Social Learning with Coarse Inference

Antonio Guarino and Philippe Jehiel ()

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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.

Date: 2013-02
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Published in American Economic Journal: Microeconomics, 2013, 5 (1), pp.147-174. ⟨10.1257/mic.5.1.147⟩

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Related works:
Journal Article: Social Learning with Coarse Inference (2013)
Working Paper: Social Learning with Coarse Inference (2013)
Working Paper: Social Learning with Coarse Inference (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-00813047

DOI: 10.1257/mic.5.1.147

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