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Locally Bayesian learning in networks

Author

Listed:
  • Li, Wei

    (Department of Economics, University of British Columbia)

  • Tan, Xu

    (Department of Economics, University of Washington)

Abstract
Agents in a network want to learn the true state of the world from their own signals and their neighbors' reports. Agents know only their local networks, consisting of their neighbors and the links among them. Every agent is Bayesian with the (possibly misspecified) prior belief that her local network is the entire network. We present a tractable learning rule to implement such locally Bayesian learning: each agent extracts new information using the full history of observed reports in her local network. Despite their limited network knowledge, agents learn correctly when the network is a social quilt, a tree-like union of cliques. But they fail to learn when a network contains interlinked circles (echo chambers), despite an arbitrarily large number of correct signals.

Suggested Citation

  • Li, Wei & Tan, Xu, 2020. "Locally Bayesian learning in networks," Theoretical Economics, Econometric Society, vol. 15(1), January.
  • Handle: RePEc:the:publsh:3273
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    References listed on IDEAS

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    Cited by:

    1. Laurent, Thibault & Panova, Elena, 2020. "Clustering in communication networks with different-minded participants," TSE Working Papers 20-1147, Toulouse School of Economics (TSE).
    2. Rapanos, Theodoros, 2023. "What makes an opinion leader: Expertise vs popularity," Games and Economic Behavior, Elsevier, vol. 138(C), pages 355-372.
    3. Li, Wei & Tan, Xu, 2021. "Cognitively-constrained learning from neighbors," Games and Economic Behavior, Elsevier, vol. 129(C), pages 32-54.
    4. Alem, Yonas & Dugoua, Eugenie, 2021. "Learning from unincentivized and incentivized communication: A randomized controlled trial in India," Ruhr Economic Papers 895, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    5. Nathan Canen & Jacob Schwartz & Kyungchul Song, 2020. "Estimating local interactions among many agents who observe their neighbors," Quantitative Economics, Econometric Society, vol. 11(3), pages 917-956, July.
    6. Alem, Yonas & Dugoua, Eugenie, 2022. "Learning from unincentivized and incentivized communication: a randomized controlled trial in India," LSE Research Online Documents on Economics 110858, London School of Economics and Political Science, LSE Library.
    7. Jan Hązła & Ali Jadbabaie & Elchanan Mossel & M. Amin Rahimian, 2021. "Bayesian Decision Making in Groups is Hard," Operations Research, INFORMS, vol. 69(2), pages 632-654, March.
    8. Promit K. Chaudhuri & Matthew O. Jackson & Sudipta Sarangi & Hector Tzavellas, 2023. "Games Under Network Uncertainty," Papers 2305.03124, arXiv.org, revised Dec 2024.

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    More about this item

    Keywords

    Locally Bayesian learning; rational learning with misspecified priors; efficient learning in finite networks;
    All these keywords.

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • 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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