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Social Learning with Coarse Inference

  • Antonio Guarino
  • Philippe Jehiel

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. (JEL D82, D83)

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Article provided by American Economic Association in its journal American Economic Journal: Microeconomics.

Volume (Year): 5 (2013)
Issue (Month): 1 (February)
Pages: 147-74

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Handle: RePEc:aea:aejmic:v:5:y:2013:i:1:p:147-74
Note: DOI: 10.1257/mic.5.1.147
Contact details of provider: Web page: https://www.aeaweb.org/aej-micro
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  1. Ellison, Glenn & Fudenberg, Drew, 1993. "Rules of Thumb for Social Learning," Journal of Political Economy, University of Chicago Press, vol. 101(4), pages 612-43, August.
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  8. Larson, Nathan, 2008. "Inertia in social learning from a summary statistic," MPRA Paper 32143, University Library of Munich, Germany, revised Jul 2011.
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  10. Ignacio Monzon & Michael Rapp, 2011. "Observational Learning with Position Uncertainty," Carlo Alberto Notebooks 206, Collegio Carlo Alberto.
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