BERK-NASH EQUILIBRIUM: A FRAMEWORK FOR MODELING AGENTS WITH MISSPECIFIED MODELS

• 成果类型: Article
• 署名作者: Esponda, Ignacio; Pouzo, Demian
• 署名单位: Washington University (WUSTL); University of California System; University of California Berkeley
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA12609
• 发表日期: 2016
• 页码: 1093-1130
• 关键词: ASYMPTOTIC-BEHAVIOR; play; INFORMATION; games; Forecast; MARKET

摘要

We develop an equilibrium framework that relaxes the standard assumption that people have a correctly specified view of their environment. Each player is characterized by a (possibly misspecified) subjective model, which describes the set of feasible beliefs over payoff-relevant consequences as a function of actions. We introduce the notion of a Berk-Nash equilibrium: Each player follows a strategy that is optimal given her belief, and her belief is restricted to be the best fit among the set of beliefs she considers possible. The notion of best fit is formalized in terms of minimizing the Kullback-Leibler divergence, which is endogenous and depends on the equilibrium strategy profile. Standard solution concepts such as Nash equilibrium and self-confirming equilibrium constitute special cases where players have correctly specified models. We provide a learning foundation for Berk-Nash equilibrium by extending and combining results from the statistics literature on misspecified learning and the economics literature on learning in games.