Asymptotic behavior of Bayesian learners with misspecified models

• 成果类型: Article
• 署名作者: Esponda, Ignacio; Pouzo, Demian; Yamamoto, Yuichi
• 署名单位: University of California System; University of California Santa Barbara; University of California System; University of California Berkeley; Hitotsubashi University
• 刊物名称: JOURNAL OF ECONOMIC THEORY
• ISSN/ISSBN: 0022-0531
• DOI: 10.1016/j.jet.2021.105260
• 发表日期: 2021
• 关键词: Bayesian learning; misspecified models; Asymptotic behavior; Differential inclusion; Berk-Nash equilibrium

摘要

We consider an agent who represents uncertainty about the environment via a possibly misspecified model. Each period, the agent takes an action, observes a consequence, and uses Bayes' rule to update her belief about the environment. This framework has become increasingly popular in economics to study behavior driven by incorrect or biased beliefs. By first showing that the key element to predict the agent's behavior is the frequency of her past actions, we are able to characterize asymptotic behavior in general settings in terms of the solutions of a differential inclusion that describes the evolution of the frequency of actions. We then present a series of implications that can be readily applied to economic applications, thus providing off-the-shelf tools that can be used to characterize behavior under misspecified learning. (c) 2021 Elsevier Inc. All rights reserved.