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A general analysis of boundedly rational learning in social networks

Author

Listed:
  • Mueller-Frank, Manuel

    (Department of Economics, IESE Business School)

  • Neri, Claudia

    (Zurich Insurance Company)

Abstract

We analyze boundedly rational learning in social networks within binary action environments. We establish how learning outcomes depend on the environment (i.e., informational structure, utility function), the axioms imposed on the updating behavior, and the network structure. In particular, we provide a normative foundation for Quasi-Bayesian updating, where a Quasi-Bayesian agent treats others' actions as if they were based only on their private signal. Quasi-Bayesian updating induces learning (i.e., convergence to the optimal action for every agent in every connected network) only in highly asymmetric environments. In all other environments learning fails in networks with a diameter larger than four. Finally, we consider a richer class of updating behavior that allows for non-stationarity and differential treatment of neighbors' actions depending on their position in the network. We show that within this class there exist updating systems which induce learning for most networks.

Suggested Citation

  • Mueller-Frank, Manuel & Neri, Claudia, 2021. "A general analysis of boundedly rational learning in social networks," Theoretical Economics, Econometric Society, vol. 16(1), January.
  • Handle: RePEc:the:publsh:2974
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    References listed on IDEAS

    as
    1. Mueller-Frank, Manuel, 2014. "Does one Bayesian make a difference?," Journal of Economic Theory, Elsevier, vol. 154(C), pages 423-452.
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    Cited by:

    1. Miguel Risco & Manuel Lleonart-Anguix, 2024. "Feed for good? On the effects of personalization algorithms in social platforms," CRC TR 224 Discussion Paper Series crctr224_2024_580, University of Bonn and University of Mannheim, Germany.
    2. Arieli, Itai & Babichenko, Yakov & Shlomov, Segev, 2021. "Virtually additive learning," Journal of Economic Theory, Elsevier, vol. 197(C).
    3. Alex Centeno, 2022. "A Structural Model for Detecting Communities in Networks," Papers 2209.08380, arXiv.org, revised Oct 2022.
    4. Abhijit Banerjee & Olivier Compte, 2024. "Consensus and Disagreement: Information Aggregation under (Not So) Naive Learning," Journal of Political Economy, University of Chicago Press, vol. 132(8), pages 2790-2829.
    5. Mueller-Frank, Manuel, 2024. "As strong as the weakest node: The impact of misinformation in social networks," Journal of Economic Theory, Elsevier, vol. 215(C).
    6. 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; naive inference; information aggregation; bounded rationality; agreement;
    All these keywords.

    JEL classification:

    • 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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