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Fragility of Asymptotic Agreement under Bayesian Learning

Author

Listed:
  • Daron Acemoglu
  • Victor Chernozhukov
  • Muhamet Yildiz

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 (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.
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Suggested Citation

  • Daron Acemoglu & Victor Chernozhukov & Muhamet Yildiz, 2009. "Fragility of Asymptotic Agreement under Bayesian Learning," Levine's Working Paper Archive 814577000000000139, David K. Levine.
    • Handle: RePEc:cla:levarc:814577000000000139
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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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