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A Theory and Experiments of Learning in Social Networks

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  • Kariv, Shachar
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    Abstract

    Individuals living in society are bound together by a social network, the complex of relationships that brings them into contact with other agents. 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 agents have different information sets, 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. Whether individuals can rationally process the information available in a network is ultimately an empirical question. This paper reports an experimental investigation of learning in three-person networks and uses the theoretical framework Gale and Kariv (2003) to interpret the data generated by the experiments. The family of three-person networks includes several nontrivial architectures, each of which gives rise to its own distinctive learning patterns. We find that the theory can account for the behavior observed in the laboratory in variety of networks and informational settings. To account for errors in subjects’ behavior, we adapt the model of Quantal Response Equilibrium of McKelvey and Palfrey (1995, 1998) and find that its restrictions are also confirmed. The ‘goodness of fit’ is better for the QRE model than for the game-theory model. This provides important support for the use of QRE to interpret experimental data.

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    Bibliographic Info

    Paper provided by Department of Economics, UC Santa Cruz in its series Santa Cruz Department of Economics, Working Paper Series with number qt8853k4jd.

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    Date of creation: 29 Aug 2004
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    Handle: RePEc:cdl:ucscec:qt8853k4jd

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    1. Richard Mckelvey & Thomas Palfrey, 1998. "Quantal Response Equilibria for Extensive Form Games," Experimental Economics, Springer, vol. 1(1), pages 9-41, June.
    2. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, April.
    3. Banerjee, Abhijit V, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, MIT Press, vol. 107(3), pages 797-817, August.
    4. Mark Rosenzweig & Andrew D. Foster, . "Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture," Home Pages _068, University of Pennsylvania.
    5. Syngjoo Choi & Douglas Gale & Shachar Kariv, 2005. "Learning in Networks: An Experimental Study," Levine's Bibliography 122247000000000044, UCLA Department of Economics.
    6. Kosfeld Michael, 2004. "Economic Networks in the Laboratory: A Survey," Review of Network Economics, De Gruyter, vol. 3(1), pages 1-23, March.
    7. Bala, V. & Goyal, S., 1995. "Learning from Neighbors," Econometric Institute Research Papers EI 9549-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Smith, L. & Sorensen, P., 1996. "Pathological Outcomes of Observational Learning," Working papers 96-19, Massachusetts Institute of Technology (MIT), Department of Economics.
    9. Esther Duflo & Emmanuel Saez, 2003. "The Role Of Information And Social Interactions In Retirement Plan Decisions: Evidence From A Randomized Experiment," The Quarterly Journal of Economics, MIT Press, vol. 118(3), pages 815-842, August.
    10. John Xu Zheng, 1996. "A consistent test of functional form via nonparametric estimation techniques," Journal of Econometrics, Elsevier, vol. 75(2), pages 263-289, December.
    11. 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.
    12. Bogaçhan Çelen & Shachar Kariv, 2004. "Distinguishing Informational Cascades from Herd Behavior in the Laboratory," American Economic Review, American Economic Association, vol. 94(3), pages 484-498, June.
    13. Gale, Douglas & Kariv, Shachar, 2003. "Bayesian learning in social networks," Games and Economic Behavior, Elsevier, vol. 45(2), pages 329-346, November.
    14. Anderson, Lisa R & Holt, Charles A, 1997. "Information Cascades in the Laboratory," American Economic Review, American Economic Association, vol. 87(5), pages 847-62, December.
    15. Angela A. Hung & Charles R. Plott, 2001. "Information Cascades: Replication and an Extension to Majority Rule and Conformity-Rewarding Institutions," American Economic Review, American Economic Association, vol. 91(5), pages 1508-1520, December.
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