Limit Points of Endogenous Misspecified Learning
研究了当代理人先验信念错误时，如何从内生数据中学习，发现只有均匀的Berk-Nash均衡能成为长期结果，且所有均匀严格的Berk-Nash均衡都有高概率成为某些初始信念下的长期结果。
We study how an agent learns from endogenous data when their prior belief is misspecified. We show that only uniform Berk–Nash equilibria can be long‐run outcomes, and that all uniformly strict Berk–Nash equilibria have an arbitrarily high probability of being the long‐run outcome for some initial beliefs. When the agent believes the outcome distribution is exogenous, every uniformly strict Berk–Nash equilibrium has positive probability of being the long‐run outcome for any initial belief. We generalize these results to settings where the agent observes a signal before acting.