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Does one Bayesian make a difference?

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  • Mueller-Frank, Manuel

Abstract

This paper develops a model of repeated interaction in social networks among agents with differing degrees of sophistication. The focus of the model is observational learning; that is, each agent receives initial private information and makes inferences regarding the private information of others through the repeated interaction with his neighbors in the network. The main question is how well agents aggregate private information through their local interactions. I show that in finite networks consisting exclusively of non-Bayesian (boundedly rational) agents, who revise their choices by averaging over the previous period's observed choices, all agents fail to perfectly aggregate the privately held information. However, the presence of at least one Bayesian agent in a strongly connected network is shown to be generically sufficient for every agent, whether Bayesian or non-Bayesian, to perfectly aggregate the private information of all agents.

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  • Mueller-Frank, Manuel, 2014. "Does one Bayesian make a difference?," Journal of Economic Theory, Elsevier, vol. 154(C), pages 423-452.
    • Handle: RePEc:eee:jetheo:v:154:y:2014:i:c:p:423-452
    DOI: 10.1016/j.jet.2014.09.005
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    Citations

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    Cited by:

    1. Ding, Huihui & Pivato, Marcus, 2021. "Deliberation and epistemic democracy," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 138-167.
    2. Elchanan Mossel & Manuel Mueller‐Frank & Allan Sly & Omer Tamuz, 2020. "Social Learning Equilibria," Econometrica, Econometric Society, vol. 88(3), pages 1235-1267, May.
    3. Buechel, Berno & Hellmann, Tim & Klößner, Stefan, 2015. "Opinion dynamics and wisdom under conformity," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 240-257.
    4. Mueller-Frank, Manuel, 2015. "Reaching Consensus in Social Networks," IESE Research Papers D/1116, IESE Business School.
    5. Foerster, Manuel, 2019. "Dynamics of strategic information transmission in social networks," Theoretical Economics, Econometric Society, vol. 14(1), January.
    6. Sushil Bikhchandani & David Hirshleifer & Omer Tamuz & Ivo Welch, 2024. "Information Cascades and Social Learning," Journal of Economic Literature, American Economic Association, vol. 62(3), pages 1040-1093, September.
    7. Polanski, Arnold & Vega-Redondo, Fernando, 2023. "Homophily and influence," Journal of Economic Theory, Elsevier, vol. 207(C).
    8. Buechel, Berno & Klößner, Stefan & Meng, Fanyuan & Nassar, Anis, 2023. "Misinformation due to asymmetric information sharing," Journal of Economic Dynamics and Control, Elsevier, vol. 150(C).
    9. Mueller-Frank, Manuel & Neri, Claudia, 2021. "A general analysis of boundedly rational learning in social networks," Theoretical Economics, Econometric Society, vol. 16(1), January.
    10. Arun G. Chandrasekhar & Horacio Larreguy & Juan Pablo Xandri, 2015. "Testing Models of Social Learning on Networks: Evidence from a Lab Experiment in the Field," NBER Working Papers 21468, National Bureau of Economic Research, Inc.
    11. Michele Crescenzi, 2023. "Group knowledge and individual introspection," Papers 2305.08729, arXiv.org, revised Sep 2024.
    12. Kivinen, Steven & Tumennasan, Norovsambuu, 2019. "Consensus in social networks: Revisited," Journal of Mathematical Economics, Elsevier, vol. 83(C), pages 11-18.
    13. Michel Grabisch & Antoine Mandel & Agnieszka Rusinowska & Emily Tanimura, 2018. "Strategic Influence in Social Networks," Mathematics of Operations Research, INFORMS, vol. 43(1), pages 29-50, February.
    14. Syngjoo Choi & Edoardo Gallo & Shachar Kariv, 2015. "Networks in the laboratory," Cambridge Working Papers in Economics 1551, Faculty of Economics, University of Cambridge.
    15. Mueller-Frank, Manuel & Arieliy, Itai, 2015. "A General Model of Boundedly Rational Observational Learning: Theory and Experiment," IESE Research Papers D/1120, IESE Business School.
    16. Dimitri Migrow, 2022. "Strategic Observational Learning," Papers 2212.09889, arXiv.org, revised Jan 2023.
    17. Li, Wei & Tan, Xu, 2021. "Cognitively-constrained learning from neighbors," Games and Economic Behavior, Elsevier, vol. 129(C), pages 32-54.

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    More about this item

    Keywords

    Social networks; Information aggregation; Bayesian learning; Boundedly rational learning; Social learning; Consensus;
    All these keywords.

    JEL classification:

    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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