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Social Learning with Coarse Inference

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  • Antonio Guarino
  • Philippe Jehiel

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. (JEL D82, D83)

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Bibliographic Info

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
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References

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  1. Steffen Huck & Joerg Oechssler, 1997. "Informational Cascades with Continuous Action Spaces," Game Theory and Information 9710002, EconWPA.
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  4. Antonio Guarino & Steffen Huck & Heike Harmgart, 2008. "When half the truth is better than the truth: A Theory of aggregate information cascades," WEF Working Papers 0046, ESRC World Economy and Finance Research Programme, Birkbeck, University of London.
  5. David Hirshleifer & Siew Hong Teoh, 2003. "Herd Behaviour and Cascading in Capital Markets: a Review and Synthesis," European Financial Management, European Financial Management Association, vol. 9(1), pages 25-66.
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  8. Celen, Bogachan & Kariv, Shachar, 2004. "Observational learning under imperfect information," Games and Economic Behavior, Elsevier, vol. 47(1), pages 72-86, April.
  9. Philippe Jehiel & Frederic Koessler, 2005. "Revisiting Games of Incomplete Information with Analogy-Based Expectations," THEMA Working Papers 2005-04, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
  10. Ignacio Monzon & Michael Rapp, 2011. "Observational Learning with Position Uncertainty," Carlo Alberto Notebooks 206, Collegio Carlo Alberto.
  11. Banerjee, Abhijit & Fudenberg, Drew, 2004. "Word-of-mouth learning," Games and Economic Behavior, Elsevier, vol. 46(1), pages 1-22, January.
  12. Gale, Douglas, 1996. "What have we learned from social learning?," European Economic Review, Elsevier, vol. 40(3-5), pages 617-628, April.
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Cited by:
  1. Antonio Guarino & Antonella Ianni, 2010. "Bayesian Social Learning with Local Interactions," Games, MDPI, Open Access Journal, vol. 1(4), pages 438-458, October.
  2. Christoph March, 2011. "Adaptive social learning," PSE Working Papers halshs-00572528, HAL.
  3. Erik Eyster & Matthew Rabin, 2010. "Na�ve Herding in Rich-Information Settings," American Economic Journal: Microeconomics, American Economic Association, vol. 2(4), pages 221-43, November.
  4. Guarino, Antonio & Harmgart, Heike & Huck, Steffen, 2011. "Aggregate information cascades," Games and Economic Behavior, Elsevier, vol. 73(1), pages 167-185, September.
  5. Vincent Mak & Rami Zwick, 2014. "Experimenting and learning with localized direct communication," Experimental Economics, Springer, vol. 17(2), pages 262-284, June.
  6. repec:hal:wpaper:halshs-00572528 is not listed on IDEAS

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