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Social learning in networks: a Quantal Response Equilibrium analysis of experimental data

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  • Syngjoo Choi

    ()

  • Douglas Gale

    ()

  • Shachar Kariv

    ()

Abstract

Individuals living in society are bound together by a social network and, in many social and economic situations, individuals learn by observing the behavior of others in their local environment. This process is called social learning. Learning in incomplete networks, where different individuals have different information, is especially challenging: because of the lack of common knowledge individuals must draw inferences about the actions others have observed, as well as about their private information. This paper reports an experimental investigation of learning in three-person networks and uses the theoretical framework of Gale and Kariv (Games Econ Behav 45:329–346, 2003 ) to interpret the data generated by the experiments. The family of three-person networks includes several non-trivial architectures, each of which gives rise to its own distinctive learning patterns. To test the usefulness of the theory in interpreting the data, we adapt the Quantal Response Equilibrium (QRE) model of Mckelvey and Palfrey (Games Econ Behav 10:6–38, 1995 ; Exp Econ 1:9–41, 1998 ). We find that the theory can account for the behavior observed in the laboratory in a variety of networks and informational settings. This provides important support for the use of QRE to interpret experimental data. Copyright Springer-Verlag 2012

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Bibliographic Info

Article provided by Springer in its journal Review of Economic Design.

Volume (Year): 16 (2012)
Issue (Month): 2 (September)
Pages: 135-157

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Handle: RePEc:spr:reecde:v:16:y:2012:i:2:p:135-157

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Keywords: Social networks; Social learning; Quantal Response Equilibrium (QRE); Experiment; D82; D83; C92;

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  1. Jacob Goeree & Thomas Palfrey & Brian Rogers, 2004. "Self-Correcting Information Cascades," Levine's Bibliography 122247000000000153, UCLA Department of Economics.
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  13. Munshi, Kaivan, 2004. "Social learning in a heterogeneous population: technology diffusion in the Indian Green Revolution," Journal of Development Economics, Elsevier, vol. 73(1), pages 185-213, February.
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  20. Syngjoo Choi, 2012. "A cognitive hierarchy model of learning in networks," Review of Economic Design, Springer, vol. 16(2), pages 215-250, September.
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Cited by:
  1. 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).
  2. 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.
  3. Matthew O. Jackson & Benjamin Golub, 2007. "Naïve Learning in Social Networks: Convergence, Influence and Wisdom of Crowds," Working Papers 2007.64, Fondazione Eni Enrico Mattei.

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