Social Learning with Coarse Inference

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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Submitted on : Sunday, April 14, 2013 - 9:32:22 PM
Last modification on : Tuesday, April 24, 2018 - 5:20:14 PM

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Antonio Guarino, Philippe Jehiel. Social Learning with Coarse Inference. American Economic Journal: Microeconomics, American Economic Association, 2013, 5 (1), pp.147-174. ⟨10.1257/mic.5.1.147⟩. ⟨hal-00813047⟩

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