Fragility of Asymptotic Agreement Under Bayesian Learning

By Daron Acemoglu, Victor Chernozhukov, Muhamet Yildiz. 2008

169 citations recorded by the source corpus.

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 (non-vanishing) 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.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b1dba4991e5da2a96a4293d68dbb4165fdedb770

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In Economics and Econometrics

  1. David H. Autor
  2. David Dorn
  3. Gordon H. Hanson
  4. Kaveh Majlesi
  5. Larry G. Epstein
  6. Kyoungwon Seo
  7. Joshua Schwartzstein
  8. Daron Acemoglu

In Management Science and Operations Research

  1. Larry G. Epstein
  2. Gordon H. Hanson
  3. David Dorn
  4. Kyoungwon Seo
  5. Joshua Schwartzstein
  6. David H. Autor
  7. Martin Schneider
  8. Cosmin L. Ilut

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