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Information diffusion in networks through social learning

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  • ,

    (IOMS Department, Stern School of Business, New York University)

  • ,

    (IOMS Department, Stern School of Business, New York University)

Abstract
We study perfect Bayesian equilibria of a sequential social learning model in which agents in a network learn about an underlying state by observing neighbors' choices. In contrast with prior work, we do not assume that the agents' sets of neighbors are mutually independent. We introduce a new metric of information diffusion in social learning, which is weaker than the traditional aggregation metric. We show that if a minimal connectivity condition holds and neighborhoods are independent, information always diffuses. Diffusion can fail in a well-connected network if neighborhoods are correlated. We show that information diffuses if neighborhood realizations convey little information about the network, as measured by network distortion, or if information asymmetries are captured through beliefs over the state of a finite Markov chain.

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  • , & ,, 2015. "Information diffusion in networks through social learning," Theoretical Economics, Econometric Society, vol. 10(3), September.
    • Handle: RePEc:the:publsh:1549
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    References listed on IDEAS

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    Cited by:

    1. Ignacio Monzón, 2017. "Aggregate Uncertainty Can Lead to Incorrect Herds," American Economic Journal: Microeconomics, American Economic Association, vol. 9(2), pages 295-314, May.
    2. Dasaratha, Krishna & He, Kevin, 2020. "Network structure and naive sequential learning," Theoretical Economics, Econometric Society, vol. 15(2), May.
    3. Navin Kartik & SangMok Lee & Tianhao Liu & Daniel Rappoport, 2021. "Beyond Unbounded Beliefs: How Preferences and Information Interplay in Social Learning," Papers 2103.02754, arXiv.org, revised Apr 2024.
    4. Kakhbod, Ali & Loginova, Uliana, 2023. "When does introducing verifiable communication choices improve welfare?," Journal of Economic Behavior & Organization, Elsevier, vol. 210(C), pages 139-162.
    5. Harry Pei, 2020. "Reputation Building under Observational Learning," Papers 2006.08068, arXiv.org, revised Nov 2020.
    6. Enrique Urbano Arellano & Xinyang Wang, 2023. "Social Learning of General Rules," Papers 2310.15861, arXiv.org.
    7. Arieli, Itai, 2017. "Payoff externalities and social learning," Games and Economic Behavior, Elsevier, vol. 104(C), pages 392-410.
    8. Diefeng Peng & Yulei Rao & Xianming Sun & Erte Xiao, 2019. "Optional Disclosure and Observational Learning," Monash Economics Working Papers 05-18, Monash University, Department of Economics.
    9. Krishna Dasaratha & Kevin He, 2019. "Aggregative Efficiency of Bayesian Learning in Networks," Papers 1911.10116, arXiv.org, revised Sep 2024.
    10. Ilan Lobel & Evan Sadler, 2016. "Preferences, Homophily, and Social Learning," Operations Research, INFORMS, vol. 64(3), pages 564-584, June.
    11. Song, Yangbo & Zhang, Jiahua, 2020. "Social learning with coordination motives," Games and Economic Behavior, Elsevier, vol. 123(C), pages 81-100.
    12. Sadler, Evan, 2020. "Innovation adoption and collective experimentation," Games and Economic Behavior, Elsevier, vol. 120(C), pages 121-131.
    13. Armando Razo, 2020. "Social dilemmas with manifest and unknown networks," Rationality and Society, , vol. 32(1), pages 3-39, February.
    14. 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.
    15. Dasaratha, Krishna & He, Kevin, 2021. "An experiment on network density and sequential learning," Games and Economic Behavior, Elsevier, vol. 128(C), pages 182-192.
    16. Ding, Huihui & Pivato, Marcus, 2021. "Deliberation and epistemic democracy," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 138-167.
    17. Sebastiano Della Lena, 2019. "Non-Bayesian Social Learning and the Spread of Misinformation in Networks," Working Papers 2019:09, Department of Economics, University of Venice "Ca' Foscari".
    18. Aleksei Smirnov & Egor Starkov, 2024. "Designing Social Learning," Papers 2405.05744, arXiv.org, revised May 2024.
    19. Parakhonyak, Alexei & Vikander, Nick, 2023. "Information design through scarcity and social learning," Journal of Economic Theory, Elsevier, vol. 207(C).

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    More about this item

    Keywords

    Social networks; Bayesian learning; information aggregation; herding;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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