Persuasion and Welfare
研究了评分、评级等信息政策如何影响异质性人群的福利，通过定义贝叶斯福利集并刻画其帕累托边界，为评估信息政策的福利效果提供了分析框架。
Information policies such as scores, ratings, and recommendations are increasingly shaping society’s choices in high-stakes domains. We provide a framework to study the welfare implications of information policies on a population of heterogeneous individuals. We define and characterize the Bayes welfare set, consisting of the population’s utility profiles that are feasible under some information policy. The Pareto frontier of this set can be recovered by a series of standard Bayesian persuasion problems. We provide necessary and sufficient conditions under which an information policy exists that Pareto dominates the no-information policy. We illustrate our results with applications to data leakage, price discrimination, and credit ratings.