Using Cumulative Prospect Theory to Explain Anomalous Crop Insurance Coverage Choice

2015 ◽  
Vol 97 (5) ◽  
pp. 1371-1384 ◽  
Author(s):  
Bruce A. Babcock
Keyword(s):  
Prospect Theory ◽  
Crop Insurance ◽  
Insurance Coverage ◽  
Cumulative Prospect Theory

scholarly journals Implications of Commodity Programs and Crop Insurance Policies for Wheat Producers

2019 ◽  
Vol 51 (02) ◽  
pp. 267-285 ◽  
Author(s):  
Jeff Luckstead ◽  
Stephen Devadoss
Keyword(s):  
Prospect Theory ◽  
Crop Insurance ◽  
Cumulative Prospect Theory ◽  
Certainty Equivalent ◽  
Coverage Level ◽  
Insurance Policies ◽  
Agriculture Risk ◽  
Commodity Programs ◽  
Plc Program ◽  
Risk Coverage

AbstractWe analyze the effects of Price Loss Coverage (PLC), Agriculture Risk Coverage (ARC), individual revenue protection insurance (RP), and Supplemental Coverage Option (SCO) on the RP coverage level, certainty equivalent, and program payments. The model is calibrated to a representative wheat farm in Mitchell County in Kansas to analyze the effects of various policies. The result highlights that when insurance is framed as an investment, cumulative prospect theory predicts farmers’ coverage decisions accurately at 70%. ARC or PLC program increases the RP coverage level to 75%, but PLC and SCO jointly decrease the RP coverage level to 70%.

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.

Optimal selection of energy storage nodes based on improved cumulative prospect theory in China

2020 ◽  
Vol 12 (6) ◽  
pp. 064101
Author(s):  
Jicheng Liu ◽  
Zhenzhen Wang ◽  
Yu Yin ◽  
Yinghuan Li ◽  
Yunyuan Lu
Keyword(s):  
Energy Storage ◽  
Prospect Theory ◽  
Cumulative Prospect Theory ◽  
Optimal Selection ◽  
Selection Of

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.

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