Open AccessFragility of asymptotic agreement under Bayesian learning
Author(s) -
Acemoglu Daron,
Chernozhukov Victor,
Yildiz Muhamet
Publication year - 2016
Publication title -
theoretical economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.404
H-Index - 32
eISSN - 1555-7561
pISSN - 1933-6837
DOI - 10.3982/te436
Subject(s) - merge (version control) , fragility , stochastic game , mathematics , bayesian probability , econometrics , statistical physics , weak convergence , convergence (economics) , mathematical economics , computer science , statistics , economics , physics , computer security , asset (computer security) , information retrieval , economic growth , thermodynamics
Under the assumption that individuals know the conditional distributions of signals given the payoff‐relevant parameters, existing results conclude that as individuals observe infinitely many signals, their beliefs about the parameters will eventually merge. We first show that these results are fragile when individuals are uncertain about the signal distributions: given any such model, vanishingly small individual uncertainty about the signal distributions can lead to substantial (nonvanishing) differences in asymptotic beliefs. Under a uniform convergence assumption, we then characterize the conditions under which a small amount of uncertainty leads to significant asymptotic disagreement.