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Social Learning with Coarse Inference

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  • Antonio Guarino
  • Philippe Jehie

Abstract

We study social learning by boundedly rational agents. Agents take a decision in sequence, after observing their predecessors and a private signal. They are unable to make perfect inferences from their predecessors' decisions: they only understand the relation between the aggregate distribution of actions and the state of nature, and make their inferences accordingly. We show that, in a discrete action space, even if agents receive signals of unbounded precision, there are asymptotic inefficiencies. In a continuous action space, compared to the rational case, agents overweight early signals. Despite this behavioral bias, eventually agents learn the realized state of the world and choose the correct action.
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Suggested Citation

  • Antonio Guarino & Philippe Jehie, 2009. "Social Learning with Coarse Inference," Levine's Working Paper Archive 814577000000000292, David K. Levine.
    • Handle: RePEc:cla:levarc:814577000000000292
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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