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Bayesian Learning in Social Networks

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  • Daron Acemoglu
  • Munther A. Dahleh
  • Ilan Lobel
  • Asuman Ozdaglar

Abstract

We study the (perfect Bayesian) equilibrium of a sequential learning model over a general social network. Each individual receives a signal about the underlying state of the world, observes the past actions of a stochastically generated neighbourhood of individuals, and chooses one of two possible actions. The stochastic process generating the neighbourhoods defines the network topology. We characterize pure strategy equilibria for arbitrary stochastic and deterministic social networks and characterize the conditions under which there will be asymptotic learning--convergence (in probability) to the right action as the social network becomes large. We show that when private beliefs are unbounded (meaning that the implied likelihood ratios are unbounded), there will be asymptotic learning as long as there is some minimal amount of "expansion in observations". We also characterize conditions under which there will be asymptotic learning when private beliefs are bounded. Copyright 2011, Oxford University Press.

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File URL: http://hdl.handle.net/10.1093/restud/rdr004
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Bibliographic Info

Article provided by Oxford University Press in its journal The Review of Economic Studies.

Volume (Year): 78 (2011)
Issue (Month): 4 ()
Pages: 1201-1236

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Handle: RePEc:oup:restud:v:78:y:2011:i:4:p:1201-1236

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Citations

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Cited by:
  1. Junjie Zhou & Ying-Ju Chen, 2013. "Targeted information release in social networks," Working Papers 13-04, NET Institute.
  2. Gabriel Galand, 2009. "The Neutrality of Money Revisited with a Bottom-Up Approach: Decentralisation, Limited Information and Bounded Rationality," Computational Economics, Society for Computational Economics, vol. 33(4), pages 337-360, May.
  3. Vivi Alatas & Abhijit Banerjee & Arun G. Chandrasekhar & Rema Hanna & Benjamin A. Olken, 2012. "Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia," NBER Working Papers 18351, National Bureau of Economic Research, Inc.
  4. Luca Corazzini & Filippo Pavesi & Beatrice Petrovich & Luca Stanca, 2010. "Influential Listeners: An Experiment on Persuasion Bias in Social Networks," Working Papers 196, University of Milano-Bicocca, Department of Economics, revised Aug 2010.
  5. Frederic Koessler & Anthony Ziegelmeyer & Juergen Bracht & Eyal Winter, 2008. "Fragility of Information Cascades: An Experimental Study using Elicited Beliefs," Jena Economic Research Papers 2008-094, Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics.
  6. Marco Pelliccia, 2013. "Ambiguous Networks," Birkbeck Working Papers in Economics and Finance 1303, Birkbeck, Department of Economics, Mathematics & Statistics.
  7. Ilan Lobel & Evan Sadler, 2013. "Preferences, Homophily, and Social Learning," Working Papers 13-01, NET Institute.
  8. Berno Buechel & Tim Hellmann & Stefan Kölßner, 2014. "Opinion Dynamics and Wisdom under Conformity," Working Papers 2014.51, Fondazione Eni Enrico Mattei.
  9. Manuel Förster & Ana Mauleon & Vincent Vannetelbosch, 2013. "Trust and Manipulation in Social Networks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00881145, HAL.
  10. Jadbabaie, Ali & Molavi, Pooya & Sandroni, Alvaro & Tahbaz-Salehi, Alireza, 2012. "Non-Bayesian social learning," Games and Economic Behavior, Elsevier, vol. 76(1), pages 210-225.
  11. Opolot, Daniel & Azomahou, Theophile, 2012. "Learning and convergence in networks," MERIT Working Papers 074, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
  12. Opolot, Daniel, 2012. "Social interactions and complex networks," MERIT Working Papers 014, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
  13. Acemoglu, Daron & Ozdaglar, Asuman & ParandehGheibi, Ali, 2010. "Spread of (mis)information in social networks," Games and Economic Behavior, Elsevier, vol. 70(2), pages 194-227, November.
  14. Pietro Battiston & Luca Stanca, 2014. "Boundedly Rational Opinion Dynamics in Directed Social Networks: Theory and Experimental Evidence," Working Papers 267, University of Milano-Bicocca, Department of Economics, revised Jan 2014.

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