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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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    1. Duffie, Darrell & Malamud, Semyon & Manso, Gustavo, 2014. "Information percolation in segmented markets," Journal of Economic Theory, Elsevier, vol. 153(C), pages 1-32.
    2. , & , & ,, 2014. "Dynamics of information exchange in endogenous social networks," Theoretical Economics, Econometric Society, vol. 9(1), January.
    3. Antonio Guarino & Philippe Jehiel, 2013. "Social Learning with Coarse Inference," American Economic Journal: Microeconomics, American Economic Association, vol. 5(1), pages 147-174, February.
    4. Venkatesh Bala & Sanjeev Goyal, 1998. "Learning from Neighbours," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 595-621.
    5. Levy, Gilat & Razin, Ronny, 2015. "Does Polarisation of Opinions Lead to Polarisation of Platforms? The Case of Correlation Neglect," Quarterly Journal of Political Science, now publishers, vol. 10(3), pages 321-355, September.
    6. Darrell Duffie & Gaston Giroux & Gustavo Manso, 2010. "Information Percolation," American Economic Journal: Microeconomics, American Economic Association, vol. 2(1), pages 100-111, February.
    7. Benjamin Enke & Florian Zimmermann, 2019. "Correlation Neglect in Belief Formation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(1), pages 313-332.
    8. ,, 2014. "On the relationship between individual and group decisions," Theoretical Economics, Econometric Society, vol. 9(1), January.
    9. Syngjoo Choi & Douglas Gale & Shachar Kariv, 2012. "Social learning in networks: a Quantal Response Equilibrium analysis of experimental data," Review of Economic Design, Springer;Society for Economic Design, vol. 16(2), pages 135-157, September.
    10. Benjamin Golub & Matthew O. Jackson, 2012. "How Homophily Affects the Speed of Learning and Best-Response Dynamics," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(3), pages 1287-1338.
    11. Peter M. DeMarzo & Dimitri Vayanos & Jeffrey Zwiebel, 2003. "Persuasion Bias, Social Influence, and Unidimensional Opinions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 909-968.
    12. Erik Eyster & Matthew Rabin, 2014. "Extensive Imitation is Irrational and Harmful," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(4), pages 1861-1898.
    13. Benjamin Golub & Matthew O. Jackson, 2010. "Naïve Learning in Social Networks and the Wisdom of Crowds," American Economic Journal: Microeconomics, American Economic Association, vol. 2(1), pages 112-149, February.
    14. Gilat Levy & Ronny Razin, 2015. "Correlation Neglect, Voting Behavior, and Information Aggregation," American Economic Review, American Economic Association, vol. 105(4), pages 1634-1645, April.
    15. Erik Eyster & Georg Weizsäcker, 2011. "Correlation Neglect in Financial Decision-Making," Discussion Papers of DIW Berlin 1104, DIW Berlin, German Institute for Economic Research.
    16. Erik Eyster & Matthew Rabin, 2010. "Naïve Herding in Rich-Information Settings," American Economic Journal: Microeconomics, American Economic Association, vol. 2(4), pages 221-243, November.
    17. Joel Sobel, 2014. "On the relationship between individual and group decisions," Levine's Working Paper Archive 786969000000000950, David K. Levine.
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    Citations

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    Cited by:

    1. Dasaratha, Krishna & He, Kevin, 2020. "Network structure and naive sequential learning," Theoretical Economics, Econometric Society, vol. 15(2), May.
    2. 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.
    3. Arieli, Itai & Babichenko, Yakov & Shlomov, Segev, 2021. "Virtually additive learning," Journal of Economic Theory, Elsevier, vol. 197(C).
    4. Polanski, Arnold & Vega-Redondo, Fernando, 2023. "Homophily and influence," Journal of Economic Theory, Elsevier, vol. 207(C).
    5. Goldbaum, David, 2021. "The origins of influence," Economic Modelling, Elsevier, vol. 97(C), pages 380-396.
    6. Bräuninger, Thomas & Marinov, Nikolay, 2022. "Political elites and the “War on Truth’’," Journal of Public Economics, Elsevier, vol. 206(C).
    7. Li, Wei & Tan, Xu, 2020. "Locally Bayesian learning in networks," Theoretical Economics, Econometric Society, vol. 15(1), January.
    8. Li, Wei & Tan, Xu, 2021. "Cognitively-constrained learning from neighbors," Games and Economic Behavior, Elsevier, vol. 129(C), pages 32-54.
    9. 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.
    10. 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.
    11. 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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