Fragility of asymptotic agreement under Bayesian learning
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Acemoglu_Fragility of asymptotic.pdf
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437.53 KB
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Author(s)
Acemoglu, Daron
Chernozhukov, Victor V.
Yildiz, Muhamet
Date Issued
February 2016
Journal
Theoretical Economics
Publisher
John Wiley & Sons
Citation
Acemoglu, Daron, Victor Chernozhukov, and Muhamet Yildiz. “Fragility of Asymptotic Agreement Under Bayesian Learning.” Theoretical Economics 11, no. 1 (January 2016): 187–225.
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
Creative Commons Attribution Non-Commercial License
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DOI of Published Version
https://doi.org/10.3982/TE436
