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Social Leanring with Course Inference

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  • Antonio Guarino

    (University College London)

  • Philippe Jehiel

    (Paris School of Economics)

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 understand their predecessors’ decisions in their finest details: they only understand the relation between the aggregate distribution of actions and the state of nature. We show that, in a continuous action space, compared to the rational case, agents put more weight on early signals. Despite this behavioral bias, beliefs converge to the truth. In a discrete action space, instead, convergence to the truth does not occur even if agents receive signals of unbounded precisions.

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Bibliographic Info

Paper provided by ESRC World Economy and Finance Research Programme, Birkbeck, University of London in its series WEF Working Papers with number 0050.

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Date of creation: Jul 2009
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Handle: RePEc:wef:wpaper:0050

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References

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  1. Georg Weizsacker, 2010. "Do We Follow Others When We Should? A Simple Test of Rational Expectations," American Economic Review, American Economic Association, vol. 100(5), pages 2340-60, December.
  2. Philippe Jeniel, 2001. "Analogy-Based Expectation Equilibrium," Economics Working Papers 0003, Institute for Advanced Study, School of Social Science.
  3. Peter M. DeMarzo & Dimitri Vayanos & Jeffrey Zwiebel, 2003. "Persuasion bias, social influence, and uni-dimensional opinions," LSE Research Online Documents on Economics 454, London School of Economics and Political Science, LSE Library.
  4. Celen, Bogachan & Kariv, Shachar, 2004. "Observational learning under imperfect information," Games and Economic Behavior, Elsevier, vol. 47(1), pages 72-86, April.
  5. Sushil Bikhchandani & David Hirshleifer & Ivo Welch, 2010. "A theory of Fads, Fashion, Custom and cultural change as informational Cascades," Levine's Working Paper Archive 1193, David K. Levine.
  6. Allison, G. & Fudenberg, D., 1992. "Rules of Thumb for Social Learning," Working papers 92-12, Massachusetts Institute of Technology (MIT), Department of Economics.
  7. Erik Eyster & Matthew Rabin, 2005. "Cursed Equilibrium," Econometrica, Econometric Society, vol. 73(5), pages 1623-1672, 09.
  8. Gale, Douglas, 1996. "What have we learned from social learning?," European Economic Review, Elsevier, vol. 40(3-5), pages 617-628, April.
  9. Steffen Huck & Joerg Oechssler, 1997. "Informational Cascades with Continuous Action Spaces," Game Theory and Information 9710002, EconWPA.
  10. Dorothea K¸bler & Georg Weizs”cker, 2004. "Limited Depth of Reasoning and Failure of Cascade Formation in the Laboratory," Review of Economic Studies, Wiley Blackwell, vol. 71(2), pages 425-441, 04.
  11. Banerjee, Abhijit V, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, MIT Press, vol. 107(3), pages 797-817, August.
  12. 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.
  13. Hirshleifer, David & Teoh, Siew Hong, 2001. "Herd Behavior and Cascading in Capital Markets: A Review and Synthesis," MPRA Paper 5186, University Library of Munich, Germany.
  14. Banerjee, Abhijit & Fudenberg, Drew, 2004. "Word-of-mouth learning," Games and Economic Behavior, Elsevier, vol. 46(1), pages 1-22, January.
  15. 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.
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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.

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