Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/331434 
Year of Publication: 
2025
Series/Report no.: 
ISER Discussion Paper No. 1284
Publisher: 
Osaka University, Institute of Social and Economic Research (ISER), 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 even an arbitrarily small amount of bias can destroy correct learning of an unknown state, i.e., there is zero probability of the posterior belief staying in a neighborhood of the true state.
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.