模棱两可
累积前景理论
稳健优化
失真(音乐)
期望效用假设
计算机科学
数学优化
班级(哲学)
风险厌恶(心理学)
前景理论
凸优化
最优决策
主观期望效用
计量经济学
经济
正多边形
数理经济学
微观经济学
数学
人工智能
放大器
计算机网络
几何学
带宽(计算)
程序设计语言
决策树
作者
Jun Cai,Jonathan Yu-Meng Li,Tiantian Mao
出处
期刊:Operations Research
[Institute for Operations Research and the Management Sciences]
日期:2023-10-10
被引量:8
标识
DOI:10.1287/opre.2020.0685
摘要
Optimal Decision Making Under Distorted Expectation with Partial Distribution Information Decision makers who are not risk neutral may evaluate expected values by distorting objective probabilities to reflect their risk attitudes, a phenomenon known as distorted expectations. This concept is widely applied in behavioral economics, insurance, finance, and other business domains. In “Distributionally Robust Optimization Under Distorted Expectations,” Cai, Li, and Mao study how decision makers using distorted expectations can optimize their decisions when only partial information about objective probabilities is available. They show that decision makers who are ambiguity averse can optimize their decisions as if they are risk averse with their risk attitudes characterized by a convex distortion function. This finding demonstrates why even non–risk-averse decision makers, such as those studied in the celebrated cumulative prospect theory, may consider it optimal to take risk-averse decisions when facing uncertainty about objective probabilities. Leveraging this finding, the authors show that a large class of distributionally robust optimization problems involving the use of distorted expectations can be tractably solved as convex programs.
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