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Analysis of opinion evolution based on non-Bayesian social learning

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  • Liu, Ying
  • Fang, Aili

Abstract

In a reposting network on a social platform, users form their own opinions relying on personal experience as well as the influence of neighboring users. We thus use a non-Bayesian social learning model to optimally integrate the user's social observations into the Bayesian inference process, where the users receive information from their neighbors and update opinions to be a weighted linear combination of the Bayesian posterior opinion and the neighbors opinions. By crawling the microblog topic, we conduct simulations and empirical analysis of the repost network under the topic. The simulation results show that users' opinions tend to reach group consensus and are similar to the empirical results.

Suggested Citation

  • Liu, Ying & Fang, Aili, 2024. "Analysis of opinion evolution based on non-Bayesian social learning," Applied Mathematics and Computation, Elsevier, vol. 464(C).
    • Handle: RePEc:eee:apmaco:v:464:y:2024:i:c:s0096300323005684
    DOI: 10.1016/j.amc.2023.128399
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    References listed on IDEAS

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    1. Boğaçhan Çelen & Shachar Kariv, 2005. "An experimental test of observational learning under imperfect information," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 26(3), pages 677-699, October.
    2. 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.
    3. Jadbabaie, Ali & Molavi, Pooya & Sandroni, Alvaro & Tahbaz-Salehi, Alireza, 2012. "Non-Bayesian social learning," Games and Economic Behavior, Elsevier, vol. 76(1), pages 210-225.
    4. Fang, Aili, 2021. "The influence of communication structure on opinion dynamics in social networks with multiple true states," Applied Mathematics and Computation, Elsevier, vol. 406(C).
    5. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
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