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

Berk-Nash Equilibrium: A Framework for Modeling Agents with Misspecified Models