多维风险下的决策
Decision Making Under Multidimensional Risk
浏览论文内容
中文总结 AI 辅助
本文针对多维风险下的决策问题，提出结构化多维期望效用的统一框架，利用有根聚类树组织风险评估，分析其唯一性与特例，并应用于不平等及多源收入研究。
中文摘要 AI 辅助
选择方案往往是多维且具有风险的。我们引入并公理化了结构化多维期望效用表示，这是一个统一框架，可推广现有评估此类方案的方法。该表示使用有根聚类树，在共同结构中组织各维度风险的联合、独立及条件评估。我们分析了该表示的唯一性并刻画了有用的特例，将其应用于个体、群体和代际间的不平等以及多源收入，刻画了分组对随机占优和多维风险规避的影响。
英文摘要
Choice alternatives are often multidimensional and risky. We introduce and axiomatize the \textit{structured multidimensional expected utility} representation, a unified framework that generalizes existing approaches to evaluating such alternatives. The representation uses a \textit{rooted clustered tree} to organize the joint, separate, and conditional evaluation of risk across dimensions within a common structure. We analyze the uniqueness of the representation and characterize useful special cases. We apply the representation to inequality across individuals, groups, and generations and to multisource income, characterizing the implications of bracketing for stochastic dominance and the avoidance of multidimensional risk.
