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Information diffusion in networks with the Bayesian Peer Influence heuristic

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  • Levy, Gilat
  • Razin, Ronny

Abstract

Repeated communication in networks is often considered to impose large information requirements on individuals, and for that reason, the literature has resorted to use heuristics, such as DeGroot's, to compute how individuals update beliefs. In this paper we propose a new heuristic which we term the Bayesian Peer Influence (BPI) heuristic. The BPI accords with Bayesian updating for all (conditionally) independent information structures. More generally, the BPI can be used to analyze the effects of correlation neglect on communication in networks. We analyze the evolution of beliefs and show that the limit is a simple extension of the BPI and parameters of the network structure. We also show that consensus in society might change dynamically, and that beliefs might become polarised. These results contrast with those obtained in papers that have used the DeGroot heuristic.

Suggested Citation

  • Levy, Gilat & Razin, Ronny, 2018. "Information diffusion in networks with the Bayesian Peer Influence heuristic," Games and Economic Behavior, Elsevier, vol. 109(C), pages 262-270.
    • Handle: RePEc:eee:gamebe:v:109:y:2018:i:c:p:262-270
    DOI: 10.1016/j.geb.2017.12.020
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Goldbaum, David, 2021. "The origins of influence," Economic Modelling, Elsevier, vol. 97(C), pages 380-396.
    2. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Belief Convergence under Misspecified Learning: A Martingale Approach," Cowles Foundation Discussion Papers 2235R2, Cowles Foundation for Research in Economics, Yale University, revised Dec 2021.
    3. Philipp Denter & Martin Dumav & Boris Ginzburg, 2021. "Social Connectivity, Media Bias, and Correlation Neglect," The Economic Journal, Royal Economic Society, vol. 131(637), pages 2033-2057.
    4. Bräuninger, Thomas & Marinov, Nikolay, 2022. "Political elites and the “War on Truth’’," Journal of Public Economics, Elsevier, vol. 206(C).
    5. Li, Wei & Tan, Xu, 2020. "Locally Bayesian learning in networks," Theoretical Economics, Econometric Society, vol. 15(1), January.
    6. Dasaratha, Krishna & He, Kevin, 2020. "Network structure and naive sequential learning," Theoretical Economics, Econometric Society, vol. 15(2), May.
    7. 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).
    8. Arieli, Itai & Babichenko, Yakov & Shlomov, Segev, 2021. "Virtually additive learning," Journal of Economic Theory, Elsevier, vol. 197(C).
    9. Polanski, Arnold & Vega-Redondo, Fernando, 2023. "Homophily and influence," Journal of Economic Theory, Elsevier, vol. 207(C).
    10. Li, Wei & Tan, Xu, 2021. "Cognitively-constrained learning from neighbors," Games and Economic Behavior, Elsevier, vol. 129(C), pages 32-54.
    11. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Belief Convergence under Misspecified Learning: A Martingale Approach," Cowles Foundation Discussion Papers 2235R3, Cowles Foundation for Research in Economics, Yale University, revised Apr 2022.
    12. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Stability and Robustness in Misspecified Learning Models," Cowles Foundation Discussion Papers 2235, Cowles Foundation for Research in Economics, Yale University.

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    More about this item

    Keywords

    Correlation neglect; Learning in networks; Bayesian heuristic; Polarisation;
    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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