IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v464y2024ics0096300323005684.html
   My bibliography  Save this article

Analysis of opinion evolution based on non-Bayesian social learning

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300323005684
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2023.128399?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Buechel, Berno & Hellmann, Tim & Klößner, Stefan, 2015. "Opinion dynamics and wisdom under conformity," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 240-257.
    2. Dasaratha, Krishna & He, Kevin, 2020. "Network structure and naive sequential learning," Theoretical Economics, Econometric Society, vol. 15(2), May.
    3. Azzimonti, Marina & Fernandes, Marcos, 2023. "Social media networks, fake news, and polarization," European Journal of Political Economy, Elsevier, vol. 76(C).
    4. 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.
    5. Michel Grabisch & Agnieszka Rusinowska, 2020. "A Survey on Nonstrategic Models of Opinion Dynamics," Games, MDPI, vol. 11(4), pages 1-29, December.
    6. Ionel Popescu & Tushar Vaidya, 2019. "Averaging plus Learning Models and Their Asymptotics," Papers 1904.08131, arXiv.org, revised Jul 2023.
    7. Battiston, Pietro & Stanca, Luca, 2015. "Boundedly rational opinion dynamics in social networks: Does indegree matter?," Journal of Economic Behavior & Organization, Elsevier, vol. 119(C), pages 400-421.
    8. 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).
    9. Gallo, E. & Langtry, A., 2020. "Social Networks, Confirmation Bias and Shock Elections," Cambridge Working Papers in Economics 2099, Faculty of Economics, University of Cambridge.
    10. Rajiv Sethi & Muhamet Yildiz, 2013. "Perspectives, Opinions, and Information Flows," Levine's Working Paper Archive 786969000000000934, David K. Levine.
    11. Hirshleifer, David & Teoh, Siew Hong, 2008. "Thought and Behavior Contagion in Capital Markets," MPRA Paper 9164, University Library of Munich, Germany.
    12. Ding, Huihui & Pivato, Marcus, 2021. "Deliberation and epistemic democracy," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 138-167.
    13. Marcos Fernandes, 2019. "Confirmation Bias in Social Networks," Department of Economics Working Papers 19-05, Stony Brook University, Department of Economics.
    14. Jan Hązła & Ali Jadbabaie & Elchanan Mossel & M. Amin Rahimian, 2021. "Bayesian Decision Making in Groups is Hard," Operations Research, INFORMS, vol. 69(2), pages 632-654, March.
    15. Edoardo Gallo & Alastair Langtry, 2020. "Social networks, confirmation bias and shock elections," Papers 2011.00520, arXiv.org.
    16. , & ,, 2015. "Information diffusion in networks through social learning," Theoretical Economics, Econometric Society, vol. 10(3), September.
    17. Catherine A. Glass & David H. Glass, 2021. "Social Influence of Competing Groups and Leaders in Opinion Dynamics," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 799-823, October.
    18. Lou, Youcheng & Wang, Shouyang, 2021. "The equivalence of two rational expectations equilibrium economies with different approaches to processing neighbors’ information," Mathematical Social Sciences, Elsevier, vol. 109(C), pages 93-105.
    19. Li, Wei & Tan, Xu, 2021. "Cognitively-constrained learning from neighbors," Games and Economic Behavior, Elsevier, vol. 129(C), pages 32-54.
    20. Guodong Shi & Alexandre Proutiere & Mikael Johansson & John S. Baras & Karl H. Johansson, 2016. "The Evolution of Beliefs over Signed Social Networks," Operations Research, INFORMS, vol. 64(3), pages 585-604, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:464:y:2024:i:c:s0096300323005684. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.