Prospect Theory and Risk Preferences of Oregon Seed Producers

1991 ◽  
Vol 73 (2) ◽  
pp. 429-435 ◽  
Author(s):  
Alan Collins ◽  
Wesley N. Musser ◽  
Robert Mason
Keyword(s):  
Prospect Theory ◽  
Risk Preferences

Software Project Managers’ Risk Preferences

1996 ◽  
Vol 11 (4) ◽  
pp. 287-295
Author(s):  
Thomas W. Lauer
Keyword(s):  
Software Development ◽  
Prospect Theory ◽  
Expected Utility ◽  
Utility Theory ◽  
Risk Preferences ◽  
Expected Utility Theory ◽  
Project Managers ◽  
Development Projects ◽  
Software Project ◽  
Potential Importance

A questionnaire was administered to 68 software project managers (SPMs). Questions were designed to test whether SPMs’ risky judgments were more consistent with Expected Utility Theory or Prospect Theory. Although the results were more consistent with Prospect Theory, they differed in important ways showing SPMs’ judgments to be less homogeneous than is assumed by either theory. These results highlight the potential importance of SPMs’ judgments to the outcome of software development projects.

scholarly journals Collaborative Optimization of Electric Vehicles Based on MultiAgent Variant Roth–Erev Algorithm

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 125
Author(s):  
Jianwei Gao ◽  
Yu Yang ◽  
Fangjie Gao ◽  
Haoyu Wu
Keyword(s):  
Utility Function ◽  
Prospect Theory ◽  
Electric Vehicles ◽  
Risk Preferences ◽  
Collaborative Optimization ◽  
Model Based ◽  
Scheduling Model ◽  
Residential Electricity ◽  
Carbon Neutral ◽  
Optimization Scheduling

With the implementation of the carbon neutral policy, the number of electric vehicles (EVs) is increasing. Thus, it is urgently needed to manage the charging and discharging behavior of EVs scientifically. In this paper, EVs are regarded as agents, and a multiagent cooperative optimization scheduling model based on Roth–Erev (RE) algorithm is proposed. The charging and discharging behaviors of EVs will influence each other. The charging and discharging strategy of one EV owner will affect the choice of others. Therefore, the RE algorithm is selected to obtain the optimal charging and discharging strategy of the EV group, with the utility function of the prospect theory proposed to describe EV owners’ different risk preferences. The utility function of the prospect theory has superior effectiveness in describing consumers’ utility. Finally, in the case of residential electricity, the effectiveness of the proposed method is verified. Compared with that of random charging, this method reduces the total EV group cost of EVs by 52.4%, with the load variance reduced by 26.4%.

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