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.

中文翻译：

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内生错误学习的极限点

我们研究了当代理商的先验信念被错误指定时，代理商如何从内源数据中学习。我们证明，只有一致的Berk-Nash均衡才可以是长期结果，并且对于某些初始信念，所有统一严格的Berk-Nash均衡都具有作为长期结果的任意高概率。当主体认为结果分布是外生的时，对于任何初始信念，每个统一严格的Berk-Nash均衡都有可能成为长期结果。我们将这些结果推广到代理在行动之前观察到信号的设置。