it began to replace narrower terms, like "resource allocation" and "policy making," shifting the way managers thought about their role from continuous, Hamlet-like deliberation toward a crisp series of conclusions reached and actions taken. Yet, decision making is, of course, a broad and ancient human pursuit, flowing back to a time when people sought guidance from the stars. From those earliest days, we have strived to invent better tools for the purpose, from the Hindu-Arabic systems for numbering and algebra, to Aristotle's systematic empiricism, to friar Occam's advances in logic, to Francis Bacon's inductive reasoning, to Descartes's application of the scientific method. A growing sophistication with managing risk, along with a nuanced understanding of human behavior and advances in technology that support and mimic cognitive processes, has improved decision making in many situations. Even so, the history of decision-making strategies--captured in this time line and examined in the four accompanying essays on risk, group dynamics, technology, and instinct--has not marched steadily toward perfect rationalism. Twentieth-century theorists showed that the costs of acquiring information lead executives to make do with only good-enough decisions. Worse, people decide against their own economic interests even when they know better. And in the absence of emotion, it's impossible to make any decisions at all. Erroneous framing, bounded awareness, excessive optimism: The debunking of Descartes's rational man threatens to swamp our confidence in our choices. Is it really surprising, then, that even as technology dramatically increases our access to information, Malcolm Gladwell extols the virtues of gut decisions made, literally, in the blink of an eye?

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.

The role of decision analysis in informed consent: choosing between intuition and systematicity.

PubMed

Ubel, P A; Loewenstein, G

1997-03-01

An important goal of informed consent is to present information to patients so that they can decide which medical option is best for them, according to their values. Research in cognitive psychology has shown that people are rapidly overwhelmed by having to consider more than a few options in making choices. Decision analysis provides a quantifiable way to assess patients' values, and it eliminates the burden of integrating these values with probabilistic information. In this paper we evaluate the relative importance of intuition and systematicity in informed consent. We point out that there is no gold standard for optimal decision making in decisions that hinge on patient values. We also point out that in some such situations it is too early to assume that the benefits of systematicity outweigh the benefits of intuition. Research is needed to address the question of which situations favor the use of intuitive approaches of decision making and which call for a more systematic approach.

Now or not-now? The influence of alexithymia on intertemporal decision-making.

PubMed

Scarpazza, Cristina; Sellitto, Manuela; di Pellegrino, Giuseppe

2017-06-01

Optimal intertemporal decisions arise from the balance between an emotional-visceral component, signaling the need for immediate gratification, and a rational, long-term oriented component. Alexithymia, a personality construct characterized by amplified sensitivity to internal bodily signals of arousal, may result in enhanced activation of the emotional-visceral component over the cognitive-rational one. To test this hypothesis, participants with high- and low-alexithymia level were compared at an intertemporal decision-making task, and their choice behavior correlated with their interoceptive sensitivity. We show that high-alexithymic tend to behave more impatiently than low-alexithymic in intertemporal decisions, particularly when the sooner reward is immediately available. Moreover, the greater their sensitivity to their own visceral sensations, the greater the impatience. Together, these results suggest a disproportionate valuation of reward available immediately in high alexithymia, possibly reflecting heightened perception of bodily physiological signals, which ultimately would bias their intertemporal decision-making. Copyright © 2017 Elsevier Inc. All rights reserved.

Supply chain optimization for pediatric perioperative departments.

PubMed

Davis, Janice L; Doyle, Robert

2011-09-01

Economic challenges compel pediatric perioperative departments to reduce nonlabor supply costs while maintaining the quality of patient care. Optimization of the supply chain introduces a framework for decision making that drives fiscally responsible decisions. The cost-effective supply chain is driven by implementing a value analysis process for product selection, being mindful of product sourcing decisions to reduce supply expense, creating logistical efficiency that will eliminate redundant processes, and managing inventory to ensure product availability. The value analysis approach is an analytical methodology for product selection that involves product evaluation and recommendation based on consideration of clinical benefit, overall financial impact, and revenue implications. Copyright © 2011 AORN, Inc. Published by Elsevier Inc. All rights reserved.

Incorporating perceptual decision-making training into high-intensity interval training for Australian football umpires.

PubMed

Kittel, Aden; Elsworthy, Nathan; Spittle, Michael

2018-05-30

Existing methods for developing decision-making skill for Australian football umpires separate the physical and perceptual aspects of their performance. This study aimed to determine the efficacy of incorporating video-based decision-making training during high-intensity interval training sessions, specific for Australian football umpires. 20 amateur Australian football umpires volunteered to participate in a randomised control trial. Participants completed an 8-week training intervention in a conditioning only (CON; n=7), combined video-based training and conditioning (COM; n=7), or separated conditioning and video-based training (SEP; n=6) group. Preliminary and post-testing involved a Yo-Yo Intermittent Recovery Test (Yo-YoIR1), and 10x300m run test with an Australian football specific video-based decision-making task. Overall, changes in decision-making accuracy following the intervention were unclear between groups. SEP was possibly beneficial compared to COM in Yo-YoIR1 performance, whereas CON was likely beneficial compared to COM in 10x300m sprint performance. There was no additional benefit to completing video-based training, whether combined with, or separate to physical training, suggesting that this was not an optimal training method. For video-based training to be an effective decision-making tool, detailed feedback should be incorporated into training. It is recommended that longer conditioning and video-based training interventions be implemented to determine training effectiveness.

Frontal, Striatal, and Medial Temporal Sensitivity to Value Distinguishes Risk-Taking from Risk-Aversive Older Adults during Decision Making.

PubMed

Goh, Joshua O S; Su, Yu-Shiang; Tang, Yong-Jheng; McCarrey, Anna C; Tereshchenko, Alexander; Elkins, Wendy; Resnick, Susan M

2016-12-07

Aging compromises the frontal, striatal, and medial temporal areas of the reward system, impeding accurate value representation and feedback processing critical for decision making. However, substantial variability characterizes age-related effects on the brain so that some older individuals evince clear neurocognitive declines whereas others are spared. Moreover, the functional correlates of normative individual differences in older-adult value-based decision making remain unclear. We performed a functional magnetic resonance imaging study in 173 human older adults during a lottery choice task in which costly to more desirable stakes were depicted using low to high expected values (EVs) of points. Across trials that varied in EVs, participants decided to accept or decline the offered stakes to maximize total accumulated points. We found that greater age was associated with less optimal decisions, accepting stakes when losses were likely and declining stakes when gains were likely, and was associated with increased frontal activity for costlier stakes. Critically, risk preferences varied substantially across older adults and neural sensitivity to EVs in the frontal, striatal, and medial temporal areas dissociated risk-aversive from risk-taking individuals. Specifically, risk-averters increased neural responses to increasing EVs as stakes became more desirable, whereas risk-takers increased neural responses with decreasing EV as stakes became more costly. Risk preference also modulated striatal responses during feedback with risk-takers showing more positive responses to gains compared with risk-averters. Our findings highlight the frontal, striatal, and medial temporal areas as key neural loci in which individual differences differentially affect value-based decision-making ability in older adults. Frontal, striatal, and medial temporal functions implicated in value-based decision processing of rewards and costs undergo substantial age-related changes. However, age effects on brain function and cognition differ across individuals. How this normative variation relates to older-adult value-based decision making is unclear. We found that although the ability make optimal decisions declines with age, there is still much individual variability in how this deterioration occurs. Critically, whereas risk-averters showed increased neural activity to increasingly valuable stakes in frontal, striatal, and medial temporal areas, risk-takers instead increased activity as stakes became more costly. Such distinct functional decision-making processing in these brain regions across normative older adults may reflect individual differences in susceptibility to age-related brain changes associated with incipient cognitive impairment. Copyright © 2016 the authors 0270-6474/16/3612498-12$15.00/0.

The coordinating contracts of supply chain in a fuzzy decision environment.

PubMed

Sang, Shengju

2016-01-01

The rapid change of the product life cycle is making the parameters of the supply chain models more and more uncertain. Therefore, we consider the coordination mechanisms between one manufacturer and one retailer in a fuzzy decision marking environment, where the parameters of the models can be forecasted and expressed as the triangular fuzzy variables. The centralized decision-making system, two types of supply chain contracts, namely, the revenue sharing contract and the return contract are proposed. To obtain their optimal policies, the fuzzy set theory is adopted to solve these fuzzy models. Finally, three numerical examples are provided to analyze the impacts of the fuzziness of the market demand, retail price and salvage value of the product on the optimal solutions in two contracts. It shows that in order to obtain more fuzzy expected profits the retailer and the manufacturer should seek as low fuzziness of demand, high fuzziness of the retail price and the salvage value as possible in both contracts.

Tuning the speed-accuracy trade-off to maximize reward rate in multisensory decision-making.

PubMed

Drugowitsch, Jan; DeAngelis, Gregory C; Angelaki, Dora E; Pouget, Alexandre

2015-06-19

For decisions made under time pressure, effective decision making based on uncertain or ambiguous evidence requires efficient accumulation of evidence over time, as well as appropriately balancing speed and accuracy, known as the speed/accuracy trade-off. For simple unimodal stimuli, previous studies have shown that human subjects set their speed/accuracy trade-off to maximize reward rate. We extend this analysis to situations in which information is provided by multiple sensory modalities. Analyzing previously collected data (Drugowitsch et al., 2014), we show that human subjects adjust their speed/accuracy trade-off to produce near-optimal reward rates. This trade-off can change rapidly across trials according to the sensory modalities involved, suggesting that it is represented by neural population codes rather than implemented by slow neuronal mechanisms such as gradual changes in synaptic weights. Furthermore, we show that deviations from the optimal speed/accuracy trade-off can be explained by assuming an incomplete gradient-based learning of these trade-offs.

A Novel Group Decision-Making Method Based on Sensor Data and Fuzzy Information.

PubMed

Bai, Yu-Ting; Zhang, Bai-Hai; Wang, Xiao-Yi; Jin, Xue-Bo; Xu, Ji-Ping; Su, Ting-Li; Wang, Zhao-Yang

2016-10-28

Algal bloom is a typical phenomenon of the eutrophication of rivers and lakes and makes the water dirty and smelly. It is a serious threat to water security and public health. Most scholars studying solutions for this pollution have studied the principles of remediation approaches, but few have studied the decision-making and selection of the approaches. Existing research uses simplex decision-making information which is highly subjective and uses little of the data from water quality sensors. To utilize these data and solve the rational decision-making problem, a novel group decision-making method is proposed using the sensor data with fuzzy evaluation information. Firstly, the optimal similarity aggregation model of group opinions is built based on the modified similarity measurement of Vague values. Secondly, the approaches' ability to improve the water quality indexes is expressed using Vague evaluation methods. Thirdly, the water quality sensor data are analyzed to match the features of the alternative approaches with grey relational degrees. This allows the best remediation approach to be selected to meet the current water status. Finally, the selection model is applied to the remediation of algal bloom in lakes. The results show this method's rationality and feasibility when using different data from different sources.

A probabilistic multi-criteria decision making technique for conceptual and preliminary aerospace systems design

NASA Astrophysics Data System (ADS)

Bandte, Oliver

It has always been the intention of systems engineering to invent or produce the best product possible. Many design techniques have been introduced over the course of decades that try to fulfill this intention. Unfortunately, no technique has succeeded in combining multi-criteria decision making with probabilistic design. The design technique developed in this thesis, the Joint Probabilistic Decision Making (JPDM) technique, successfully overcomes this deficiency by generating a multivariate probability distribution that serves in conjunction with a criterion value range of interest as a universally applicable objective function for multi-criteria optimization and product selection. This new objective function constitutes a meaningful Xnetric, called Probability of Success (POS), that allows the customer or designer to make a decision based on the chance of satisfying the customer's goals. In order to incorporate a joint probabilistic formulation into the systems design process, two algorithms are created that allow for an easy implementation into a numerical design framework: the (multivariate) Empirical Distribution Function and the Joint Probability Model. The Empirical Distribution Function estimates the probability that an event occurred by counting how many times it occurred in a given sample. The Joint Probability Model on the other hand is an analytical parametric model for the multivariate joint probability. It is comprised of the product of the univariate criterion distributions, generated by the traditional probabilistic design process, multiplied with a correlation function that is based on available correlation information between pairs of random variables. JPDM is an excellent tool for multi-objective optimization and product selection, because of its ability to transform disparate objectives into a single figure of merit, the likelihood of successfully meeting all goals or POS. The advantage of JPDM over other multi-criteria decision making techniques is that POS constitutes a single optimizable function or metric that enables a comparison of all alternative solutions on an equal basis. Hence, POS allows for the use of any standard single-objective optimization technique available and simplifies a complex multi-criteria selection problem into a simple ordering problem, where the solution with the highest POS is best. By distinguishing between controllable and uncontrollable variables in the design process, JPDM can account for the uncertain values of the uncontrollable variables that are inherent to the design problem, while facilitating an easy adjustment of the controllable ones to achieve the highest possible POS. Finally, JPDM's superiority over current multi-criteria decision making techniques is demonstrated with an optimization of a supersonic transport concept and ten contrived equations as well as a product selection example, determining an airline's best choice among Boeing's B-747, B-777, Airbus' A340, and a Supersonic Transport. The optimization examples demonstrate JPDM's ability to produce a better solution with a higher POS than an Overall Evaluation Criterion or Goal Programming approach. Similarly, the product selection example demonstrates JPDM's ability to produce a better solution with a higher POS and different ranking than the Overall Evaluation Criterion or Technique for Order Preferences by Similarity to the Ideal Solution (TOPSIS) approach.

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.

Trade-off decisions in distribution utility management

NASA Astrophysics Data System (ADS)

Slavickas, Rimas Anthony

As a result of the "unbundling" of traditional monopolistic electricity generation and transmission enterprises into a free-market economy, power distribution utilities are faced with very difficult decisions pertaining to electricity supply options and quality of service to the customers. The management of distribution utilities has become increasingly complex, versatile, and dynamic to the extent that conventional, non-automated management tools are almost useless and obsolete. This thesis presents a novel and unified approach to managing electricity supply options and quality of service to customers. The technique formulates the problem in terms of variables, parameters, and constraints. An advanced Mixed Integer Programming (MIP) optimization formulation is developed together with novel, logical, decision-making algorithms. These tools enable the utility management to optimize various cost components and assess their time-trend impacts, taking into account the intangible issues such as customer perception, customer expectation, social pressures, and public response to service deterioration. The above concepts are further generalized and a Logical Proportion Analysis (LPA) methodology and associated software have been developed. Solutions using numbers are replaced with solutions using words (character strings) which more closely emulate the human decision-making process and advance the art of decision-making in the power utility environment. Using practical distribution utility operation data and customer surveys, the developments outlined in this thesis are successfully applied to several important utility management problems. These involve the evaluation of alternative electricity supply options, the impact of rate structures on utility business, and the decision of whether to continue to purchase from a main grid or generate locally (partially or totally) by building Non-Utility Generation (NUG).

The Optimal Observation Problem applied to a rating curve estimation including the "cost-to-wait"

NASA Astrophysics Data System (ADS)

Raso, Luciano; Werner, Micha; Weijs, Steven

2013-04-01

In order to manage a system, a decision maker (DM) tries to make the best decision under uncertainty, having partial knowledge on the effects of his/her decision. Observations reduce uncertainty, but are costly. Deciding what to observe and when to stop observing is a complementary problem that the DM has to face. The Optimal Observation Problem (OOP) offers a solution to the questions: (1) which observation is more effective? And (2) Is the next observation worth its cost? We show an application of the OOP to a rating curve estimation in the White Carter River (Scotland). The cost of extra gauging is compensated by the value of better decisions, that reduce the costs due to floods. The observational decision is then whether to gauge, and when. In the application, we include the "cost-to-wait" in the cost structure. The Algorithm find thus an optimal trade-off between getting less informative data now or wait for more informative, but later. The OOP can be used to plan a measurement campaign, also taking into account that the rating curve can change.

Bioeconomic Approaches to Sustainable Management of Natural Tropical Forests

Treesearch

Tom Holmes; Erin O. Sills

2016-01-01

Bioeconomic models are idealized representations of human-nature interactions used to describe how the decisions that people make regarding the harvest of biological resources affect the future condition of resource stocks and the flow of net economic benefits. This modeling approach posits an assumed goal or objective that a decision-maker seeks to optimize subject to...

Big Data & Learning Analytics: A Potential Way to Optimize eLearning Technological Tools

ERIC Educational Resources Information Center

García, Olga Arranz; Secades, Vidal Alonso

2013-01-01

In the information age, one of the most influential institutions is education. The recent emergence of MOOCS [Massively Open Online Courses] is a sample of the new expectations that are offered to university students. Basing decisions on data and evidence seems obvious, and indeed, research indicates that data-driven decision-making improves…

Public preferences for engagement in Health Technology Assessment decision-making: protocol of a mixed methods study.

PubMed

Wortley, Sally; Tong, Allison; Lancsar, Emily; Salkeld, Glenn; Howard, Kirsten

2015-07-14

Much attention in recent years has been given to the topic of public engagement in health technology assessment (HTA) decision-making. HTA organizations spend substantial resources and time on undertaking public engagement, and numerous studies have examined challenges and barriers to engagement in the decision-making process however uncertainty remains as to optimal methods to incorporate the views of the public in HTA decision-making. Little research has been done to ascertain whether current engagement processes align with public preferences and to what extent their desire for engagement is dependent on the question being asked by decision-makers or the characteristics of the decision. This study will examine public preferences for engagement in Australian HTA decision-making using an exploratory mixed methods design. The aims of this study are to: 1) identify characteristics about HTA decisions that are important to the public in determining whether public engagement should be undertaken on a particular topic, 2) determine which decision characteristics influence public preferences for the extent, or type of public engagement, and 3) describe reasons underpinning these preferences. Focus group participants from the general community, aged 18-70 years, will be purposively sampled from the Australian population to ensure a wide range of demographic groups. Each focus group will include a general discussion on public engagement as well as a ranking exercise using a modified nominal group technique (NGT). The NGT will inform the design of a discrete choice study to quantitatively assess public preferences for engagement in HTA decision-making. The proposed research seeks to investigate under what circumstances and how the public would like their views and preferences to be considered in health technology assessments. HTA organizations regularly make decisions about when and how public engagement should occur but without consideration of the public's preferences on the method and extent of engagement. This information has the potential to assist decision-makers in tailoring engagement approaches, and may be particularly useful in decisions with potential for conflict where clarification of public values and preferences could strengthen the decision-making process.

INTEGRATING DATA ANALYTICS AND SIMULATION METHODS TO SUPPORT MANUFACTURING DECISION MAKING

PubMed Central

Kibira, Deogratias; Hatim, Qais; Kumara, Soundar; Shao, Guodong

2017-01-01

Modern manufacturing systems are installed with smart devices such as sensors that monitor system performance and collect data to manage uncertainties in their operations. However, multiple parameters and variables affect system performance, making it impossible for a human to make informed decisions without systematic methodologies and tools. Further, the large volume and variety of streaming data collected is beyond simulation analysis alone. Simulation models are run with well-prepared data. Novel approaches, combining different methods, are needed to use this data for making guided decisions. This paper proposes a methodology whereby parameters that most affect system performance are extracted from the data using data analytics methods. These parameters are used to develop scenarios for simulation inputs; system optimizations are performed on simulation data outputs. A case study of a machine shop demonstrates the proposed methodology. This paper also reviews candidate standards for data collection, simulation, and systems interfaces. PMID:28690363

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.

Investigation of Multi-Criteria Decision Consistency: A Triplex Approach to Optimal Oilfield Portfolio Investment Decisions

NASA Astrophysics Data System (ADS)

Qaradaghi, Mohammed

Complexity of the capital intensive oil and gas portfolio investments is continuously growing. It is manifested in the constant increase in the type, number and degree of risks and uncertainties, which consequently lead to more challenging decision making problems. A typical complex decision making problem in petroleum exploration and production (E&P) is the selection and prioritization of oilfields/projects in a portfolio investment. Prioritizing oilfields maybe required for different purposes, including the achievement of a targeted production and allocation of limited available development resources. These resources cannot be distributed evenly nor can they be allocated based on the oilfield size or production capacity alone since various other factors need to be considered simultaneously. These factors may include subsurface complexity, size of reservoir, plateau production and needed infrastructure in addition to other issues of strategic concern, such as socio-economic, environmental and fiscal policies, particularly when the decision making involves governments or national oil companies. Therefore, it would be imperative to employ decision aiding tools that not only address these factors, but also incorporate the decision makers' preferences clearly and accurately. However, the tools commonly used in project portfolio selection and optimization, including intuitive approaches, vary in their focus and strength in addressing the different criteria involved in such decision problems. They are also disadvantaged by a number of drawbacks, which may include lacking the capacity to address multiple and interrelated criteria, uncertainty and risk, project relationship with regard to value contribution and optimum resource utilization, non-monetary attributes, decision maker's knowledge and expertise, in addition to varying levels of ease of use and other practical and theoretical drawbacks. These drawbacks have motivated researchers to investigate other tools and techniques that can provide more flexibility and inclusiveness in the decision making process, such as Multi-Criteria Decision Making (MCDM) methods. However, it can be observed that the MCDM literature: 1) is primarily focused on suggesting certain MCDM techniques to specific problems without providing sufficient evidence for their selection, 2) is inadequate in addressing MCDM in E&P portfolio selection and prioritization compared with other fields, and 3) does not address prioritizing brownfields (i.e., developed oilfields). This research study aims at addressing the above drawbacks through combining three MCDM methods (i.e., AHP, PROMETHEE and TOPSIS) into a single decision making tool that can support optimal oilfield portfolio investment decisions by helping determine the share of each oilfield of the total development resources allocated. Selecting these methods is reinforced by a pre-deployment and post-deployment validation framework. In addition, this study proposes a two-dimensional consistency test to verify the output coherence or prioritization stability of the MCDM methods in comparison with an intuitive approach. Nine scenarios representing all possible outcomes of the internal and external consistency tests are further proposed to reach a conclusion. The methodology is applied to a case study of six major oilfields in Iraq to generate percentage shares of each oilfield of a total production target that is in line with Iraq's aspiration to increase oil production. However, the methodology is intended to be applicable to other E&P portfolio investment prioritization scenarios by taking the specific contextual characteristics into consideration.

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.

Empirical validation of a real options theory based method for optimizing evacuation decisions within chemical plants.

PubMed

Reniers, G L L; Audenaert, A; Pauwels, N; Soudan, K

2011-02-15

This article empirically assesses and validates a methodology to make evacuation decisions in case of major fire accidents in chemical clusters. In this paper, a number of empirical results are presented, processed and discussed with respect to the implications and management of evacuation decisions in chemical companies. It has been shown in this article that in realistic industrial settings, suboptimal interventions may result in case the prospect to obtain additional information at later stages of the decision process is ignored. Empirical results also show that implications of interventions, as well as the required time and workforce to complete particular shutdown activities, may be very different from one company to another. Therefore, to be optimal from an economic viewpoint, it is essential that precautionary evacuation decisions are tailor-made per company. Copyright © 2010 Elsevier B.V. All rights reserved.

Heuristic decision-making about research participation in children with cystic fibrosis.

PubMed

Christofides, Emily; Dobson, Jennifer A; Solomon, Melinda; Waters, Valerie; O'Doherty, Kieran C

2016-08-01

Traditional perspectives on informed consent assume that when faced with decisions about whether to participate in research, individuals behave according to principles of classical rationality, taking into account all available information to weigh risks and benefits to come to a decision that is optimal for them. However, theoretical and empirical research in psychology suggests that people may not make decisions in this way. Less is known about decision-making processes as they pertain to participating in biomedical research, particularly when the participants are children. We sought to better understand research decision processes especially in children who tend to participate extensively in research due to chronic illness. To learn more about children's decision-making in this context, we interviewed 19 young patients with cystic fibrosis (male n = 7; female n = 12) aged 8-18 years (M = 13 years) at a children's hospital in Canada between April and August 2013. We found that participants generally had a default approach to participation decisions, which they attributed to their parents' attitudes to research, experiences of having grown up participating in research, trusting the researchers, and wanting to help. Most of our participants made the decision to participate in research based on a heuristic with a baseline to say "yes", subject to change based on aspects of the research or particular preferences. In particular, concerns with the procedure, unwillingness to talk about cystic fibrosis, logistical challenges, and perceptions of risk all influenced the decision, as did the perceived importance or personal relevance of the research. Our study illustrates that rather than conducting risk/benefit analyses, participants tended to adopt a heuristic-like approach, consistent with decision theories that view heuristic decision-making as ecologically rational. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Optimal throughput for cognitive radio with energy harvesting in fading wireless channel.

PubMed

Vu-Van, Hiep; Koo, Insoo

2014-01-01

Energy resource management is a crucial problem of a device with a finite capacity battery. In this paper, cognitive radio is considered to be a device with an energy harvester that can harvest energy from a non-RF energy resource while performing other actions of cognitive radio. Harvested energy will be stored in a finite capacity battery. At the start of the time slot of cognitive radio, the radio needs to determine if it should remain silent or carry out spectrum sensing based on the idle probability of the primary user and the remaining energy in order to maximize the throughput of the cognitive radio system. In addition, optimal sensing energy and adaptive transmission power control are also investigated in this paper to effectively utilize the limited energy of cognitive radio. Finding an optimal approach is formulated as a partially observable Markov decision process. The simulation results show that the proposed optimal decision scheme outperforms the myopic scheme in which current throughput is only considered when making a decision.

Reward rate optimization in two-alternative decision making: empirical tests of theoretical predictions.

PubMed

Simen, Patrick; Contreras, David; Buck, Cara; Hu, Peter; Holmes, Philip; Cohen, Jonathan D

2009-12-01

The drift-diffusion model (DDM) implements an optimal decision procedure for stationary, 2-alternative forced-choice tasks. The height of a decision threshold applied to accumulating information on each trial determines a speed-accuracy tradeoff (SAT) for the DDM, thereby accounting for a ubiquitous feature of human performance in speeded response tasks. However, little is known about how participants settle on particular tradeoffs. One possibility is that they select SATs that maximize a subjective rate of reward earned for performance. For the DDM, there exist unique, reward-rate-maximizing values for its threshold and starting point parameters in free-response tasks that reward correct responses (R. Bogacz, E. Brown, J. Moehlis, P. Holmes, & J. D. Cohen, 2006). These optimal values vary as a function of response-stimulus interval, prior stimulus probability, and relative reward magnitude for correct responses. We tested the resulting quantitative predictions regarding response time, accuracy, and response bias under these task manipulations and found that grouped data conformed well to the predictions of an optimally parameterized DDM.

The Choice Project: Peer Workers Promoting Shared Decision Making at a Youth Mental Health Service.

PubMed

Simmons, Magenta Bender; Batchelor, Samantha; Dimopoulos-Bick, Tara; Howe, Deb

2017-08-01

In youth mental health services, consumer participation is essential, but few implementation strategies exist to engage young consumers. This project evaluated an intervention implemented in an Australian youth mental health service that utilized peer workers to promote shared decision making via an online tool. All new clients ages 16-25 were invited to participate in this nonrandomized comparative study, which used a historical comparison group (N=80). Intervention participants (N=149) engaged with a peer worker and used the online tool before and during their intake assessment. Pre- and postintake data were collected for both groups; measures included decisional conflict, perceived shared decision making, and satisfaction. A series of paired t tests, analyses of variance, and multiple regressions were conducted to assess differences in scores across intervention and comparison groups and pre- and postintake assessments. Ratings of perceived shared decision making with intake workers were higher in the intervention group than in the comparison group (p=.015). In both groups, decisional conflict scores were significantly lower after the intake assessment (p<.001 for both groups). Both perceived shared decision making and lower decisional conflict were associated with satisfaction (p<.015). Young people who participated in an intervention that combined peer work and shared decision making reported feeling more involved in their assessment. Feeling involved and having lower decisional conflict after seeing an intake worker were important for client satisfaction. These findings demonstrate the importance of both peer work and shared decision making for promoting optimal outcomes in youth mental health services.

Patients' views on their decision making during inpatient rehabilitation after newly acquired spinal cord injury-A qualitative interview-based study.

PubMed

Scheel-Sailer, Anke; Post, Marcel W; Michel, Franz; Weidmann-Hügle, Tatjana; Baumann Hölzle, Ruth

2017-10-01

Involving patients in decision making is a legal requirement in many countries, associated with better rehabilitation outcomes, but not easily accomplished during initial inpatient rehabilitation after severe trauma. Providing medical treatment according to the principles of shared decision making is challenging as a point in case for persons with spinal cord injury (SCI). The aim of this study was to retrospectively explore the patients' views on their participation in decision making during their first inpatient rehabilitation after onset of SCI, in order to optimize treatment concepts. A total of 22 participants with SCI were interviewed in-depth using a semi-structured interview scheme between 6 months and 35 years post-onset. Interviews were transcribed verbatim and analysed with the Mayring method for qualitative content analysis. Participants experienced a substantially reduced ability to participate in decision making during the early phase after SCI. They perceived physical, psychological and environmental factors to have impacted upon this ability. Patients mentioned regaining their ability to make decisions was an important goal during their first rehabilitation. Receiving adequate information in an understandable and personalized way was a prerequisite to achieve this goal. Other important factors included medical and psychological condition, personal engagement, time and dialogue with peers. During the initial rehabilitation of patients with SCI, professionals need to deal with the discrepancy between the obligation to respect a patient's autonomy and their diminished ability for decision making. © 2017 The Authors Health Expectations Published by John Wiley & Sons Ltd.

Optimal Decision-making Model of Integrated Water Resources Management - A Case of Hsinchu Water Resources Management

NASA Astrophysics Data System (ADS)

Wang, S. Y.; Ho, C. C.; Chang, L. C.

2017-12-01

The public use water in Hsinchu are mainly supplied from Baoshan Reservoir, Second Baoshan Reservoir, Yongheshan Reservoir and Longen Weir. However, the increasing water demand, caused by development of the Hsinchu Science and Industrial Park, results in supply stable water getting more difficult. For stabilize water supply in Hsinchu, the study applies long-term and short-term plans to fulfill the water shortage. Developing an efficient methodology to define a cost-effective action portfolio is an important task. Hence, the study develops a novel decision model, the Stochastic Programming with Recourse Decision Model (SPRDM), to estimate a cost-effective action portfolio. The first-stage of SPRDM determine the long-term action portfolio and the portfolio accompany recourse information (the probability for water shortage event). The second-stage of SPRDM optimize the cost-effective action portfolio in response to the recourse information. In order to consider the uncertainty of reservoir sediment and demand growth, the study set 9 scenarios comprise optimistic, most likely, and pessimistic reservoir sediment and demand growth. The results show the optimal action portfolio consist of FengTain Lake and Panlon Weir, Hsinchu Desalination Plant, Domestic and Industrial Water long-term plans, and Emergency Backup Well, Irrigation Water Transference, Preliminary Water Rationing, Advanced Water Rationing and Water Transport from Other Districts short-term plans. The minimum expected cost of optimal action portfolio is NT$1.1002 billion. The results can be used as a reference for decision making because the results have considered the uncertainty of varied hydrology, reservoir sediment, and water demand growth.

How outcome prediction could affect patient decision making in knee replacements: a qualitative study.

PubMed

Barlow, Timothy; Scott, Patricia; Griffin, Damian; Realpe, Alba

2016-07-22

There is approximately a 17 % dissatisfaction rate with knee replacements. Calls for tools that can pre-operatively identify patients at risk of being dissatisfied have been widespread. However, it is not known how to present such information to patients, how it would affect their decision making process, and at what part of the pathway such a tool should be used. Using focus groups involving 12 participants and in-depth interviews with 10 participants, we examined how individual predictions of outcome could affect patients' decision making by providing fictitious predictions to patients at different stages of treatment. A thematic analysis was used to analyse the data. Our results demonstrate several interesting findings. Firstly, patients who have received information from friends and family are unwilling to adjust their expectation of outcome down (i.e. to a worse outcome), but highly willing to adjust it up (to a better outcome). This is an example of the optimism bias, and suggests that the effect on expectation of a poor outcome prediction would be blunted. Secondly, patients generally wanted a "bottom line" outcome, rather than lots of detail. Thirdly, patients who were earlier in their treatment for osteoarthritis were more likely to find the information useful, and it was more likely to affect their decision, than patients later in their treatment pathway. This research suggest that an outcome prediction tool would have most effect targeted towards people at the start of their treatment pathway, with a "bottom line" prediction of outcome. However, any effect on expectation and decision making of a poor outcome prediction is likely to be blunted by the optimism bias. These findings merit replication in a larger sample size.

Mammalian choices: combining fast-but-inaccurate and slow-but-accurate decision-making systems.

PubMed

Trimmer, Pete C; Houston, Alasdair I; Marshall, James A R; Bogacz, Rafal; Paul, Elizabeth S; Mendl, Mike T; McNamara, John M

2008-10-22

Empirical findings suggest that the mammalian brain has two decision-making systems that act at different speeds. We represent the faster system using standard signal detection theory. We represent the slower (but more accurate) cortical system as the integration of sensory evidence over time until a certain level of confidence is reached. We then consider how two such systems should be combined optimally for a range of information linkage mechanisms. We conclude with some performance predictions that will hold if our representation is realistic.

Integrating Land Cover Modeling and Adaptive Management to Conserve Endangered Species and Reduce Catastrophic Fire Risk

NASA Technical Reports Server (NTRS)

Breininger, David; Duncan, Brean; Eaton, Mitchell; Johnson, Fred; Nichols, James

2014-01-01

Land cover modeling is used to inform land management, but most often via a two-step process where science informs how management alternatives can influence resources and then decision makers can use this to make decisions. A more efficient process is to directly integrate science and decision making, where science allows us to learn to better accomplish management objectives and is developed to address specific decisions. Co-development of management and science is especially productive when decisions are complicated by multiple objectives and impeded by uncertainty. Multiple objectives can be met by specification of tradeoffs, and relevant uncertainty can be addressed through targeted science (i.e., models and monitoring). We describe how to integrate habitat and fuels monitoring with decision making focused on dual objectives of managing for endangered species and minimizing catastrophic fire risk. Under certain conditions, both objectives might be achieved by a similar management policy, but habitat trajectories suggest tradeoffs. Knowledge about system responses to actions can be informed by applying competing management actions to different land units in the same system state and by ideas about fire behavior. Monitoring and management integration is important to optimize state-specific management decisions and increase knowledge about system responses. We believe this approach has broad utility for and cover modeling programs intended to inform decision making.

Simulation-optimization model for production planning in the blood supply chain.

PubMed

Osorio, Andres F; Brailsford, Sally C; Smith, Honora K; Forero-Matiz, Sonia P; Camacho-Rodríguez, Bernardo A

2017-12-01

Production planning in the blood supply chain is a challenging task. Many complex factors such as uncertain supply and demand, blood group proportions, shelf life constraints and different collection and production methods have to be taken into account, and thus advanced methodologies are required for decision making. This paper presents an integrated simulation-optimization model to support both strategic and operational decisions in production planning. Discrete-event simulation is used to represent the flows through the supply chain, incorporating collection, production, storing and distribution. On the other hand, an integer linear optimization model running over a rolling planning horizon is used to support daily decisions, such as the required number of donors, collection methods and production planning. This approach is evaluated using real data from a blood center in Colombia. The results show that, using the proposed model, key indicators such as shortages, outdated units, donors required and cost are improved.

Online gaming for learning optimal team strategies in real time

NASA Astrophysics Data System (ADS)

Hudas, Gregory; Lewis, F. L.; Vamvoudakis, K. G.

2010-04-01

This paper first presents an overall view for dynamical decision-making in teams, both cooperative and competitive. Strategies for team decision problems, including optimal control, zero-sum 2-player games (H-infinity control) and so on are normally solved for off-line by solving associated matrix equations such as the Riccati equation. However, using that approach, players cannot change their objectives online in real time without calling for a completely new off-line solution for the new strategies. Therefore, in this paper we give a method for learning optimal team strategies online in real time as team dynamical play unfolds. In the linear quadratic regulator case, for instance, the method learns the Riccati equation solution online without ever solving the Riccati equation. This allows for truly dynamical team decisions where objective functions can change in real time and the system dynamics can be time-varying.

Decision-making ability of Physarum polycephalum enhanced by its coordinated spatiotemporal oscillatory dynamics.

PubMed

Iwayama, Koji; Zhu, Liping; Hirata, Yoshito; Aono, Masashi; Hara, Masahiko; Aihara, Kazuyuki

2016-04-12

An amoeboid unicellular organism, a plasmodium of the true slime mold Physarum polycephalum, exhibits complex spatiotemporal oscillatory dynamics and sophisticated information processing capabilities while deforming its amorphous body. We previously devised an 'amoeba-based computer (ABC),' that implemented optical feedback control to lead this amoeboid organism to search for a solution to the traveling salesman problem (TSP). In the ABC, the shortest TSP route (the optimal solution) is represented by the shape of the organism in which the body area (nutrient absorption) is maximized while the risk of being exposed to aversive light stimuli is minimized. The shortness of the TSP route found by ABC, therefore, serves as a quantitative measure of the optimality of the decision made by the organism. However, it remains unclear how the decision-making ability of the organism originates from the oscillatory dynamics of the organism. We investigated the number of coexisting traveling waves in the spatiotemporal patterns of the oscillatory dynamics of the organism. We show that a shorter TSP route can be found when the organism exhibits a lower number of traveling waves. The results imply that the oscillatory dynamics are highly coordinated throughout the global body. Based on the results, we discuss the fact that the decision-making ability of the organism can be enhanced not by uncorrelated random fluctuations, but by its highly coordinated oscillatory dynamics.

Decision Making and Risk Management in Adventure Sports Coaching

ERIC Educational Resources Information Center

Collins, Loel; Collins, Dave

2013-01-01

Adventure sport coaches practice in environments that are dynamic and high in risk, both perceived and actual. The inherent risks associated with these activities, individuals' responses and the optimal exploitation of both combine to make the processes of risk management more complex and hazardous than the traditional sports where risk management…

Bayesian Phase II optimization for time-to-event data based on historical information.

PubMed

Bertsche, Anja; Fleischer, Frank; Beyersmann, Jan; Nehmiz, Gerhard

2017-01-01

After exploratory drug development, companies face the decision whether to initiate confirmatory trials based on limited efficacy information. This proof-of-concept decision is typically performed after a Phase II trial studying a novel treatment versus either placebo or an active comparator. The article aims to optimize the design of such a proof-of-concept trial with respect to decision making. We incorporate historical information and develop pre-specified decision criteria accounting for the uncertainty of the observed treatment effect. We optimize these criteria based on sensitivity and specificity, given the historical information. Specifically, time-to-event data are considered in a randomized 2-arm trial with additional prior information on the control treatment. The proof-of-concept criterion uses treatment effect size, rather than significance. Criteria are defined on the posterior distribution of the hazard ratio given the Phase II data and the historical control information. Event times are exponentially modeled within groups, allowing for group-specific conjugate prior-to-posterior calculation. While a non-informative prior is placed on the investigational treatment, the control prior is constructed via the meta-analytic-predictive approach. The design parameters including sample size and allocation ratio are then optimized, maximizing the probability of taking the right decision. The approach is illustrated with an example in lung cancer.

Coordinating vendor-buyer decisions for imperfect quality items considering trade credit and fully backlogged shortages

NASA Astrophysics Data System (ADS)

Khanna, Aditi; Gautam, Prerna; Jaggi, Chandra K.

2016-03-01

Supply chain management has become a critical issue for modern business environments. In today's world of cooperative decision-making, individual decisions in order to reduce inventory costs may not lead to an overall optimal solution. Coordination is necessary among participants of supply chain to achieve better performance. There are legitimate and important efforts from the vendor to enhance the relation with buyer; one such effort is offering trade credit which has been a driver of growth and development of business between them. The cost of financing is a core consideration in effective financial management, in general and in context of business. Also, due to imperfect production a vendor may produce defective items which results in shortages. Motivated with these aspects, an integrated vendor-buyer inventory model is developed for imperfect quality items with allowable shortages; in which the vendor offers credit period to the buyer for payment. The objective is to minimize the total joint annual costs incurred by the vendor and the buyer by using integrated decision making approach. The expected total annual integrated cost is derived and a solution procedure is provided to find the optimal solution. Numerical analysis shows that the integrated model gives an impressive cost reduction, in comparison to independent decision policies by the vendor and the buyer.

Building Bridges between Perceptual and Economic Decision-Making: Neural and Computational Mechanisms.

PubMed

Summerfield, Christopher; Tsetsos, Konstantinos

2012-01-01

Investigation into the neural and computational bases of decision-making has proceeded in two parallel but distinct streams. Perceptual decision-making (PDM) is concerned with how observers detect, discriminate, and categorize noisy sensory information. Economic decision-making (EDM) explores how options are selected on the basis of their reinforcement history. Traditionally, the sub-fields of PDM and EDM have employed different paradigms, proposed different mechanistic models, explored different brain regions, disagreed about whether decisions approach optimality. Nevertheless, we argue that there is a common framework for understanding decisions made in both tasks, under which an agent has to combine sensory information (what is the stimulus) with value information (what is it worth). We review computational models of the decision process typically used in PDM, based around the idea that decisions involve a serial integration of evidence, and assess their applicability to decisions between good and gambles. Subsequently, we consider the contribution of three key brain regions - the parietal cortex, the basal ganglia, and the orbitofrontal cortex (OFC) - to perceptual and EDM, with a focus on the mechanisms by which sensory and reward information are integrated during choice. We find that although the parietal cortex is often implicated in the integration of sensory evidence, there is evidence for its role in encoding the expected value of a decision. Similarly, although much research has emphasized the role of the striatum and OFC in value-guided choices, they may play an important role in categorization of perceptual information. In conclusion, we consider how findings from the two fields might be brought together, in order to move toward a general framework for understanding decision-making in humans and other primates.

Building Bridges between Perceptual and Economic Decision-Making: Neural and Computational Mechanisms

PubMed Central

Summerfield, Christopher; Tsetsos, Konstantinos

2012-01-01

Investigation into the neural and computational bases of decision-making has proceeded in two parallel but distinct streams. Perceptual decision-making (PDM) is concerned with how observers detect, discriminate, and categorize noisy sensory information. Economic decision-making (EDM) explores how options are selected on the basis of their reinforcement history. Traditionally, the sub-fields of PDM and EDM have employed different paradigms, proposed different mechanistic models, explored different brain regions, disagreed about whether decisions approach optimality. Nevertheless, we argue that there is a common framework for understanding decisions made in both tasks, under which an agent has to combine sensory information (what is the stimulus) with value information (what is it worth). We review computational models of the decision process typically used in PDM, based around the idea that decisions involve a serial integration of evidence, and assess their applicability to decisions between good and gambles. Subsequently, we consider the contribution of three key brain regions – the parietal cortex, the basal ganglia, and the orbitofrontal cortex (OFC) – to perceptual and EDM, with a focus on the mechanisms by which sensory and reward information are integrated during choice. We find that although the parietal cortex is often implicated in the integration of sensory evidence, there is evidence for its role in encoding the expected value of a decision. Similarly, although much research has emphasized the role of the striatum and OFC in value-guided choices, they may play an important role in categorization of perceptual information. In conclusion, we consider how findings from the two fields might be brought together, in order to move toward a general framework for understanding decision-making in humans and other primates. PMID:22654730

Improving care coordination in the specialty referral process between primary and specialty care.

PubMed

Lin, Caroline Y

2012-01-01

There is growing evidence of sub-optimal care coordination in the US. Care coordination includes the specialty referral process, which involves referral decision-making and information transfer between primary and specialty care. This article summarizes the evidence of sub-optimal care coordination in this process, as well as potential strategies to improve it.

Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing.

PubMed

Tulabandhula, Theja; Rudin, Cynthia

2014-06-01

Our goal is to design a prediction and decision system for real-time use during a professional car race. In designing a knowledge discovery process for racing, we faced several challenges that were overcome only when domain knowledge of racing was carefully infused within statistical modeling techniques. In this article, we describe how we leveraged expert knowledge of the domain to produce a real-time decision system for tire changes within a race. Our forecasts have the potential to impact how racing teams can optimize strategy by making tire-change decisions to benefit their rank position. Our work significantly expands previous research on sports analytics, as it is the only work on analytical methods for within-race prediction and decision making for professional car racing.

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

PubMed

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

2014-10-01

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

Fuzzy multi-objective optimization case study based on an anaerobic co-digestion process of food waste leachate and piggery wastewater.

PubMed

Choi, Angelo Earvin Sy; Park, Hung Suck

2018-06-20

This paper presents the development and evaluation of fuzzy multi-objective optimization for decision-making that includes the process optimization of anaerobic digestion (AD) process. The operating cost criteria which is a fundamental research gap in previous AD analysis was integrated for the case study in this research. In this study, the mixing ratio of food waste leachate (FWL) and piggery wastewater (PWW), calcium carbonate (CaCO 3 ) and sodium chloride (NaCl) concentrations were optimized to enhance methane production while minimizing operating cost. The results indicated a maximum of 63.3% satisfaction for both methane production and operating cost under the following optimal conditions: mixing ratio (FWL: PWW) - 1.4, CaCO 3 - 2970.5 mg/L and NaCl - 2.7 g/L. In multi-objective optimization, the specific methane yield (SMY) was 239.0 mL CH 4 /g VS added , while 41.2% volatile solids reduction (VSR) was obtained at an operating cost of 56.9 US$/ton. In comparison with the previous optimization study that utilized the response surface methodology, the SMY, VSR and operating cost of the AD process were 310 mL/g, 54% and 83.2 US$/ton, respectively. The results from multi-objective fuzzy optimization proves to show the potential application of this technique for practical decision-making in the process optimization of AD process. Copyright © 2018 Elsevier Ltd. All rights reserved.

Telehealth: When Technology Meets Health Care

MedlinePlus

... of digital information and communication technologies, such as computers and mobile devices, to access health care services ... your medical history may not be considered. The computer-driven decision-making model may not be optimal ...

Fingernail Injuries and NASA's Integrated Medical Model

NASA Technical Reports Server (NTRS)

Kerstman, Eric; Butler, Doug

2008-01-01

The goal of space medicine is to optimize both crew health and performance. Currently, expert opinion is primarily relied upon for decision-making regarding medical equipment and supplies flown in space. Evidence-based decisions are preferred due to mass and volume limitations and the expense of space flight. The Integrated Medical Model (IMM) is an attempt to move us in that direction!

Risk preferences in strategic wildfire decision making: A choice experiment with U.S. wildfire managers

Treesearch

Matthew J. Wibbenmeyer; Michael S. Hand; David E. Calkin; Tyron J. Venn; Matthew P. Thompson

2013-01-01

Federal policy has embraced risk management as an appropriate paradigm for wildfire management. Economic theory suggests that over repeated wildfire events, potential economic costs and risks of ecological damage are optimally balanced when management decisions are free from biases, risk aversion, and risk seeking. Of primary concern in this article is how managers...

Bayesian design of decision rules for failure detection

NASA Technical Reports Server (NTRS)

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

1984-01-01

The formulation of the decision making process of a failure detection algorithm as a Bayes sequential decision problem provides a simple conceptualization of the decision rule design problem. As the optimal Bayes rule is not computable, a methodology that is based on the Bayesian approach and aimed at a reduced computational requirement is developed for designing suboptimal rules. A numerical algorithm is constructed to facilitate the design and performance evaluation of these suboptimal rules. The result of applying this design methodology to an example shows that this approach is potentially a useful one.

[Clinical economics: a concept to optimize healthcare services].

PubMed

Porzsolt, F; Bauer, K; Henne-Bruns, D

2012-03-01

Clinical economics strives to support healthcare decisions by economic considerations. Making economic decisions does not mean saving costs but rather comparing the gained added value with the burden which has to be accepted. The necessary rules are offered in various disciplines, such as economy, epidemiology and ethics. Medical doctors have recognized these rules but are not applying them in daily clinical practice. This lacking orientation leads to preventable errors. Examples of these errors are shown for diagnosis, screening, prognosis and therapy. As these errors can be prevented by application of clinical economic principles the possible consequences for optimization of healthcare are discussed.

Cyber Physical Intelligence for Oil Spills (CPI)

NASA Astrophysics Data System (ADS)

Lary, D. J.

2015-12-01

The National Academy of Sciences estimate 1.7 to 8.8 million tons of oil are released into global waters every year. The effects of these spills include dead wildlife, oil covered marshlands and contaminated water. Deepwater horizon cost approximately $50 billion and severely challenged response capabilities. In such large spills optimizing a coordinated response is a particular challenge. This challenge can be met in a revolutionary new way by using an objectively optimized Cyber Physical Decision Making System (CPS) for rapid response products and a framework for objectively optimized decision-making in an uncertain environment. The CPS utilizes machine learning for the processing of the massive real-time streams of Big Data from comprehensive hyperspectral remote sensing acquired by a team of low-cost robotic aerial vehicles, providing a real-time aerial view and stream of hyperspectral imagery from the near UV to the thermal infrared, and a characterization of oil thickness, oil type and oil weathering. The objective decision making paradigm is modeled on the human brain and provides the optimal course trajectory for response vessels to achieve the most expeditious cleanup of oil spills using the available resources. In addition, oil spill cleanups often involve surface oil burns that can lead to air quality issues. The aerial vehicles comprehensively characterize air quality in real-time, streaming location, temperature, pressure, humidity, the abundance of 6 criterion pollutants (O3, CO, NO, NO2, SO2, and H2S) and the full size distribution of airborne particulates. This CPS can be readily applied to other systems in agriculture, water conversation, monitoring of stream quality, air quality, diagnosing risk of wild fires, etc..

Generalized Cartographic and Simultaneous Representation of Utility Networks for Decision-Support Systems and Crisis Management in Urban Environments

NASA Astrophysics Data System (ADS)

Becker, T.; König, G.

2015-10-01

Cartographic visualizations of crises are used to create a Common Operational Picture (COP) and enforce Situational Awareness by presenting relevant information to the involved actors. As nearly all crises affect geospatial entities, geo-data representations have to support location-specific analysis throughout the decision-making process. Meaningful cartographic presentation is needed for coordinating the activities of crisis manager in a highly dynamic situation, since operators' attention span and their spatial memories are limiting factors during the perception and interpretation process. Situational Awareness of operators in conjunction with a COP are key aspects in decision-making process and essential for making well thought-out and appropriate decisions. Considering utility networks as one of the most complex and particularly frequent required systems in urban environment, meaningful cartographic presentation of multiple utility networks with respect to disaster management do not exist. Therefore, an optimized visualization of utility infrastructure for emergency response procedures is proposed. The article will describe a conceptual approach on how to simplify, aggregate, and visualize multiple utility networks and their components to meet the requirements of the decision-making process and to support Situational Awareness.

Seasonality in communication and collective decision-making in ants.

PubMed

Stroeymeyt, N; Jordan, C; Mayer, G; Hovsepian, S; Giurfa, M; Franks, N R

2014-04-07

The ability of animals to adjust their behaviour according to seasonal changes in their ecology is crucial for their fitness. Eusocial insects display strong collective behavioural seasonality, yet the mechanisms underlying such changes are poorly understood. We show that nest preference by emigrating Temnothorax albipennis ant colonies is influenced by a season-specific modulatory pheromone that may help tune decision-making according to seasonal constraints. The modulatory pheromone triggers aversion towards low-quality nests and enhances colony cohesion in summer and autumn, but not after overwintering-in agreement with reports that field colonies split in spring and reunite in summer. Interestingly, we show that the pheromone acts by downgrading the perceived value of marked nests by informed and naive individuals. This contrasts with theories of collective intelligence, stating that accurate collective decision-making requires independent evaluation of options by individuals. The violation of independence highlighted here was accordingly shown to increase error rate during emigrations. However, this is counterbalanced by enhanced cohesion and the transmission of valuable information through the colony. Our results support recent claims that optimal decisions are not necessarily those that maximize accuracy. Other criteria-such as cohesion or reward rate-may be more relevant in animal decision-making.

Lay Consultations in Heart Failure Symptom Evaluation.

PubMed

Reeder, Katherine M; Sims, Jessica L; Ercole, Patrick M; Shetty, Shivan S; Wallendorf, Michael

2017-01-01

Lay consultations can facilitate or impede healthcare. However, little is known about how lay consultations for symptom evaluation affect treatment decision-making. The purpose of this study was to explore the role of lay consultations in symptom evaluation prior to hospitalization among patients with heart failure. Semi-structured interviews were conducted with 60 patients hospitalized for acute decompensated heart failure. Chi-square and Fisher's exact tests, along with logistic regression were used to characterize lay consultations in this sample. A large proportion of patients engaged in lay consultations for symptom evaluation and decision-making before hospitalization. Lay consultants provided attributions and advice and helped make the decision to seek medical care. Men consulted more often with their spouse than women, while women more often consulted with adult children. Findings have implications for optimizing heart failure self-management interventions, improving outcomes, and reducing hospital readmissions.

Lifecycle analysis for automobiles: Uses and limitations

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

Gaines, L.; Stodolsky, F.

There has been a recent trend toward the use of lifecycle analysis (LCA) as a decision-making tool for the automotive industry. However, the different practitioners` methods and assumptions vary widely, as do the interpretations put on the results. The lack of uniformity has been addressed by such groups as the Society of Environmental Toxicology and Chemistry (SETAC) and the International Organization for Standardization (ISO), but standardization of methodology assures neither meaningful results nor appropriate use of the results. This paper examines the types of analysis that are possible for automobiles, explains possible pitfalls to be avoided, and suggests ways thatmore » LCA can be used as part of a rational decision-making procedure. The key to performing a useful analysis is identification of the factors that will actually be used in making the decision. It makes no sense to analyze system energy use in detail if direct financial cost is to be the decision criterion. Criteria may depend on who is making the decision (consumer, producer, regulator). LCA can be used to track system performance for a variety of criteria, including emissions, energy use, and monetary costs, and these can have spatial and temporal distributions. Because optimization of one parameter is likely to worsen another, identification of trade-offs is an important function of LCA.« less

The influence of number line estimation precision and numeracy on risky financial decision making.

PubMed

Park, Inkyung; Cho, Soohyun

2018-01-10

This study examined whether different aspects of mathematical proficiency influence one's ability to make adaptive financial decisions. "Numeracy" refers to the ability to process numerical and probabilistic information and is commonly reported as an important factor which contributes to financial decision-making ability. The precision of mental number representation (MNR), measured with the number line estimation (NLE) task has been reported to be another critical factor. This study aimed to examine the contribution of these mathematical proficiencies while controlling for the influence of fluid intelligence, math anxiety and personality factors. In our decision-making task, participants chose between two options offering probabilistic monetary gain or loss. Sensitivity to expected value was measured as an index for the ability to discriminate between optimal versus suboptimal options. Partial correlation and hierarchical regression analyses revealed that NLE precision better explained EV sensitivity compared to numeracy, after controlling for all covariates. These results suggest that individuals with more precise MNR are capable of making more rational financial decisions. We also propose that the measurement of "numeracy," which is commonly used interchangeably with general mathematical proficiency, should include more diverse aspects of mathematical cognition including basic understanding of number magnitude. © 2018 International Union of Psychological Science.

Effort-Based Decision Making: A Novel Approach for Assessing Motivation in Schizophrenia

PubMed Central

Green, Michael F.; Horan, William P.; Barch, Deanna M.; Gold, James M.

2015-01-01

Because negative symptoms, including motivational deficits, are a critical unmet need in schizophrenia, there are many ongoing efforts to develop new pharmacological and psychosocial interventions for these impairments. A common challenge of these studies involves how to evaluate and select optimal endpoints. Currently, all studies of negative symptoms in schizophrenia depend on ratings from clinician-conducted interviews. Effort-based decision-making tasks may provide a more objective, and perhaps more sensitive, endpoint for trials of motivational negative symptoms. These tasks assess how much effort a person is willing to exert for a given level of reward. This area has been well-studied with animal models of effort and motivation, and effort-based decision-making tasks have been adapted for use in humans. Very recently, several studies have examined physical and cognitive types of effort-based decision-making tasks in cross-sectional studies of schizophrenia, providing evidence for effort-related impairment in this illness. This article covers the theoretical background on effort-based decision-making tasks to provide a context for the subsequent articles in this theme section. In addition, we review the existing literature of studies using these tasks in schizophrenia, consider some practical challenges in adapting them for use in clinical trials in schizophrenia, and discuss interpretive challenges that are central to these types of tasks. PMID:26089350

Criteria for Drug Reimbursement Decision-Making: An Emerging Public Health Challenge in Bulgaria

PubMed Central

Iskrov, Georgi; Stefanov, Rumen

2016-01-01

Background: During times of fiscal austerity, means of reimbursement decision-making are of particular interest for public health theory and practice. Introduction of advanced health technologies, growing health expenditures and increased public scrutiny over drug reimbursement decisions have pushed governments to consider mechanisms that promote the use of effective health technologies, while constraining costs. Aims: The study’s aim was to explore the current rationale of the drug reimbursement decision-making framework in Bulgaria. Our pilot research focused on one particular component of this process – the criteria used – because of the critical role that criteria are known to have in setting budgets and priorities in the field of public health. The analysis pursued two objectives: to identify important criteria relevant to drug reimbursement decision-making and to unveil relationships between theory and practice. Study Design: Cross-sectional study. Methods: The study was realized through a closed-ended survey on reimbursement criteria among four major public health stakeholders – medical professionals, patients, health authorities, and industry. Empirical outcomes were then cross-compared with the theoretical framework, as defined by current Bulgarian public health legislation. Analysis outlined what is done and what needs to be done in the field of public health reimbursement decision-making. Results: Bulgarian public health stakeholders agreed on 15 criteria to form a tentative optimal framework for drug reimbursement decision-making. The most apparent gap between the empirically found preferences and the official legislation is the lack of consideration for the strength of evidence in reimbursement decisions. Conclusion: Bulgarian policy makers need to address specific gaps, such as formal consideration for strength of evidence, explicit role of efficiency criteria, and means to effectively empower patient and citizen involvement in public health decision-making. Drug reimbursement criteria have to be integrated into legitimate public health decision support tools that ensure the achievement of national public health objectives. These recommendations could be expanded to all Eastern European countries who share common public health problems. PMID:26966615

The doctor-patient relationship as a toolkit for uncertain clinical decisions.

PubMed

Diamond-Brown, Lauren

2016-06-01

Medical uncertainty is a well-recognized problem in healthcare, yet how doctors make decisions in the face of uncertainty remains to be understood. This article draws on interdisciplinary literature on uncertainty and physician decision-making to examine a specific physician response to uncertainty: using the doctor-patient relationship as a toolkit. Additionally, I ask what happens to this process when the doctor-patient relationship becomes fragmented. I answer these questions by examining obstetrician-gynecologists' narratives regarding how they make decisions when faced with uncertainty in childbirth. Between 2013 and 2014, I performed 21 semi-structured interviews with obstetricians in the United States. Obstetricians were selected to maximize variation in relevant physician, hospital, and practice characteristics. I began with grounded theory and moved to analytical coding of themes in relation to relevant literature. My analysis renders it evident that some physicians use the doctor-patient relationship as a toolkit for dealing with uncertainty. I analyze how this process varies for physicians in different models of care by comparing doctors' experiences in models with continuous versus fragmented doctor-patient relationships. My key findings are that obstetricians in both models appealed to the ideal of patient-centered decision-making to cope with uncertain decisions, but in practice physicians in fragmented care faced a number of challenges to using the doctor-patient relationship as a toolkit for decision-making. These challenges led to additional uncertainties and in some cases to poor outcomes for doctors and/or patients; they also raised concerns about the reproduction of inequality. Thus organization of care delivery mitigates the efficacy of doctors' use of the doctor-patient relationship toolkit for uncertain decisions. These findings have implications for theorizing about decision-making under conditions of medical uncertainty, for understanding how the doctor-patient relationship and model of care affect physician decision-making, and for forming policy on the optimal structure of medical work. Copyright © 2016 Elsevier Ltd. All rights reserved.

A conceptual evolutionary aseismic decision support framework for hospitals

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

Heuristic decomposition for non-hierarchic systems

NASA Technical Reports Server (NTRS)

Bloebaum, Christina L.; Hajela, P.

1991-01-01

Design and optimization is substantially more complex in multidisciplinary and large-scale engineering applications due to the existing inherently coupled interactions. The paper introduces a quasi-procedural methodology for multidisciplinary optimization that is applicable for nonhierarchic systems. The necessary decision-making support for the design process is provided by means of an embedded expert systems capability. The method employs a decomposition approach whose modularity allows for implementation of specialized methods for analysis and optimization within disciplines.

Risk-based decision making to manage water quality failures caused by combined sewer overflows

NASA Astrophysics Data System (ADS)

Sriwastava, A. K.; Torres-Matallana, J. A.; Tait, S.; Schellart, A.

2017-12-01

Regulatory authorities set certain environmental permit for water utilities such that the combined sewer overflows (CSO) managed by these companies conform to the regulations. These utility companies face the risk of paying penalty or negative publicity in case they breach the environmental permit. These risks can be addressed by designing appropriate solutions such as investing in additional infrastructure which improve the system capacity and reduce the impact of CSO spills. The performance of these solutions is often estimated using urban drainage models. Hence, any uncertainty in these models can have a significant effect on the decision making process. This study outlines a risk-based decision making approach to address water quality failure caused by CSO spills. A calibrated lumped urban drainage model is used to simulate CSO spill quality in Haute-Sûre catchment in Luxembourg. Uncertainty in rainfall and model parameters is propagated through Monte Carlo simulations to quantify uncertainty in the concentration of ammonia in the CSO spill. A combination of decision alternatives such as the construction of a storage tank at the CSO and the reduction in the flow contribution of catchment surfaces are selected as planning measures to avoid the water quality failure. Failure is defined as exceedance of a concentration-duration based threshold based on Austrian emission standards for ammonia (De Toffol, 2006) with a certain frequency. For each decision alternative, uncertainty quantification results into a probability distribution of the number of annual CSO spill events which exceed the threshold. For each alternative, a buffered failure probability as defined in Rockafellar & Royset (2010), is estimated. Buffered failure probability (pbf) is a conservative estimate of failure probability (pf), however, unlike failure probability, it includes information about the upper tail of the distribution. A pareto-optimal set of solutions is obtained by performing mean- pbf optimization. The effectiveness of using buffered failure probability compared to the failure probability is tested by comparing the solutions obtained by using mean-pbf and mean-pf optimizations.

Incorporating uncertainty of management costs in sensitivity analyses of matrix population models.

PubMed

Salomon, Yacov; McCarthy, Michael A; Taylor, Peter; Wintle, Brendan A

2013-02-01

The importance of accounting for economic costs when making environmental-management decisions subject to resource constraints has been increasingly recognized in recent years. In contrast, uncertainty associated with such costs has often been ignored. We developed a method, on the basis of economic theory, that accounts for the uncertainty in population-management decisions. We considered the case where, rather than taking fixed values, model parameters are random variables that represent the situation when parameters are not precisely known. Hence, the outcome is not precisely known either. Instead of maximizing the expected outcome, we maximized the probability of obtaining an outcome above a threshold of acceptability. We derived explicit analytical expressions for the optimal allocation and its associated probability, as a function of the threshold of acceptability, where the model parameters were distributed according to normal and uniform distributions. To illustrate our approach we revisited a previous study that incorporated cost-efficiency analyses in management decisions that were based on perturbation analyses of matrix population models. Incorporating derivations from this study into our framework, we extended the model to address potential uncertainties. We then applied these results to 2 case studies: management of a Koala (Phascolarctos cinereus) population and conservation of an olive ridley sea turtle (Lepidochelys olivacea) population. For low aspirations, that is, when the threshold of acceptability is relatively low, the optimal strategy was obtained by diversifying the allocation of funds. Conversely, for high aspirations, the budget was directed toward management actions with the highest potential effect on the population. The exact optimal allocation was sensitive to the choice of uncertainty model. Our results highlight the importance of accounting for uncertainty when making decisions and suggest that more effort should be placed on understanding the distributional characteristics of such uncertainty. Our approach provides a tool to improve decision making. © 2013 Society for Conservation Biology.

Shared clinical decision making

PubMed Central

AlHaqwi, Ali I.; AlDrees, Turki M.; AlRumayyan, Ahmad; AlFarhan, Ali I.; Alotaibi, Sultan S.; AlKhashan, Hesham I.; Badri, Motasim

2015-01-01

Objectives: To determine preferences of patients regarding their involvement in the clinical decision making process and the related factors in Saudi Arabia. Methods: This cross-sectional study was conducted in a major family practice center in King Abdulaziz Medical City, Riyadh, Saudi Arabia, between March and May 2012. Multivariate multinomial regression models were fitted to identify factors associated with patients preferences. Results: The study included 236 participants. The most preferred decision-making style was shared decision-making (57%), followed by paternalistic (28%), and informed consumerism (14%). The preference for shared clinical decision making was significantly higher among male patients and those with higher level of education, whereas paternalism was significantly higher among older patients and those with chronic health conditions, and consumerism was significantly higher in younger age groups. In multivariate multinomial regression analysis, compared with the shared group, the consumerism group were more likely to be female [adjusted odds ratio (AOR) =2.87, 95% confidence interval [CI] 1.31-6.27, p=0.008] and non-dyslipidemic (AOR=2.90, 95% CI: 1.03-8.09, p=0.04), and the paternalism group were more likely to be older (AOR=1.03, 95% CI: 1.01-1.05, p=0.04), and female (AOR=2.47, 95% CI: 1.32-4.06, p=0.008). Conclusion: Preferences of patients for involvement in the clinical decision-making varied considerably. In our setting, underlying factors that influence these preferences identified in this study should be considered and tailored individually to achieve optimal treatment outcomes. PMID:26620990

Value of information analysis in healthcare: a review of principles and applications.

PubMed

Tuffaha, Haitham W; Gordon, Louisa G; Scuffham, Paul A

2014-06-01

Economic evaluations are increasingly utilized to inform decisions in healthcare; however, decisions remain uncertain when they are not based on adequate evidence. Value of information (VOI) analysis has been proposed as a systematic approach to measure decision uncertainty and assess whether there is sufficient evidence to support new technologies. The objective of this paper is to review the principles and applications of VOI analysis in healthcare. Relevant databases were systematically searched to identify VOI articles. The findings from the selected articles were summarized and narratively presented. Various VOI methods have been developed and applied to inform decision-making, optimally designing research studies and setting research priorities. However, the application of this approach in healthcare remains limited due to technical and policy challenges. There is a need to create more awareness about VOI analysis, simplify its current methods, and align them with the needs of decision-making organizations.

Combined monitoring, decision and control model for the human operator in a command and control desk

NASA Technical Reports Server (NTRS)

Muralidharan, R.; Baron, S.

1978-01-01

A report is given on the ongoing efforts to mode the human operator in the context of the task during the enroute/return phases in the ground based control of multiple flights of remotely piloted vehicles (RPV). The approach employed here uses models that have their analytical bases in control theory and in statistical estimation and decision theory. In particular, it draws heavily on the modes and the concepts of the optimal control model (OCM) of the human operator. The OCM is being extended into a combined monitoring, decision, and control model (DEMON) of the human operator by infusing decision theoretic notions that make it suitable for application to problems in which human control actions are infrequent and in which monitoring and decision-making are the operator's main activities. Some results obtained with a specialized version of DEMON for the RPV control problem are included.

Motivation alters response bias and neural activation patterns in a perceptual decision-making task.

PubMed

Reckless, G E; Bolstad, I; Nakstad, P H; Andreassen, O A; Jensen, J

2013-05-15

Motivation has been demonstrated to affect individuals' response strategies in economic decision-making, however, little is known about how motivation influences perceptual decision-making behavior or its related neural activity. Given the important role motivation plays in shaping our behavior, a better understanding of this relationship is needed. A block-design, continuous performance, perceptual decision-making task where participants were asked to detect a picture of an animal among distractors was used during functional magnetic resonance imaging (fMRI). The effect of positive and negative motivation on sustained activity within regions of the brain thought to underlie decision-making was examined by altering the monetary contingency associated with the task. In addition, signal detection theory was used to investigate the effect of motivation on detection sensitivity, response bias and response time. While both positive and negative motivation resulted in increased sustained activation in the ventral striatum, fusiform gyrus, left dorsolateral prefrontal cortex (DLPFC) and ventromedial prefrontal cortex, only negative motivation resulted in the adoption of a more liberal, closer to optimal response bias. This shift toward a liberal response bias correlated with increased activation in the left DLPFC, but did not result in improved task performance. The present findings suggest that motivation alters aspects of the way perceptual decisions are made. Further, this altered response behavior is reflected in a change in left DLPFC activation, a region involved in the computation of perceptual decisions. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

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

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.

Supervision of dynamic systems: Monitoring, decision-making and control

NASA Technical Reports Server (NTRS)

White, T. N.

1982-01-01

Effects of task variables on the performance of the human supervisor by means of modelling techniques are discussed. The task variables considered are: The dynamics of the system, the task to be performed, the environmental disturbances and the observation noise. A relationship between task variables and parameters of a supervisory model is assumed. The model consists of three parts: (1) The observer part is thought to be a full order optimal observer, (2) the decision-making part is stated as a set of decision rules, and (3) the controller part is given by a control law. The observer part generates, on the basis of the system output and the control actions, an estimate of the state of the system and its associated variance. The outputs of the observer part are then used by the decision-making part to determine the instants in time of the observation actions on the one hand and the controls actions on the other. The controller part makes use of the estimated state to derive the amplitude(s) of the control action(s).

Diverse decisions. How culture affects ethical decision making.

PubMed

Wright, F; Cohen, S; Caroselli, C

1997-03-01

Even under optimal conditions, assisting patients and families in making ethical decisions is difficult at best. Often these decisions revolve around the end-of-life issues that require acknowledgement that the patient is unlikely to survive, which may be perceived as a failure to both the family and the staff. At the very least, it can be a sad time, fraught with uncertainty and indecision. When these difficulties are coupled with ineffective communication related to cultural insensitivity or unawareness, the effects can be devastating to the decision-making process. All CCNs are expected to master the skills necessary for assisting patients and families through the harrowing experience of life-threatening illness. Whereas much of critical care focuses on managing pathophysiologic disturbances, emotional needs are equally important. It follows then that the CCN must assume responsibility for assisting patients and families in coping with the crisis of critical illness and working through ethical issues, which often include end-of-life decisions and organ donation. Culturally competent care is required when addressing patient needs holistically, but it is so much more. It is an opportunity to enrich and deepen the CCN/patient/family relationship, advocate for the patient, and broaden the opportunities for communication among staff. This article has provided some beginning steps for increasing nursing cultural awareness and has offered some initial strategies to consider when designing a plan of care. Through continuing efforts, CCNs and organizations can do much to decrease the alienation that many patients and families have traditionally encountered in the CCU, an estrangement that is exacerbated when their culture is different from the predominant culture of the unit. The effort to become more culturally aware may appear to require extraordinary effort; however, the rewards of optimizing patient care are unsurpassed.

Siting and Routing Assessment for Solid Waste Management Under Uncertainty Using the Grey Mini-Max Regret Criterion

NASA Astrophysics Data System (ADS)

Chang, Ni-Bin; Davila, Eric

2006-10-01

Solid waste management (SWM) is at the forefront of environmental concerns in the Lower Rio Grande Valley (LRGV), South Texas. The complexity in SWM drives area decision makers to look for innovative and forward-looking solutions to address various waste management options. In decision analysis, it is not uncommon for decision makers to go by an option that may minimize the maximum regret when some determinant factors are vague, ambiguous, or unclear. This article presents an innovative optimization model using the grey mini-max regret (GMMR) integer programming algorithm to outline an optimal regional coordination of solid waste routing and possible landfill/incinerator construction under an uncertain environment. The LRGV is an ideal location to apply the GMMR model for SWM planning because of its constant urban expansion, dwindling landfill space, and insufficient data availability signifying the planning uncertainty combined with vagueness in decision-making. The results give local decision makers hedged sets of options that consider various forms of systematic and event-based uncertainty. By extending the dimension of decision-making, this may lead to identifying a variety of beneficial solutions with efficient waste routing and facility siting for the time frame of 2005 through 2010 in LRGV. The results show the ability of the GMMR model to open insightful scenario planning that can handle situational and data-driven uncertainty in a way that was previously unavailable. Research findings also indicate that the large capital investment of incineration facilities makes such an option less competitive among municipal options for landfills. It is evident that the investment from a municipal standpoint is out of the question, but possible public-private partnerships may alleviate this obstacle.

Reinforcement learning and decision making in monkeys during a competitive game.

PubMed

Lee, Daeyeol; Conroy, Michelle L; McGreevy, Benjamin P; Barraclough, Dominic J

2004-12-01

Animals living in a dynamic environment must adjust their decision-making strategies through experience. To gain insights into the neural basis of such adaptive decision-making processes, we trained monkeys to play a competitive game against a computer in an oculomotor free-choice task. The animal selected one of two visual targets in each trial and was rewarded only when it selected the same target as the computer opponent. To determine how the animal's decision-making strategy can be affected by the opponent's strategy, the computer opponent was programmed with three different algorithms that exploited different aspects of the animal's choice and reward history. When the computer selected its targets randomly with equal probabilities, animals selected one of the targets more often, violating the prediction of probability matching, and their choices were systematically influenced by the choice history of the two players. When the computer exploited only the animal's choice history but not its reward history, animal's choice became more independent of its own choice history but was still related to the choice history of the opponent. This bias was substantially reduced, but not completely eliminated, when the computer used the choice history of both players in making its predictions. These biases were consistent with the predictions of reinforcement learning, suggesting that the animals sought optimal decision-making strategies using reinforcement learning algorithms.

Précis of Simple heuristics that make us smart.

PubMed

Todd, P M; Gigerenzer, G

2000-10-01

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

Development of an Optimal Water Allocation Decision Tool for the Major Crops During the Water Deficit Period in the Southeast U.S.

NASA Technical Reports Server (NTRS)

Paudel, Krishna P.; Limaye, Ashutosh; Hatch, Upton; Cruise, James; Musleh, Fuad

2005-01-01

We developed a dynamic model to optimize irrigation application in three major crops (corn, cotton and peanuts) grown in the Southeast USA. Water supply amount is generated from an engineering model which is then combined with economic models to find the optimal amount of irrigation water to apply on each crop field during the six critical water deficit weeks in summer. Results indicate that water is applied on the crop with the highest marginal value product of irrigation. Decision making tool such as the one developed here would help farmers and policy makers to find the maximum profitable solution when water shortage is a serious concern.

Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, Part I: main content.

PubMed

Orellana, Liliana; Rotnitzky, Andrea; Robins, James M

2010-01-01

Dynamic treatment regimes are set rules for sequential decision making based on patient covariate history. Observational studies are well suited for the investigation of the effects of dynamic treatment regimes because of the variability in treatment decisions found in them. This variability exists because different physicians make different decisions in the face of similar patient histories. In this article we describe an approach to estimate the optimal dynamic treatment regime among a set of enforceable regimes. This set is comprised by regimes defined by simple rules based on a subset of past information. The regimes in the set are indexed by a Euclidean vector. The optimal regime is the one that maximizes the expected counterfactual utility over all regimes in the set. We discuss assumptions under which it is possible to identify the optimal regime from observational longitudinal data. Murphy et al. (2001) developed efficient augmented inverse probability weighted estimators of the expected utility of one fixed regime. Our methods are based on an extension of the marginal structural mean model of Robins (1998, 1999) which incorporate the estimation ideas of Murphy et al. (2001). Our models, which we call dynamic regime marginal structural mean models, are specially suitable for estimating the optimal treatment regime in a moderately small class of enforceable regimes of interest. We consider both parametric and semiparametric dynamic regime marginal structural models. We discuss locally efficient, double-robust estimation of the model parameters and of the index of the optimal treatment regime in the set. In a companion paper in this issue of the journal we provide proofs of the main results.

Vulnerable patients' perceptions of health care quality and quality data.

PubMed

Raven, Maria Catherine; Gillespie, Colleen C; DiBennardo, Rebecca; Van Busum, Kristin; Elbel, Brian

2012-01-01

Little is known about how patients served by safety-net hospitals utilize and respond to hospital quality data. To understand how vulnerable, lower income patients make health care decisions and define quality of care and whether hospital quality data factor into such decisions and definitions. Mixed quantitative and qualitative methods were used to gather primary data from patients at an urban, tertiary-care safety-net hospital. The study hospital is a member of the first public hospital system to voluntarily post hospital quality data online for public access. Patients were recruited from outpatient and inpatient clinics. Surveys were used to collect data on participants' sociodemographic characteristics, health literacy, health care experiences, and satisfaction variables. Focus groups were used to explore a representative sample of 24 patients' health care decision making and views of quality. Data from focus group transcripts were iteratively coded and analyzed by the authors. Focus group participants were similar to the broader diverse, low-income clinic. Participants reported exercising choice in making decisions about where to seek health care. Multiple sources influenced decision-making processes including participants' own beliefs and values, social influences, and prior experiences. Hospital quality data were notably absent as a source of influence in health care decision making for this population largely because participants were unaware of its existence. Participants' views of hospital quality were influenced by the quality and efficiency of services provided (with an emphasis on the doctor-patient relationship) and patient centeredness. When presented with it, patients appreciated the hospital quality data and, with guidance, were interested in incorporating it into health care decision making. Results suggest directions for optimizing the presentation, content, and availability of hospital quality data. Future research will explore how similar populations form and make choices based on presentation of hospital quality data.

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

PubMed Central

Bourjaily, Mark A.

2012-01-01

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

Development Optimization and Uncertainty Analysis Methods for Oil and Gas Reservoirs

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

Ettehadtavakkol, Amin, E-mail: amin.ettehadtavakkol@ttu.edu; Jablonowski, Christopher; Lake, Larry

Uncertainty complicates the development optimization of oil and gas exploration and production projects, but methods have been devised to analyze uncertainty and its impact on optimal decision-making. This paper compares two methods for development optimization and uncertainty analysis: Monte Carlo (MC) simulation and stochastic programming. Two example problems for a gas field development and an oilfield development are solved and discussed to elaborate the advantages and disadvantages of each method. Development optimization involves decisions regarding the configuration of initial capital investment and subsequent operational decisions. Uncertainty analysis involves the quantification of the impact of uncertain parameters on the optimum designmore » concept. The gas field development problem is designed to highlight the differences in the implementation of the two methods and to show that both methods yield the exact same optimum design. The results show that both MC optimization and stochastic programming provide unique benefits, and that the choice of method depends on the goal of the analysis. While the MC method generates more useful information, along with the optimum design configuration, the stochastic programming method is more computationally efficient in determining the optimal solution. Reservoirs comprise multiple compartments and layers with multiphase flow of oil, water, and gas. We present a workflow for development optimization under uncertainty for these reservoirs, and solve an example on the design optimization of a multicompartment, multilayer oilfield development.« less

Developmental telomere attrition predicts impulsive decision-making in adult starlings

PubMed Central

Bateson, Melissa; Brilot, Ben O.; Gillespie, Robert; Monaghan, Pat; Nettle, Daniel

2015-01-01

Animals in a poor biological state face reduced life expectancy, and as a consequence should make decisions that prioritize immediate survival and reproduction over long-term benefits. We tested the prediction that if, as has been suggested, developmental telomere attrition is a biomarker of state and future life expectancy, then individuals who have undergone greater developmental telomere attrition should display greater choice impulsivity as adults. We measured impulsive decision-making in a cohort of European starlings (Sturnus vulgaris) in which we had previously manipulated developmental telomere attrition by cross-fostering sibling chicks into broods of different sizes. We show that as predicted by state-dependent optimality models, individuals who had sustained greater developmental telomere attrition and who had shorter current telomeres made more impulsive foraging decisions as adults, valuing smaller, sooner food rewards more highly than birds with less attrition and longer telomeres. Our findings shed light on the biological embedding of early adversity and support a functional explanation for its consequences that could be applicable to other species, including humans. PMID:25473012

[Decision process in a multidisciplinary cancer team with limited evidence].

PubMed

Lassalle, R; Marold, J; Schöbel, M; Manzey, D; Bohn, S; Dietz, A; Boehm, A

2014-04-01

The Head and Neck Cancer Tumor Board is a multispeciality comprehensive conference that brings together experts with different backgrounds to make group decisions about the appropriate treatment. Due to the complexity of the patient cases and the collaboration of different medical disciplines most of these decisions have to be made under uncertainty, i. e., with-out knowing all relevant factors and without being quite sure about the outcome. To develop effective team decision making under uncertainty, it is necessary to understand how medical experts perceive and handle uncertainties. The aim of this field study was to develop a knowledge base by exploring additionally the factors that influence group decision making processes. A structured nonparticipant observational study was employed to address the research goal. Video data were analyzed by 2 independent observers using an observation checklist. A total of 20 videotaped case discussions were studied. Observations were complemented by a questionnaire gathering subjective evaluations of board members about the process and quality of their decisions (N=15). The results show that uncertainty is recognized by board members. Reasons for uncertainty may stem from the complexity of the cases (e. g. therapy options) or the assessment from different disciplines coming together at the board. With respect to handling uncertainty and guaranteeing an optimal decision making process potential for improvement could be defined. This pertains to the handling of different levels of competence, the promotion of a positive discussion culture as well as structuring of the decision making process. © Georg Thieme Verlag KG Stuttgart · New York.

Humans Optimize Decision-Making by Delaying Decision Onset

PubMed Central

Teichert, Tobias; Ferrera, Vincent P.; Grinband, Jack

2014-01-01

Why do humans make errors on seemingly trivial perceptual decisions? It has been shown that such errors occur in part because the decision process (evidence accumulation) is initiated before selective attention has isolated the relevant sensory information from salient distractors. Nevertheless, it is typically assumed that subjects increase accuracy by prolonging the decision process rather than delaying decision onset. To date it has not been tested whether humans can strategically delay decision onset to increase response accuracy. To address this question we measured the time course of selective attention in a motion interference task using a novel variant of the response signal paradigm. Based on these measurements we estimated time-dependent drift rate and showed that subjects should in principle be able trade speed for accuracy very effectively by delaying decision onset. Using the time-dependent estimate of drift rate we show that subjects indeed delay decision onset in addition to raising response threshold when asked to stress accuracy over speed in a free reaction version of the same motion-interference task. These findings show that decision onset is a critical aspect of the decision process that can be adjusted to effectively improve decision accuracy. PMID:24599295

The Effects of a Concurrent Task on Human Optimization and Self Control

ERIC Educational Resources Information Center

Reed, Phil; Thompson, Caitlin; Osborne, Lisa A.; McHugh, Louise

2011-01-01

Memory deficits have been shown to hamper decision making in a number of populations. In two experiments, participants were required to select one of three alternatives that varied in reinforcer amount and delay, and the effect of a concurrent task on a behavioral choice task that involved making either an impulsive, self-controlled, or optimal…

A Signal-Detection Analysis of Fast-and-Frugal Trees

ERIC Educational Resources Information Center

Luan, Shenghua; Schooler, Lael J.; Gigerenzer, Gerd

2011-01-01

Models of decision making are distinguished by those that aim for an optimal solution in a world that is precisely specified by a set of assumptions (a so-called "small world") and those that aim for a simple but satisfactory solution in an uncertain world where the assumptions of optimization models may not be met (a so-called "large world"). Few…

'Heads you win, tails I lose': a critical incident study of GPs' decisions about emergency admission referrals.

PubMed

Dempsey, Owen P; Bekker, Hilary L

2002-12-01

Acute hospital Trusts' inability to cope with the numbers of emergency admissions has led to the production of guidelines by the Department of Health aimed at reducing inappropriate admissions by GPs. There is a paucity of research describing GPs' decisions to (not) admit patients and it is unclear how effective these guidelines are in changing these practices. To describe GPs' decision-making about referrals for emergency hospital admissions. Observational design using the critical incident technique to elicit data. Eight GPs in West Yorkshire recorded details of memorable emergency admission decisions, both prospective and retrospective consultations. The transcript data were classified by theme using NUD*IST. Forty prospective and 8 retrospective consultations were analysed. Factors affecting GPs' decisions were:Identification of all consequences for all stakeholders in the decision. Emotional impact on the GP of managing these conflicting needs. 'Peer review' of the GP's professionalism about the decision. Contextual pressures limiting effectiveness of GPs' decision-making. Referral decisions require the evaluation of several conflicting consequences for many stakeholders in time-pressured and peer-reviewed situations. These factors encourage the use of heuristics, i.e. GPs' judgements will be influenced more by the social context of the choice than information about the patient's condition. Emergency referral guidelines provide more information to evaluate from another stakeholder; introducing guidelines is likely to increase GPs' use of heuristics and the making of less optimal decisions.

Melioration as rational choice: sequential decision making in uncertain environments.

PubMed

Sims, Chris R; Neth, Hansjörg; Jacobs, Robert A; Gray, Wayne D

2013-01-01

Melioration-defined as choosing a lesser, local gain over a greater longer term gain-is a behavioral tendency that people and pigeons share. As such, the empirical occurrence of meliorating behavior has frequently been interpreted as evidence that the mechanisms of human choice violate the norms of economic rationality. In some environments, the relationship between actions and outcomes is known. In this case, the rationality of choice behavior can be evaluated in terms of how successfully it maximizes utility given knowledge of the environmental contingencies. In most complex environments, however, the relationship between actions and future outcomes is uncertain and must be learned from experience. When the difficulty of this learning challenge is taken into account, it is not evident that melioration represents suboptimal choice behavior. In the present article, we examine human performance in a sequential decision-making experiment that is known to induce meliorating behavior. In keeping with previous results using this paradigm, we find that the majority of participants in the experiment fail to adopt the optimal decision strategy and instead demonstrate a significant bias toward melioration. To explore the origins of this behavior, we develop a rational analysis (Anderson, 1990) of the learning problem facing individuals in uncertain decision environments. Our analysis demonstrates that an unbiased learner would adopt melioration as the optimal response strategy for maximizing long-term gain. We suggest that many documented cases of melioration can be reinterpreted not as irrational choice but rather as globally optimal choice under uncertainty.

Solving multi-objective optimization problems in conservation with the reference point method

PubMed Central

Dujardin, Yann; Chadès, Iadine

2018-01-01

Managing the biodiversity extinction crisis requires wise decision-making processes able to account for the limited resources available. In most decision problems in conservation biology, several conflicting objectives have to be taken into account. Most methods used in conservation either provide suboptimal solutions or use strong assumptions about the decision-maker’s preferences. Our paper reviews some of the existing approaches to solve multi-objective decision problems and presents new multi-objective linear programming formulations of two multi-objective optimization problems in conservation, allowing the use of a reference point approach. Reference point approaches solve multi-objective optimization problems by interactively representing the preferences of the decision-maker with a point in the criteria (objectives) space, called the reference point. We modelled and solved the following two problems in conservation: a dynamic multi-species management problem under uncertainty and a spatial allocation resource management problem. Results show that the reference point method outperforms classic methods while illustrating the use of an interactive methodology for solving combinatorial problems with multiple objectives. The method is general and can be adapted to a wide range of ecological combinatorial problems. PMID:29293650

Multi-objective optimization of solid waste flows: environmentally sustainable strategies for municipalities.

PubMed

Minciardi, Riccardo; Paolucci, Massimo; Robba, Michela; Sacile, Roberto

2008-11-01

An approach to sustainable municipal solid waste (MSW) management is presented, with the aim of supporting the decision on the optimal flows of solid waste sent to landfill, recycling and different types of treatment plants, whose sizes are also decision variables. This problem is modeled with a non-linear, multi-objective formulation. Specifically, four objectives to be minimized have been taken into account, which are related to economic costs, unrecycled waste, sanitary landfill disposal and environmental impact (incinerator emissions). An interactive reference point procedure has been developed to support decision making; these methods are considered appropriate for multi-objective decision problems in environmental applications. In addition, interactive methods are generally preferred by decision makers as they can be directly involved in the various steps of the decision process. Some results deriving from the application of the proposed procedure are presented. The application of the procedure is exemplified by considering the interaction with two different decision makers who are assumed to be in charge of planning the MSW system in the municipality of Genova (Italy).

Postnatal Psychosocial Assessment and Clinical Decision-Making, a Descriptive Study.

PubMed

Sims, Deborah; Fowler, Cathrine

2018-05-18

The aim of this study is to describe experienced child and family health nurses' clinical decision-making during a postnatal psychosocial assessment. Maternal emotional wellbeing in the postnatal year optimises parenting and promotes infant development. Psychosocial assessment potentially enables early intervention and reduces the risk of a mental disorder occurring during this time of change. Assessment accuracy, and the interventions used are determined by the standard of nursing decision-making. A qualitative methodology was employed to explore decision-making behaviour when conducting a postnatal psychosocial assessment. This study was conducted in an Australian early parenting organisation. Twelve experienced child and family health nurses were interviewed. A detailed description of a postnatal psychosocial assessment process was obtained using a critical incident technique. Template analysis was used to determine the information domains the nurses accessed, and content analysis was used to determine the nurses' thinking strategies, to make clinical decisions from this assessment. The nurses described 24 domains of information and used 17 thinking strategies, in a variety of combinations. The four information domains most commonly used were parenting, assessment tools, women-determined issues and sleep. The seven thinking strategies most commonly used were searching for information, forming relationships between the information, recognising a pattern, drawing a conclusion, setting priorities, providing explanations for the information and judging the value of the information. The variety and complexity of the clinical decision-making involved in postnatal psychosocial assessment confirms that the nurses use information appropriately and within their scope of nursing practice. The standard of clinical decision-making determines the results of the assessment and the optimal access to care. Knowledge of the information domains and the decision-making strategies that experienced nurses use for psychosocial assessment potentially improves practice by providing a framework for education and mentoring. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Econ's optimal decision model of wheat production and distribution-documentation

NASA Technical Reports Server (NTRS)

1977-01-01

The report documents the computer programs written to implement the ECON optical decision model. The programs were written in APL, an extremely compact and powerful language particularly well suited to this model, which makes extensive use of matrix manipulations. The algorithms used are presented and listings of and descriptive information on the APL programs used are given. Possible changes in input data are also given.

Understanding Optimal Decision-Making in Wargaming

DTIC Science & Technology

2013-10-01

beneficial outcomes from wargaming, one of which is a better understanding of the impact of decisions as a part of combat processes. However, using...under instrument flight rules ( IFR ) (Bellenkes et al., 1997; Katoh, 1997). Of note, eye-tracking technology also has been applied to investigate...Neuroscience, 7 . Skinner, A., Berka, C., Ohara-Long, L., & Sebrechts, M. (2010). Impact of Virtual En- vironment Fidelity on Behavioral and

The development of a multi-criteria decision analysis aid to help with contraceptive choices: My Contraception Tool.

PubMed

French, Rebecca S; Cowan, Frances M; Wellings, Kaye; Dowie, Jack

2014-04-01

My Contraception Tool (MCT) applies the principles of multi-criteria decision analysis to the choice of contraceptive method. Its purpose is to make the decision-making process transparent to the user and to suggest a method to them based on their own preferences. The contraceptive option that emerges as optimal from the analysis takes account of the probability of a range of outcomes and the relative weight ascribed to them by the user. The development of MCT was a collaborative project between London School of Hygiene & Tropical Medicine, Brook, FPA and Maldaba Ltd. MCT is available online via the Brook and FPA websites. In this article we describe MCT's development and how it works. Further work is needed to assess the impact it has on decision quality and contraceptive behaviour.

Using price-volume agreements to manage pharmaceutical leakage and off-label promotion.

PubMed

Zhang, Hui; Zaric, Gregory S

2015-09-01

Unapproved or "off-label" uses of prescription drugs are quite common. The extent of this use may be influenced by the promotional efforts of manufacturers. This paper investigates how a manufacturer makes promotional decisions in the presence of a price-volume agreement. We developed an optimization model in which the manufacturer maximizes its expected profit by choosing the level of marketing effort to promote uses for different indications. We considered several ways a volume threshold is determined. We also compared models in which off-label uses are reimbursed and those in which they are forbidden to illustrate the impact of off-label promotion on the optimal decisions and on the decision maker's performance. We found that the payer chooses a threshold which may be the same as the manufacturer's optimal decision. We also found that the manufacturer not only considers the promotional cost in promoting off-label uses but also considers the health benefit of off-label uses. In some situations, using a price-volume agreement to control leakage may be a better idea than simply preventing leakage without using the agreement, from a social welfare perspective.

Multiple objective optimization in reliability demonstration test

DOE PAGES

Lu, Lu; Anderson-Cook, Christine Michaela; Li, Mingyang

2016-10-01

Reliability demonstration tests are usually performed in product design or validation processes to demonstrate whether a product meets specified requirements on reliability. For binomial demonstration tests, the zero-failure test has been most commonly used due to its simplicity and use of minimum sample size to achieve an acceptable consumer’s risk level. However, this test can often result in unacceptably high risk for producers as well as a low probability of passing the test even when the product has good reliability. This paper explicitly explores the interrelationship between multiple objectives that are commonly of interest when planning a demonstration test andmore » proposes structured decision-making procedures using a Pareto front approach for selecting an optimal test plan based on simultaneously balancing multiple criteria. Different strategies are suggested for scenarios with different user priorities and graphical tools are developed to help quantify the trade-offs between choices and to facilitate informed decision making. As a result, potential impacts of some subjective user inputs on the final decision are studied to offer insights and useful guidance for general applications.« less

Collective irrationality and positive feedback.

PubMed

Nicolis, Stamatios C; Zabzina, Natalia; Latty, Tanya; Sumpter, David J T

2011-04-26

Recent experiments on ants and slime moulds have assessed the degree to which they make rational decisions when presented with a number of alternative food sources or shelter. Ants and slime moulds are just two examples of a wide range of species and biological processes that use positive feedback mechanisms to reach decisions. Here we use a generic, experimentally validated model of positive feedback between group members to show that the probability of taking the best of options depends crucially on the strength of feedback. We show how the probability of choosing the best option can be maximized by applying an optimal feedback strength. Importantly, this optimal value depends on the number of options, so that when we change the number of options the preference of the group changes, producing apparent "irrationalities". We thus reinterpret the idea that collectives show "rational" or "irrational" preferences as being a necessary consequence of the use of positive feedback. We argue that positive feedback is a heuristic which often produces fast and accurate group decision-making, but is always susceptible to apparent irrationality when studied under particular experimental conditions.

An intertemporal decision framework for electrochemical energy storage management

NASA Astrophysics Data System (ADS)

He, Guannan; Chen, Qixin; Moutis, Panayiotis; Kar, Soummya; Whitacre, Jay F.

2018-05-01

Dispatchable energy storage is necessary to enable renewable-based power systems that have zero or very low carbon emissions. The inherent degradation behaviour of electrochemical energy storage (EES) is a major concern for both EES operational decisions and EES economic assessments. Here, we propose a decision framework that addresses the intertemporal trade-offs in terms of EES degradation by deriving, implementing and optimizing two metrics: the marginal benefit of usage and the average benefit of usage. These metrics are independent of the capital cost of the EES system, and, as such, separate the value of EES use from the initial cost, which provides a different perspective on storage valuation and operation. Our framework is proved to produce the optimal solution for EES life-cycle profit maximization. We show that the proposed framework offers effective ways to assess the economic values of EES, to make investment decisions for various applications and to inform related subsidy policies.

The optimal retailer's ordering policies with trade credit financing and limited storage capacity in the supply chain system

NASA Astrophysics Data System (ADS)

Yen, Ghi-Feng; Chung, Kun-Jen; Chen, Tzung-Ching

2012-11-01

The traditional economic order quantity model assumes that the retailer's storage capacity is unlimited. However, as we all know, the capacity of any warehouse is limited. In practice, there usually exist various factors that induce the decision-maker of the inventory system to order more items than can be held in his/her own warehouse. Therefore, for the decision-maker, it is very practical to determine whether or not to rent other warehouses. In this article, we try to incorporate two levels of trade credit and two separate warehouses (own warehouse and rented warehouse) to establish a new inventory model to help the decision-maker to make the decision. Four theorems are provided to determine the optimal cycle time to generalise some existing articles. Finally, the sensitivity analysis is executed to investigate the effects of the various parameters on ordering policies and annual costs of the inventory system.

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).

Effects of emotion on prospection during decision-making.

PubMed

Worthy, Darrell A; Byrne, Kaileigh A; Fields, Sherecce

2014-01-01

In two experiments we examined the role of emotion, specifically worry, anxiety, and mood, on prospection during decision-making. Worry is a particularly relevant emotion to study in the context of prospection because high levels of worry may make individuals more aversive toward the uncertainty associated with the prospect of obtaining future improvements in rewards or states. Thus, high levels of worry might lead to reduced prospection during decision-making and enhance preference for immediate over delayed rewards. In Experiment 1 participants performed a two-choice dynamic decision-making task where they were required to choose between one option (the decreasing option) which provided larger immediate rewards but declines in future states, and another option (the increasing option) which provided smaller immediate rewards but improvements in future states, making it the optimal choice. High levels of worry were associated with poorer performance in the task. Additionally, fits of a sophisticated reinforcement-learning model that incorporated both reward-based and state-based information suggested that individuals reporting high levels of worry gave greater weight to the immediate rewards they would receive on each trial than to the degree to which each action would lead to improvements in their future state. In Experiment 2 we found that high levels of worry were associated with greater delay discounting using a standard delay discounting task. Combined, the results suggest that high levels of worry are associated with reduced prospection during decision-making. We attribute these results to high worriers' aversion toward the greater uncertainty associated with attempting to improve future rewards than to maximize immediate reward. These results have implications for researchers interested in the effects of emotion on cognition, and suggest that emotion strongly affects the focus on temporal outcomes during decision-making.

Integrating land cover modeling and adaptive management to conserve endangered species and reduce catastrophic fire risk

USGS Publications Warehouse

Breininger, David; Duncan, Brean; Eaton, Mitchell J.; Johnson, Fred; Nichols, James

2014-01-01

Land cover modeling is used to inform land management, but most often via a two-step process, where science informs how management alternatives can influence resources, and then, decision makers can use this information to make decisions. A more efficient process is to directly integrate science and decision-making, where science allows us to learn in order to better accomplish management objectives and is developed to address specific decisions. Co-development of management and science is especially productive when decisions are complicated by multiple objectives and impeded by uncertainty. Multiple objectives can be met by the specification of tradeoffs, and relevant uncertainty can be addressed through targeted science (i.e., models and monitoring). We describe how to integrate habitat and fuel monitoring with decision-making focused on the dual objectives of managing for endangered species and minimizing catastrophic fire risk. Under certain conditions, both objectives might be achieved by a similar management policy; other conditions require tradeoffs between objectives. Knowledge about system responses to actions can be informed by developing hypotheses based on ideas about fire behavior and then applying competing management actions to different land units in the same system state. Monitoring and management integration is important to optimize state-specific management decisions and to increase knowledge about system responses. We believe this approach has broad utility and identifies a clear role for land cover modeling programs intended to inform decision-making.

Spares Management : Optimizing Hardware Usage for the Space Shuttle Main Engine

NASA Technical Reports Server (NTRS)

Gulbrandsen, K. A.

1999-01-01

The complexity of the Space Shuttle Main Engine (SSME), combined with mounting requirements to reduce operations costs have increased demands for accurate tracking, maintenance, and projections of SSME assets. The SSME Logistics Team is developing an integrated asset management process. This PC-based tool provides a user-friendly asset database for daily decision making, plus a variable-input hardware usage simulation with complex logic yielding output that addresses essential asset management issues. Cycle times on critical tasks are significantly reduced. Associated costs have decreased as asset data quality and decision-making capability has increased.

Unlocking SmartWay Data for Shippers: Optimize Supply Chain Decision Making and Enhance CSR Reporting

EPA Pesticide Factsheets

This EPA presentation provides information on the SmartWay Transport Partnership Program, including SW brand market research results, program success, partner participation, logo usage, and available promotional and publicity resources.

Development of Chemical Process Design and Control for Sustainability

EPA Science Inventory

This contribution describes a novel process systems engineering framework that couples advanced control with sustainability evaluation and decision making for the optimization of process operations to minimize environmental impacts associated with products, materials, and energy....

Using Data to Optimize Community College Marketing

ERIC Educational Resources Information Center

Clagett, Craig A.

2012-01-01

Marketing is an essential component of an effective enrollment management plan. The broad mission of a comprehensive community college requires multiple, targeted communications campaigns. Institutional research can contribute to marketing success at all phases of decision making.

Maximum-likelihood soft-decision decoding of block codes using the A* algorithm

NASA Technical Reports Server (NTRS)

Ekroot, L.; Dolinar, S.

1994-01-01

The A* algorithm finds the path in a finite depth binary tree that optimizes a function. Here, it is applied to maximum-likelihood soft-decision decoding of block codes where the function optimized over the codewords is the likelihood function of the received sequence given each codeword. The algorithm considers codewords one bit at a time, making use of the most reliable received symbols first and pursuing only the partially expanded codewords that might be maximally likely. A version of the A* algorithm for maximum-likelihood decoding of block codes has been implemented for block codes up to 64 bits in length. The efficiency of this algorithm makes simulations of codes up to length 64 feasible. This article details the implementation currently in use, compares the decoding complexity with that of exhaustive search and Viterbi decoding algorithms, and presents performance curves obtained with this implementation of the A* algorithm for several codes.

Engaging Gatekeepers, Optimizing Decision Making, and Mitigating Bias: Design Specifications for Systemic Diversity Interventions.

PubMed

Vinkenburg, Claartje J

2017-06-01

In this contribution to the Journal of Applied Behavioral Science Special Issue on Understanding Diversity Dynamics in Systems: Social Equality as an Organization Change Issue, I develop and describe design specifications for systemic diversity interventions in upward mobility career systems, aimed at optimizing decision making through mitigating bias by engaging gatekeepers. These interventions address the paradox of meritocracy that underlies the surprising lack of diversity at the top of the career pyramid in these systems. I ground the design specifications in the limited empirical evidence on "what works" in systemic interventions. Specifically, I describe examples from interventions in academic settings, including a bias literacy program, participatory modeling, and participant observation. The design specifications, paired with inspirational examples of successful interventions, should assist diversity officers and consultants in designing and implementing interventions to promote the advancement to and representation of nondominant group members at the top of the organizational hierarchy.

Engaging Gatekeepers, Optimizing Decision Making, and Mitigating Bias: Design Specifications for Systemic Diversity Interventions

PubMed Central

Vinkenburg, Claartje J.

2017-01-01

In this contribution to the Journal of Applied Behavioral Science Special Issue on Understanding Diversity Dynamics in Systems: Social Equality as an Organization Change Issue, I develop and describe design specifications for systemic diversity interventions in upward mobility career systems, aimed at optimizing decision making through mitigating bias by engaging gatekeepers. These interventions address the paradox of meritocracy that underlies the surprising lack of diversity at the top of the career pyramid in these systems. I ground the design specifications in the limited empirical evidence on “what works” in systemic interventions. Specifically, I describe examples from interventions in academic settings, including a bias literacy program, participatory modeling, and participant observation. The design specifications, paired with inspirational examples of successful interventions, should assist diversity officers and consultants in designing and implementing interventions to promote the advancement to and representation of nondominant group members at the top of the organizational hierarchy. PMID:28546644

Decision-support models for empiric antibiotic selection in Gram-negative bloodstream infections.

PubMed

MacFadden, D R; Coburn, B; Shah, N; Robicsek, A; Savage, R; Elligsen, M; Daneman, N

2018-04-25

Early empiric antibiotic therapy in patients can improve clinical outcomes in Gram-negative bacteraemia. However, the widespread prevalence of antibiotic-resistant pathogens compromises our ability to provide adequate therapy while minimizing use of broad antibiotics. We sought to determine whether readily available electronic medical record data could be used to develop predictive models for decision support in Gram-negative bacteraemia. We performed a multi-centre cohort study, in Canada and the USA, of hospitalized patients with Gram-negative bloodstream infection from April 2010 to March 2015. We analysed multivariable models for prediction of antibiotic susceptibility at two empiric windows: Gram-stain-guided and pathogen-guided treatment. Decision-support models for empiric antibiotic selection were developed based on three clinical decision thresholds of acceptable adequate coverage (80%, 90% and 95%). A total of 1832 patients with Gram-negative bacteraemia were evaluated. Multivariable models showed good discrimination across countries and at both Gram-stain-guided (12 models, areas under the curve (AUCs) 0.68-0.89, optimism-corrected AUCs 0.63-0.85) and pathogen-guided (12 models, AUCs 0.75-0.98, optimism-corrected AUCs 0.64-0.95) windows. Compared to antibiogram-guided therapy, decision-support models of antibiotic selection incorporating individual patient characteristics and prior culture results have the potential to increase use of narrower-spectrum antibiotics (in up to 78% of patients) while reducing inadequate therapy. Multivariable models using readily available epidemiologic factors can be used to predict antimicrobial susceptibility in infecting pathogens with reasonable discriminatory ability. Implementation of sequential predictive models for real-time individualized empiric antibiotic decision-making has the potential to both optimize adequate coverage for patients while minimizing overuse of broad-spectrum antibiotics, and therefore requires further prospective evaluation. Readily available epidemiologic risk factors can be used to predict susceptibility of Gram-negative organisms among patients with bacteraemia, using automated decision-making models. Copyright © 2018 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Impact of stakeholders influence, geographic level and risk perception on strategic decisions in simulated foot and mouth disease epizootics in France.

PubMed

Marsot, Maud; Rautureau, Séverine; Dufour, Barbara; Durand, Benoit

2014-01-01

Comparison of control strategies against animal infectious diseases allows determining optimal strategies according to their epidemiological and/or economic impacts. However, in real life, the choice of a control strategy does not always obey a pure economic or epidemiological rationality. The objective of this study was to analyze the choice of a foot and mouth disease (FMD) control strategy as a decision-making process in which the decision-maker is influenced by several stakeholders (government, agro-food industries, public opinion). For each of these, an indicator of epizootic impact was quantified to compare seven control strategies. We then determined how, in France, the optimal control strategy varied according to the relative weights of stakeholders and to the perception of risk by the decision-maker (risk-neutral/risk-averse). When the scope of decision was national, whatever their perception of risk and the stakeholders' weights, decision-makers chose a strategy based on vaccination. This consensus concealed marked differences between regions, which were connected with the regional breeding characteristics. Vaccination-based strategies were predominant in regions with dense cattle and swine populations, and in regions with a dense population of small ruminants, combined with a medium density of cattle and swine. These differences between regions suggested that control strategies could be usefully adapted to local breeding conditions. We then analyzed the feasibility of adaptive decision-making processes depending on the date and place where the epizootic starts, or on the evolution of the epizootic over time. The initial conditions always explained at least half of the variance of impacts, the remaining variance being attributed to the variability of epizootics evolution. However, the first weeks of this evolution explained a large part of the impacts variability. Although the predictive value of the initial conditions for determining the optimal strategy was weak, adaptive strategies changing dynamically according to the evolution of the epizootic appeared feasible.

An Investigation of Generalized Differential Evolution Metaheuristic for Multiobjective Optimal Crop-Mix Planning Decision

PubMed Central

Olugbara, Oludayo

2014-01-01

This paper presents an annual multiobjective crop-mix planning as a problem of concurrent maximization of net profit and maximization of crop production to determine an optimal cropping pattern. The optimal crop production in a particular planting season is a crucial decision making task from the perspectives of economic management and sustainable agriculture. A multiobjective optimal crop-mix problem is formulated and solved using the generalized differential evolution 3 (GDE3) metaheuristic to generate a globally optimal solution. The performance of the GDE3 metaheuristic is investigated by comparing its results with the results obtained using epsilon constrained and nondominated sorting genetic algorithms—being two representatives of state-of-the-art in evolutionary optimization. The performance metrics of additive epsilon, generational distance, inverted generational distance, and spacing are considered to establish the comparability. In addition, a graphical comparison with respect to the true Pareto front for the multiobjective optimal crop-mix planning problem is presented. Empirical results generally show GDE3 to be a viable alternative tool for solving a multiobjective optimal crop-mix planning problem. PMID:24883369

Optimization applications in aircraft engine design and test

NASA Technical Reports Server (NTRS)

Pratt, T. K.

1984-01-01

Starting with the NASA-sponsored STAEBL program, optimization methods based primarily upon the versatile program COPES/CONMIN were introduced over the past few years to a broad spectrum of engineering problems in structural optimization, engine design, engine test, and more recently, manufacturing processes. By automating design and testing processes, many repetitive and costly trade-off studies have been replaced by optimization procedures. Rather than taking engineers and designers out of the loop, optimization has, in fact, put them more in control by providing sophisticated search techniques. The ultimate decision whether to accept or reject an optimal feasible design still rests with the analyst. Feedback obtained from this decision process has been invaluable since it can be incorporated into the optimization procedure to make it more intelligent. On several occasions, optimization procedures have produced novel designs, such as the nonsymmetric placement of rotor case stiffener rings, not anticipated by engineering designers. In another case, a particularly difficult resonance contraint could not be satisfied using hand iterations for a compressor blade, when the STAEBL program was applied to the problem, a feasible solution was obtained in just two iterations.

An investigation of generalized differential evolution metaheuristic for multiobjective optimal crop-mix planning decision.

PubMed

Adekanmbi, Oluwole; Olugbara, Oludayo; Adeyemo, Josiah

2014-01-01

This paper presents an annual multiobjective crop-mix planning as a problem of concurrent maximization of net profit and maximization of crop production to determine an optimal cropping pattern. The optimal crop production in a particular planting season is a crucial decision making task from the perspectives of economic management and sustainable agriculture. A multiobjective optimal crop-mix problem is formulated and solved using the generalized differential evolution 3 (GDE3) metaheuristic to generate a globally optimal solution. The performance of the GDE3 metaheuristic is investigated by comparing its results with the results obtained using epsilon constrained and nondominated sorting genetic algorithms-being two representatives of state-of-the-art in evolutionary optimization. The performance metrics of additive epsilon, generational distance, inverted generational distance, and spacing are considered to establish the comparability. In addition, a graphical comparison with respect to the true Pareto front for the multiobjective optimal crop-mix planning problem is presented. Empirical results generally show GDE3 to be a viable alternative tool for solving a multiobjective optimal crop-mix planning problem.

How to deal with climate change uncertainty in the planning of engineering systems

NASA Astrophysics Data System (ADS)

Spackova, Olga; Dittes, Beatrice; Straub, Daniel

2016-04-01

The effect of extreme events such as floods on the infrastructure and built environment is associated with significant uncertainties: These include the uncertain effect of climate change, uncertainty on extreme event frequency estimation due to limited historic data and imperfect models, and, not least, uncertainty on future socio-economic developments, which determine the damage potential. One option for dealing with these uncertainties is the use of adaptable (flexible) infrastructure that can easily be adjusted in the future without excessive costs. The challenge is in quantifying the value of adaptability and in finding the optimal sequence of decision. Is it worth to build a (potentially more expensive) adaptable system that can be adjusted in the future depending on the future conditions? Or is it more cost-effective to make a conservative design without counting with the possible future changes to the system? What is the optimal timing of the decision to build/adjust the system? We develop a quantitative decision-support framework for evaluation of alternative infrastructure designs under uncertainties, which: • probabilistically models the uncertain future (trough a Bayesian approach) • includes the adaptability of the systems (the costs of future changes) • takes into account the fact that future decisions will be made under uncertainty as well (using pre-posterior decision analysis) • allows to identify the optimal capacity and optimal timing to build/adjust the infrastructure. Application of the decision framework will be demonstrated on an example of flood mitigation planning in Bavaria.

Early Huntington's Disease: Impulse Control Deficits but Correct Judgment Regarding Risky Situations.

PubMed

Galvez, Victor; Fernandez-Ruiz, Juan; Bayliss, Leo; Ochoa-Morales, Adriana; Hernandez-Castillo, Carlos R; Díaz, Rosalinda; Campos-Romo, Aurelio

2017-01-01

Huntington's disease (HD) patients show alterations in decision making tasks. However, it is still uncertain if these deficits are due to poor judgment regarding risky situations, or to impulse control deficits. To elucidate whether decision-making in patients is related to genuine risk behavior or to impulse control deficits. To test between these two alternative possibilities, we evaluated the performance of 19 prodromal HD patients and 19 matched healthy controls in the Cambridge Gambling Task (CGT). This task assesses decision-making while dissociating between genuine risk-taking behaviors (ascending condition) from impulsive behavior (descending condition). The results showed that patients and controls had the same performance during all trials in the ascending condition, reflecting a correct judgment regarding risky situations; however, during the descending condition, patients responded before the controls in all trials, making a significantly larger number of higher bets. Unlike the control group, they did not wait for more optimal subsequent options. These results suggest impulse control deficits in HD gene carriers, but unimpaired risk-taking judgment.

Lay Consultations in Heart Failure Symptom Evaluation

PubMed Central

Reeder, Katherine M.; Sims, Jessica L.; Ercole, Patrick M.; Shetty, Shivan S.; Wallendorf, Michael

2017-01-01

Purpose Lay consultations can facilitate or impede healthcare. However, little is known about how lay consultations for symptom evaluation affect treatment decision-making. The purpose of this study was to explore the role of lay consultations in symptom evaluation prior to hospitalization among patients with heart failure. Methods Semi-structured interviews were conducted with 60 patients hospitalized for acute decompensated heart failure. Chi-square and Fisher’s exact tests, along with logistic regression were used to characterize lay consultations in this sample. Results A large proportion of patients engaged in lay consultations for symptom evaluation and decision-making before hospitalization. Lay consultants provided attributions and advice and helped make the decision to seek medical care. Men consulted more often with their spouse than women, while women more often consulted with adult children. Conclusions Findings have implications for optimizing heart failure self-management interventions, improving outcomes, and reducing hospital readmissions. PMID:29399657

Case-based ethics instruction: the influence of contextual and individual factors in case content on ethical decision-making.

PubMed

Bagdasarov, Zhanna; Thiel, Chase E; Johnson, James F; Connelly, Shane; Harkrider, Lauren N; Devenport, Lynn D; Mumford, Michael D

2013-09-01

Cases have been employed across multiple disciplines, including ethics education, as effective pedagogical tools. However, the benefit of case-based learning in the ethics domain varies across cases, suggesting that not all cases are equal in terms of pedagogical value. Indeed, case content appears to influence the extent to which cases promote learning and transfer. Consistent with this argument, the current study explored the influences of contextual and personal factors embedded in case content on ethical decision-making. Cases were manipulated to include a clear description of the social context and the goals of the characters involved. Results indicated that social context, specifically the description of an autonomy-supportive environment, facilitated execution of sense making processes and resulted in greater decision ethicality. Implications for designing optimal cases and case-based training programs are discussed.

Risk Informed Margins Management as part of Risk Informed Safety Margin Characterization

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

Curtis Smith

2014-06-01

The ability to better characterize and quantify safety margin is important to improved decision making about Light Water Reactor (LWR) design, operation, and plant life extension. A systematic approach to characterization of safety margins and the subsequent margin management options represents a vital input to the licensee and regulatory analysis and decision making that will be involved. In addition, as research and development in the LWR Sustainability (LWRS) Program and other collaborative efforts yield new data, sensors, and improved scientific understanding of physical processes that govern the aging and degradation of plant SSCs needs and opportunities to better optimize plantmore » safety and performance will become known. To support decision making related to economics, readability, and safety, the Risk Informed Safety Margin Characterization (RISMC) Pathway provides methods and tools that enable mitigation options known as risk informed margins management (RIMM) strategies.« less

System design and improvement of an emergency department using Simulation-Based Multi-Objective Optimization

NASA Astrophysics Data System (ADS)

Goienetxea Uriarte, A.; Ruiz Zúñiga, E.; Urenda Moris, M.; Ng, A. H. C.

2015-05-01

Discrete Event Simulation (DES) is nowadays widely used to support decision makers in system analysis and improvement. However, the use of simulation for improving stochastic logistic processes is not common among healthcare providers. The process of improving healthcare systems involves the necessity to deal with trade-off optimal solutions that take into consideration a multiple number of variables and objectives. Complementing DES with Multi-Objective Optimization (SMO) creates a superior base for finding these solutions and in consequence, facilitates the decision-making process. This paper presents how SMO has been applied for system improvement analysis in a Swedish Emergency Department (ED). A significant number of input variables, constraints and objectives were considered when defining the optimization problem. As a result of the project, the decision makers were provided with a range of optimal solutions which reduces considerably the length of stay and waiting times for the ED patients. SMO has proved to be an appropriate technique to support healthcare system design and improvement processes. A key factor for the success of this project has been the involvement and engagement of the stakeholders during the whole process.

Decision - making of Direct Customers Based on Available Transfer Capability

NASA Astrophysics Data System (ADS)

Quan, Tang; Zhaohang, Lin; Huaqiang, Li

2017-05-01

Large customer direct-power-purchasing is a hot spot in the electricity market reform. In this paper, the author established an Available Transfer Capability (ATC) model which takes uncertain factors into account, applied the model into large customer direct-power-purchasing transactions and improved the reliability of power supply during direct-power-purchasing by introducing insurance theory. The author also considered the customers loss suffered from power interruption when building ATC model, established large customer decision model, took purchasing quantity of power from different power plants and reserved capacity insurance as variables, targeted minimum power interruption loss as optimization goal and best solution by means of particle swarm algorithm to produce optimal power purchasing decision of large consumers. Simulation was made through IEEE57 system finally and proved that such method is effective.

Behavioral economics: "nudging" underserved populations to be screened for cancer.

PubMed

Purnell, Jason Q; Thompson, Tess; Kreuter, Matthew W; McBride, Timothy D

2015-01-15

Persistent disparities in cancer screening by race/ethnicity and socioeconomic status require innovative prevention tools and techniques. Behavioral economics provides tools to potentially reduce disparities by informing strategies and systems to increase prevention of breast, cervical, and colorectal cancers. With an emphasis on the predictable, but sometimes flawed, mental shortcuts (heuristics) people use to make decisions, behavioral economics offers insights that practitioners can use to enhance evidence-based cancer screening interventions that rely on judgments about the probability of developing and detecting cancer, decisions about competing screening options, and the optimal presentation of complex choices (choice architecture). In the area of judgment, we describe ways practitioners can use the availability and representativeness of heuristics and the tendency toward unrealistic optimism to increase perceptions of risk and highlight benefits of screening. We describe how several behavioral economic principles involved in decision-making can influence screening attitudes, including how framing and context effects can be manipulated to highlight personally salient features of cancer screening tests. Finally, we offer suggestions about ways practitioners can apply principles related to choice architecture to health care systems in which cancer screening takes place. These recommendations include the use of incentives to increase screening, introduction of default options, appropriate feedback throughout the decision-making and behavior completion process, and clear presentation of complex choices, particularly in the context of colorectal cancer screening. We conclude by noting gaps in knowledge and propose future research questions to guide this promising area of research and practice.

Behavioral Economics: “Nudging” Underserved Populations to Be Screened for Cancer

PubMed Central

Thompson, Tess; Kreuter, Matthew W.; McBride, Timothy D.

2015-01-01

Persistent disparities in cancer screening by race/ethnicity and socioeconomic status require innovative prevention tools and techniques. Behavioral economics provides tools to potentially reduce disparities by informing strategies and systems to increase prevention of breast, cervical, and colorectal cancers. With an emphasis on the predictable, but sometimes flawed, mental shortcuts (heuristics) people use to make decisions, behavioral economics offers insights that practitioners can use to enhance evidence-based cancer screening interventions that rely on judgments about the probability of developing and detecting cancer, decisions about competing screening options, and the optimal presentation of complex choices (choice architecture). In the area of judgment, we describe ways practitioners can use the availability and representativeness of heuristics and the tendency toward unrealistic optimism to increase perceptions of risk and highlight benefits of screening. We describe how several behavioral economic principles involved in decision-making can influence screening attitudes, including how framing and context effects can be manipulated to highlight personally salient features of cancer screening tests. Finally, we offer suggestions about ways practitioners can apply principles related to choice architecture to health care systems in which cancer screening takes place. These recommendations include the use of incentives to increase screening, introduction of default options, appropriate feedback throughout the decision-making and behavior completion process, and clear presentation of complex choices, particularly in the context of colorectal cancer screening. We conclude by noting gaps in knowledge and propose future research questions to guide this promising area of research and practice. PMID:25590600

Resilience thinking and a decision-analytic approach to conservation: strange bedfellows or essential partners?

USGS Publications Warehouse

Johnson, Fred A.; Williams, Byron K.; Nichols, James D.

2013-01-01

There has been some tendency to view decision science and resilience theory as opposing approaches, or at least as contending perspectives, for natural resource management. Resilience proponents have been especially critical of optimization in decision science, at least for those cases where it is focused on the aggressive pursuit of efficiency. In general, optimization of resource systems is held to reduce spatial, temporal, or organizational heterogeneity that would otherwise limit efficiency, leading to homogenization of a system and making it less able to cope with unexpected changes or disturbances. For their part, decision analysts have been critical of resilience proponents for not providing much practical advice to decision makers. We believe a key source of tension between resilience thinking and application of decision science is the pursuit of efficiency in the latter (i.e., choosing the “best” management action or strategy option to maximize productivity of one or few resource components), vs. a desire in the former to keep options open (i.e., maintaining and enhancing diversity). It seems obvious, however, that with managed natural systems, there must be a principle by which to guide decision making, which at a minimumallows for a comparison of projected outcomes associated with decision alternatives. This is true even if the primary concern of decision making is the preservation of system resilience. We describe how a careful framing of conservation problems, especially in terms of management objectives and predictive models, can help reduce the purported tension between resiliencethinking and decision analysis. In particular, objective setting in conservation problems needs to be more attuned to the dynamics of ecological systems and to the possibility of deep uncertainties that underlie the risk of unintended, if not irreversible, outcomes. Resilience thinking also leads to the suggestion that model development should focus more on process rather than pattern, on multiple scales of influence, and on phenomena that can create alternative stability regimes. Although we acknowledge the inherent difficulties in modeling ecological processes, we stress that formulation of useful models need not depend on a thorough mechanistic understanding or precise parameterization, assuming that uncertainty is acknowledged and treated in a systematic manner.

Linear versus quadratic portfolio optimization model with transaction cost

NASA Astrophysics Data System (ADS)

Razak, Norhidayah Bt Ab; Kamil, Karmila Hanim; Elias, Siti Masitah

2014-06-01

Optimization model is introduced to become one of the decision making tools in investment. Hence, it is always a big challenge for investors to select the best model that could fulfill their goal in investment with respect to risk and return. In this paper we aims to discuss and compare the portfolio allocation and performance generated by quadratic and linear portfolio optimization models namely of Markowitz and Maximin model respectively. The application of these models has been proven to be significant and popular among others. However transaction cost has been debated as one of the important aspects that should be considered for portfolio reallocation as portfolio return could be significantly reduced when transaction cost is taken into consideration. Therefore, recognizing the importance to consider transaction cost value when calculating portfolio' return, we formulate this paper by using data from Shariah compliant securities listed in Bursa Malaysia. It is expected that, results from this paper will effectively justify the advantage of one model to another and shed some lights in quest to find the best decision making tools in investment for individual investors.

Use of structured decision making to identify monitoring variables and management priorities for salt marsh ecosystems

USGS Publications Warehouse

Neckles, Hilary A.; Lyons, James E.; Guntenspergen, Glenn R.; Shriver, W. Gregory; Adamowicz, Susan C.

2015-01-01

Most salt marshes in the USA have been degraded by human activities, and coastal managers are faced with complex choices among possible actions to restore or enhance ecosystem integrity. We applied structured decision making (SDM) to guide selection of monitoring variables and management priorities for salt marshes within the National Wildlife Refuge System in the northeastern USA. In general, SDM is a systematic process for decomposing a decision into its essential elements. We first engaged stakeholders in clarifying regional salt marsh decision problems, defining objectives and attributes to evaluate whether objectives are achieved, and developing a pool of alternative management actions for achieving objectives. Through this process, we identified salt marsh attributes that were applicable to monitoring National Wildlife Refuges on a regional scale and that targeted management needs. We then analyzed management decisions within three salt marsh units at Prime Hook National Wildlife Refuge, coastal Delaware, as a case example of prioritizing management alternatives. Values for salt marsh attributes were estimated from 2 years of baseline monitoring data and expert opinion. We used linear value modeling to aggregate multiple attributes into a single performance score for each alternative, constrained optimization to identify alternatives that maximized total management benefits subject to refuge-wide cost constraints, and used graphical analysis to identify the optimal set of alternatives for the refuge. SDM offers an efficient, transparent approach for integrating monitoring into management practice and improving the quality of management decisions.

Assessment of adaptation measures to high-mountain risks in Switzerland under climate uncertainties

NASA Astrophysics Data System (ADS)

Muccione, Veruska; Lontzek, Thomas; Huggel, Christian; Ott, Philipp; Salzmann, Nadine

2015-04-01

The economic evaluation of different adaptation options is important to support policy-makers that need to set priorities in the decision-making process. However, the decision-making process faces considerable uncertainties regarding current and projected climate impacts. First, physical climate and related impact systems are highly complex and not fully understood. Second, the further we look into the future, the more important the emission pathways become, with effects on the frequency and severity of climate impacts. Decision on adaptation measures taken today and in the future must be able to adequately consider the uncertainties originating from the different sources. Decisions are not taken in a vacuum but always in the context of specific social, economic, institutional and political conditions. Decision finding processes strongly depend on the socio-political system and usually have evolved over some time. Finding and taking decisions in the respective socio-political and economic context multiplies the uncertainty challenge. Our presumption is that a sound assessment of the different adaptation options in Switzerland under uncertainty necessitates formulating and solving a dynamic, stochastic optimization problem. Economic optimization models in the field of climate change are not new. Typically, such models are applied for global-scale studies but barely for local-scale problems. In this analysis, we considered the case of the Guttannen-Grimsel Valley, situated in the Swiss Bernese Alps. The alpine community has been affected by high-magnitude, high-frequency debris flows that started in 2009 and were historically unprecendented. They were related to thaw of permafrost in the rock slopes of Ritzlihorn and repeated rock fall events that accumulated at the debris fan and formed a sediment source for debris flows and were transported downvalley. An important transit road, a trans-European gas pipeline and settlements were severely affected and partly destroyed. Several adaptation measures were discussed by the responsible authorities but decision making is particularly challenging under multiple uncertainties. For this area, we developed a stochastic optimization model for concrete and real-case adaptation options and measures and use dynamic programming to explore the optimal adaptation decisions under uncertainty in face of uncertain impacts from climate change of debris flows and flooding. Even though simplification needed to be made the results produced were concrete and tangible, indicating that excavation is a preferable adaptation option based on our assumption and modeling in comparison to building a dam or relocation, which is not necessarily intuitive and adds an additional perspective to what has so far been sketched and evaluated by cantonal and communal authorities for Guttannen. Moreover, the building of an alternative cantonal road appears to be more expensive than costs incurring due to road closure.

Metaheuristic simulation optimisation for the stochastic multi-retailer supply chain

NASA Astrophysics Data System (ADS)

Omar, Marina; Mustaffa, Noorfa Haszlinna H.; Othman, Siti Norsyahida

2013-04-01

Supply Chain Management (SCM) is an important activity in all producing facilities and in many organizations to enable vendors, manufacturers and suppliers to interact gainfully and plan optimally their flow of goods and services. A simulation optimization approach has been widely used in research nowadays on finding the best solution for decision-making process in Supply Chain Management (SCM) that generally faced a complexity with large sources of uncertainty and various decision factors. Metahueristic method is the most popular simulation optimization approach. However, very few researches have applied this approach in optimizing the simulation model for supply chains. Thus, this paper interested in evaluating the performance of metahueristic method for stochastic supply chains in determining the best flexible inventory replenishment parameters that minimize the total operating cost. The simulation optimization model is proposed based on the Bees algorithm (BA) which has been widely applied in engineering application such as training neural networks for pattern recognition. BA is a new member of meta-heuristics. BA tries to model natural behavior of honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new algorithms for solving optimization problems. This model considers an outbound centralised distribution system consisting of one supplier and 3 identical retailers and is assumed to be independent and identically distributed with unlimited supply capacity at supplier.

The Nature of Belief-Directed Exploratory Choice in Human Decision-Making

PubMed Central

Knox, W. Bradley; Otto, A. Ross; Stone, Peter; Love, Bradley C.

2011-01-01

In non-stationary environments, there is a conflict between exploiting currently favored options and gaining information by exploring lesser-known options that in the past have proven less rewarding. Optimal decision-making in such tasks requires considering future states of the environment (i.e., planning) and properly updating beliefs about the state of the environment after observing outcomes associated with choices. Optimal belief-updating is reflective in that beliefs can change without directly observing environmental change. For example, after 10 s elapse, one might correctly believe that a traffic light last observed to be red is now more likely to be green. To understand human decision-making when rewards associated with choice options change over time, we develop a variant of the classic “bandit” task that is both rich enough to encompass relevant phenomena and sufficiently tractable to allow for ideal actor analysis of sequential choice behavior. We evaluate whether people update beliefs about the state of environment in a reflexive (i.e., only in response to observed changes in reward structure) or reflective manner. In contrast to purely “random” accounts of exploratory behavior, model-based analyses of the subjects’ choices and latencies indicate that people are reflective belief updaters. However, unlike the Ideal Actor model, our analyses indicate that people’s choice behavior does not reflect consideration of future environmental states. Thus, although people update beliefs in a reflective manner consistent with the Ideal Actor, they do not engage in optimal long-term planning, but instead myopically choose the option on every trial that is believed to have the highest immediate payoff. PMID:22319503

Risk management for optimal land use planning integrating ecosystem services values: A case study in Changsha, Middle China.

PubMed

Liang, Jie; Zhong, Minzhou; Zeng, Guangming; Chen, Gaojie; Hua, Shanshan; Li, Xiaodong; Yuan, Yujie; Wu, Haipeng; Gao, Xiang

2017-02-01

Land-use change has direct impact on ecosystem services and alters ecosystem services values (ESVs). Ecosystem services analysis is beneficial for land management and decisions. However, the application of ESVs for decision-making in land use decisions is scarce. In this paper, a method, integrating ESVs to balance future ecosystem-service benefit and risk, is developed to optimize investment in land for ecological conservation in land use planning. Using ecological conservation in land use planning in Changsha as an example, ESVs is regarded as the expected ecosystem-service benefit. And uncertainty of land use change is regarded as risk. This method can optimize allocation of investment in land to improve ecological benefit. The result shows that investment should be partial to Liuyang City to get higher benefit. The investment should also be shifted from Liuyang City to other regions to reduce risk. In practice, lower limit and upper limit for weight distribution, which affects optimal outcome and selection of investment allocation, should be set in investment. This method can reveal the optimal spatial allocation of investment to maximize the expected ecosystem-service benefit at a given level of risk or minimize risk at a given level of expected ecosystem-service benefit. Our results of optimal analyses highlight tradeoffs between future ecosystem-service benefit and uncertainty of land use change in land use decisions. Copyright © 2016 Elsevier B.V. All rights reserved.

Critical care nurses' decision making: sedation assessment and management in intensive care.

PubMed

Aitken, Leanne M; Marshall, Andrea P; Elliott, Rosalind; McKinley, Sharon

2009-01-01

This study was designed to examine the decision making processes that nurses use when assessing and managing sedation for a critically ill patient, specifically the attributes and concepts used to determine sedation needs and the influence of a sedation guideline on the decision making processes. Sedation management forms an integral component of the care of critical care patients. Despite this, there is little understanding of how nurses make decisions regarding assessment and management of intensive care patients' sedation requirements. Appropriate nursing assessment and management of sedation therapy is essential to quality patient care. Observational study. Nurses providing sedation management for a critically ill patient were observed and asked to think aloud during two separate occasions for two hours of care. Follow-up interviews were conducted to collect data from five expert critical care nurses pre- and postimplementation of a sedation guideline. Data from all sources were integrated, with data analysis identifying the type and number of attributes and concepts used to form decisions. Attributes and concepts most frequently used related to sedation and sedatives, anxiety and agitation, pain and comfort and neurological status. On average each participant raised 48 attributes related to sedation assessment and management in the preintervention phase and 57 attributes postintervention. These attributes related to assessment (pre, 58%; post, 65%), physiology (pre, 10%; post, 9%) and treatment (pre, 31%; post, 26%) aspects of care. Decision making in this setting is highly complex, incorporating a wide range of attributes that concentrate primarily on assessment aspects of care. Clinical guidelines should provide support for strategies known to positively influence practice. Further, the education of nurses to use such guidelines optimally must take into account the highly complex iterative process and wide range of data sources used to make decisions.

Theory-informed design of values clarification methods: a cognitive psychological perspective on patient health-related decision making.

PubMed

Pieterse, Arwen H; de Vries, Marieke; Kunneman, Marleen; Stiggelbout, Anne M; Feldman-Stewart, Deb

2013-01-01

Healthcare decisions, particularly those involving weighing benefits and harms that may significantly affect quality and/or length of life, should reflect patients' preferences. To support patients in making choices, patient decision aids and values clarification methods (VCM) in particular have been developed. VCM intend to help patients to determine the aspects of the choices that are important to their selection of a preferred option. Several types of VCM exist. However, they are often designed without clear reference to theory, which makes it difficult for their development to be systematic and internally coherent. Our goal was to provide theory-informed recommendations for the design of VCM. Process theories of decision making specify components of decision processes, thus, identify particular processes that VCM could aim to facilitate. We conducted a review of the MEDLINE and PsycINFO databases and of references to theories included in retrieved papers, to identify process theories of decision making. We selected a theory if (a) it fulfilled criteria for a process theory; (b) provided a coherent description of the whole process of decision making; and (c) empirical evidence supports at least some of its postulates. Four theories met our criteria: Image Theory, Differentiation and Consolidation theory, Parallel Constraint Satisfaction theory, and Fuzzy-trace Theory. Based on these, we propose that VCM should: help optimize mental representations; encourage considering all potentially appropriate options; delay selection of an initially favoured option; facilitate the retrieval of relevant values from memory; facilitate the comparison of options and their attributes; and offer time to decide. In conclusion, our theory-based design recommendations are explicit and transparent, providing an opportunity to test each in a systematic manner. Copyright © 2012 Elsevier Ltd. All rights reserved.

The CHOICE pilot project: Challenges of implementing a combined peer work and shared decision-making programme in an early intervention service.

PubMed

Simmons, Magenta B; Coates, Dominiek; Batchelor, Samantha; Dimopoulos-Bick, Tara; Howe, Deborah

2017-12-12

Youth participation is central to early intervention policy and quality frameworks. There is good evidence for peer support (individuals with lived experience helping other consumers) and shared decision making (involving consumers in making decisions about their own care) in adult settings. However, youth programs are rarely tested or described in detail. This report aims to fill this gap by describing a consumer focused intervention in an early intervention service. This paper describes the development process, intervention content and implementation challenges of the Choices about Healthcare Options Informed by Client Experiences and Expectations (CHOICE) Pilot Project. This highly novel and innovative project combined both youth peer work and youth shared decision making. Eight peer workers were employed to deliver an online shared decision-making tool at a youth mental health service in New South Wales, Australia. The intervention development involved best practice principles, including international standards and elements of co-design. The implementation of the peer workforce in the service involved a number of targeted strategies designed to support this new service model. However, several implementation challenges were experienced which resulted in critical learning about how best to deliver these types of interventions. Delivering peer work and shared decision making within an early intervention service is feasible, but not without challenges. Providing adequate detail about interventions and implementation strategies fills a critical gap in the literature. Understanding optimal youth involvement strategies assists others to deliver acceptable and effective services to young people who experience mental ill health. © 2017 John Wiley & Sons Australia, Ltd.

Communication and Decision-Making About End-of-Life Care in the Intensive Care Unit.

PubMed

Brooks, Laura Anne; Manias, Elizabeth; Nicholson, Patricia

2017-07-01

Clinicians in the intensive care unit commonly face decisions involving withholding or withdrawing life-sustaining therapy, which present many clinical and ethical challenges. Communication and shared decision-making are key aspects relating to the transition from active treatment to end-of-life care. To explore the experiences and perspectives of nurses and physicians when initiating end-of-life care in the intensive care unit. The study was conducted in a 24-bed intensive care unit in Melbourne, Australia. An interpretative, qualitative inquiry was used, with focus groups as the data collection method. Intensive care nurses and physicians were recruited to participate in a discipline-specific focus group. Focus group discussions were audio-recorded, transcribed, and subjected to thematic data analysis. Five focus groups were conducted; 17 nurses and 11 physicians participated. The key aspects discussed included communication and shared decision-making. Themes related to communication included the timing of end-of-life care discussions and conducting difficult conversations. Implementation and multidisciplinary acceptance of end-of-life care plans and collaborative decisions involving patients and families were themes related to shared decision-making. Effective communication and decision-making practices regarding initiating end-of-life care in the intensive care unit are important. Multidisciplinary implementation and acceptance of end-of-life care plans in the intensive care unit need improvement. Clear organizational processes that support the introduction of nurse and physician end-of-life care leaders are essential to optimize outcomes for patients, family members, and clinicians. ©2017 American Association of Critical-Care Nurses.

An Overlay Architecture for Throughput Optimal Multipath Routing

DTIC Science & Technology

2017-01-14

1 An Overlay Architecture for Throughput Optimal Multipath Routing Nathaniel M. Jones, Georgios S. Paschos, Brooke Shrader, and Eytan Modiano...decisions. In this work, we study an overlay architecture for dynamic routing such that only a subset of devices (overlay nodes) need to make dynamic routing...a legacy network. Network overlays are frequently used to deploy new communication architectures in legacy networks [13]. To accomplish this, messages

Adaptive Decision Making Using Probabilistic Programming and Stochastic Optimization

DTIC Science & Technology

2018-01-01

world optimization problems (and hence 16 Approved for Public Release (PA); Distribution Unlimited Pred. demand (uncertain; discrete ...simplify the setting, we further assume that the demands are discrete , taking on values d1, . . . , dk with probabilities (conditional on x) (pθ)i ≡ p...Tyrrell Rockafellar. Implicit functions and solution mappings. Springer Monogr. Math ., 2009. Anthony V Fiacco and Yo Ishizuka. Sensitivity and stability

Derivative Trade Optimizing Model Utilizing GP Based on Behavioral Finance Theory

NASA Astrophysics Data System (ADS)

Matsumura, Koki; Kawamoto, Masaru

This paper proposed a new technique which makes the strategy trees for the derivative (option) trading investment decision based on the behavioral finance theory and optimizes it using evolutionary computation, in order to achieve high profitability. The strategy tree uses a technical analysis based on a statistical, experienced technique for the investment decision. The trading model is represented by various technical indexes, and the strategy tree is optimized by the genetic programming(GP) which is one of the evolutionary computations. Moreover, this paper proposed a method using the prospect theory based on the behavioral finance theory to set psychological bias for profit and deficit and attempted to select the appropriate strike price of option for the higher investment efficiency. As a result, this technique produced a good result and found the effectiveness of this trading model by the optimized dealings strategy.

Investigating the decision-making processes that contribute to impaired driving.

DOT National Transportation Integrated Search

2015-08-01

Alcohol-impaired (AI) driving continues to cause a disproportionate number of fatalities within the college and : young adult populations, indicating optimal prevention programs for AI driving have yet to be developed. The : current study tested the ...