Distributionally Robust Optimization Under Distorted Expectations
研究了决策者在仅知部分客观概率信息时，如何利用扭曲期望进行优化决策，发现模糊厌恶者会像风险厌恶者一样行动，并可将此类问题转化为凸规划求解。
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.