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Non-Bayesian Social Learning, Second Version

Author

Listed:
  • Ali Jadbabaie

    () (Department of Electrical and Systems Engineering, University of Pennsylvania)

  • Alvaro Sandroni

    () (Kellogg School of Management, Northwestern University)

  • Alireza Tahbaz-Salehi

    () (Laboratory for Information and Decision Systems,Massachusetts Institute of Technology)

Abstract

We develop a dynamic model of opinion formation in social networks. Relevant information is spread throughout the network in such a way that no agent has enough data to learn a payoff-relevant parameter. Individuals engage in communication with their neighbors in order to learn from their experiences. However, instead of incorporating the views of their neighbors in a fully Bayesian manner, agents use a simple updating rule which linearly combines their personal experience and the views of their neighbors (even though the neighbors’ views may be quite inaccurate). This non-Bayesian learning rule is motivated by the formidable complexity required to fully implement Bayesian updating in networks. We show that, under mild assumptions, repeated interactions lead agents to successfully aggregate information and to learn the true underlying state of the world. This result holds in spite of the apparent naıvite of agents’ updating rule, the agents’ need for information from sources (i.e., other agents) the existence of which they may not be aware of, the possibility that the most persuasive agents in the network are precisely those least informed and with worst prior views, and the assumption that no agent can tell whether their own views or their neighbors’ views are more accurate.

Suggested Citation

  • Ali Jadbabaie & Alvaro Sandroni & Alireza Tahbaz-Salehi, 2010. "Non-Bayesian Social Learning, Second Version," PIER Working Paper Archive 10-005, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Feb 2010.
  • Handle: RePEc:pen:papers:10-005
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    Citations

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    Cited by:

    1. Kwon, Seokbeom & Motohashi, Kazuyuki, 2017. "How institutional arrangements in the National Innovation System affect industrial competitiveness: A study of Japan and the U.S. with multiagent simulation," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 221-235.
    2. Bohren, J. Aislinn, 2016. "Informational herding with model misspecification," Journal of Economic Theory, Elsevier, vol. 163(C), pages 222-247.
    3. Alexander Ludwig & Alexander Zimper, 2013. "A decision-theoretic model of asset-price underreaction and overreaction to dividend news," Annals of Finance, Springer, vol. 9(4), pages 625-665, November.
    4. repec:eee:mateco:v:70:y:2017:i:c:p:90-104 is not listed on IDEAS
    5. 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.
    6. Zhang, Hanzhe, 2013. "Evolutionary justifications for non-Bayesian beliefs," Economics Letters, Elsevier, vol. 121(2), pages 198-201.
    7. Füllbrunn, Sascha & Rau, Holger A. & Weitzel, Utz, 2014. "Does ambiguity aversion survive in experimental asset markets?," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 810-826.

    More about this item

    Keywords

    Social networks; learning; information aggregation;

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation

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