Non-Bayesian Persuasion
研究在概率推断存在错误时，如何优化信息设计来说服他人，发现凹化方法可推广至多种非贝叶斯更新规则，并应用于揭示原理、规则排序及说服效果分析。
Following Kamenica and Gentzkow, this paper studies persuasion as an information design problem. We investigate how mistakes in probabilistic inference impact optimal persuasion. The concavification method is shown to extend naturally to a large class of belief updating rules, which we identify and characterize. This class comprises many non-Bayesian models discussed in the literature. We apply this new technique to gain insight into the revelation principle, the ranking of updating rules, when persuasion is beneficial to the sender, and when it is detrimental to the receiver. Our key result also extends to shed light on the question of robust persuasion.