Bayesian Learning When Players Are Misspecified about Others

Takeshi Murooka and Yuichi Yamamoto

ISER Discussion Paper from Institute of Social and Economic Research, The University of Osaka

Abstract: This paper considers Bayesian learning when players are biased about the data-generating process, and are biased about the opponent’s bias about the data-generating process. Specifically, we assume that each player’s bias about others takes the form of interpersonal projection, which is a tendency to overestimate the extent to which others share the player’s own view. We show that there is a class of games in which even an arbitrarily small amount of bias can destroy correct learning of an unknown state, i.e., the probability of the posterior beliefs converging to an approximately correct state suddenly drops to zero.

Date: 2025-04, Revised 2026-08
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Persistent link: https://EconPapers.repec.org/RePEc:dpr:wpaper:1284r

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