scholarly journals Do People Make Decisions Under Risk Based on Ignorance? An Empirical Test of the Priority Heuristic Against Cumulative Prospect Theory

2008 ◽  
Author(s):  
Andreas Glöckner ◽  
Tilmann Betsch
Keyword(s):  
Prospect Theory ◽  
Empirical Test ◽  
Cumulative Prospect Theory ◽  
Decisions Under Risk

scholarly journals Cumulative Prospect Theory Version with Fuzzy Values of Outcome Estimates

Risks ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 72
Author(s):  
Oleg Uzhga-Rebrov ◽  
Peter Grabusts
Keyword(s):  
Prospect Theory ◽  
Expected Utility ◽  
Utility Theory ◽  
Expected Utility Theory ◽  
Cumulative Prospect Theory ◽  
Development History ◽  
Decisions Under Risk ◽  
History Of ◽  
Main Factors ◽  
Theoretical Foundations

Choosing solutions under risk and uncertainty requires the consideration of several factors. One of the main factors in choosing a solution is modeling the decision maker’s attitude to risk. The expected utility theory was the first approach that allowed to correctly model various nuances of the attitude to risk. Further research in this area has led to the emergence of even more effective approaches to solving this problem. Currently, the most developed theory of choice with respect to decisions under risk conditions is the cumulative prospect theory. This paper presents the development history of various extensions of the original expected utility theory, and the analysis of the main properties of the cumulative prospect theory. The main result of this work is a fuzzy version of the prospect theory, which allows handling fuzzy values of the decisions (prospects). The paper presents the theoretical foundations of the proposed version, an illustrative practical example, and conclusions based on the results obtained.

Probability Dominance

2020 ◽  
Vol 102 (5) ◽  
pp. 1006-1020
Author(s):  
Enrico Diecidue ◽  
Haim Levy ◽  
Moshe Levy
Keyword(s):  
Decision Making ◽  
Prospect Theory ◽  
Expected Utility ◽  
Cumulative Prospect Theory ◽  
Economic Consequences ◽  
Regret Theory ◽  
Decisions Under Risk ◽  
Set Up ◽  
Winner Take All ◽  
Probability Dominance

The most commonly employed paradigms for decision making under risk are expected utility, prospect theory, and regret theory. We examine the simple heuristic of maximizing the probability of being ahead, which in some natural economic situations may be in contradiction to all three of the above fundamental paradigms. We test whether this heuristic, which we call probability dominance (PD), affects decisions under risk. We set up head-to-head situations where all preferences of a given class (expected utility, original or cumulative prospect theory, or regret theory) favor one alternative yet PD favors the other. Our experiments reveal that 49% of subjects' choices are aligned with PD in contradiction to any form of expected utility or prospect theory maximization; 73% are aligned with PD as opposed to preferences under risk aversion and under original and cumulative prospect theory preferences; and 68% to 76% are aligned with PD contradicting preferences under regret theory. We conclude that probability dominance substantially affects choices and should therefore be incorporated into decision-making models. We show that PD has significant economic consequences. The PD heuristic may have evolved through situations of winner-take-all competition.

Research on disruption management of urgent arrival in job shop with deteriorating effect

2021 ◽  
pp. 1-13
Author(s):  
Ning Tao ◽  
Duan Xiaodong ◽  
An Lu ◽  
Gou Tao
Keyword(s):  
Prospect Theory ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Cumulative Prospect Theory ◽  
Disruption Management ◽  
Particle Swarm Algorithm ◽  
Shop Scheduling ◽  
Management Method

A disruption management method based on cumulative prospect theory is proposed for the urgent with deteriorating effect arrival in flexible job shop scheduling problem (FJSP). First, the mathematical model of problem is established with minimizing the completion time of urgent order, minimizing the total process time of the system and minimizing the total cost as the target. Then, the cumulative prospect theory equation of the urgent arrival in job shop scheduling process is induced designed. Based on the selected model, an optimized multi-phase quantum particle swarm algorithm (MQPSO) is proposed for selecting processing route. Finally, using Solomon example simulation and company Z riveting shop example as the study object, the performance of the proposed method is analyzed. It is compared with the current common rescheduling methods, and the results verify that the method proposed in this paper not only meets the goal of the optimized objects, but improves the practical requirements for the stability of production and processing system during urgent arrival. Lastly, the optimized multiphase quantum particle swarm algorithm is used to solve disruption management of urgent arrival problem. Through instance analysis and comparison, the effectiveness and efficiency of urgent arrival disruption management method with deteriorating effect are verified.

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