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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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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. 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.
  2. Marco Pelliccia, 2013. "Ambiguous Networks," Birkbeck Working Papers in Economics and Finance 1303, Birkbeck, Department of Economics, Mathematics & Statistics.
  3. repec:dgr:unumer:2012014 is not listed on IDEAS
  4. 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.
  5. Berno Buechel & Tim Hellmann & Stefan Kloessner, 2012. "Opinion Dynamics under Conformity," Working Papers 469, Bielefeld University, Center for Mathematical Economics.
  6. FORSTER, Manuel & MAULEON, Ana & VANNETELBOSCH, Vincent, 2013. "Trust and manipulation in social networks," CORE Discussion Papers 2013050, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  7. 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.
  8. 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.
  9. Ilan Lobel & Evan Sadler, 2013. "Preferences, Homophily, and Social Learning," Working Papers 13-01, NET Institute.
  10. 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).
  11. 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.
  12. 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.
  13. 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).
  14. Junjie Zhou & Ying-Ju Chen, 2013. "Targeted information release in social networks," Working Papers 13-04, NET Institute.

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