IDEAS home Printed from https://ideas.repec.org/p/tin/wpaper/20180018.html
   My bibliography  Save this paper

Naive Learning in Social Networks with Random Communication

Author

Listed:
  • Bernd (B.) Heidergott

    (VU Amsterdam, The Netherlands)

  • Jia-Ping Huang

    (Shenzhen University, China)

  • Ines (I.) Lindner

    (VU Amsterdam, The Netherlands)

Abstract

We study social learning in a social network setting where agents receive independent noisy signals about the truth. Agents naïvely update beliefs by repeatedly taking weighted averages of neighbors' opinions. The weights are fixed in the sense of representing average frequency and intensity of social interaction. However, the way people communicate is random such that agents do not update their belief in exactly the same way at every point in time. We show that even if the social network does not privilege any agent in terms of influence, a large society almost always fails to converge to the truth. We conclude that wisdom of crowds is an illusive concept and bares the danger of mistaking consensus for truth.

Suggested Citation

  • Bernd (B.) Heidergott & Jia-Ping Huang & Ines (I.) Lindner, 2018. "Naive Learning in Social Networks with Random Communication," Tinbergen Institute Discussion Papers 18-018/II, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20180018
    as

    Download full text from publisher

    File URL: https://papers.tinbergen.nl/18018.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    2. 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.
    3. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331, September.
    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.
    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. Sanjeev Goyal, 2015. "Networks in Economics: A Perspective on the Literature," Cambridge Working Papers in Economics 1548, Faculty of Economics, University of Cambridge.
    2. Vivi Alatas & Abhijit Banerjee & Arun G. Chandrasekhar & Rema Hanna & Benjamin A. Olken, 2016. "Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia," American Economic Review, American Economic Association, vol. 106(7), pages 1663-1704, July.
    3. Arifovic, Jasmina & Eaton, B. Curtis & Walker, Graeme, 2015. "The coevolution of beliefs and networks," Journal of Economic Behavior & Organization, Elsevier, vol. 120(C), pages 46-63.
    4. Pongou, Roland & Serrano, Roberto, 2013. "Dynamic Network Formation in Two-Sided Economies," MPRA Paper 46021, University Library of Munich, Germany.
    5. Ming Hu & Joseph Milner & Jiahua Wu, 2016. "Liking and Following and the Newsvendor: Operations and Marketing Policies Under Social Influence," Management Science, INFORMS, vol. 62(3), pages 867-879, March.
    6. Dasaratha, Krishna & He, Kevin, 2020. "Network structure and naive sequential learning," Theoretical Economics, Econometric Society, vol. 15(2), May.
    7. Thomas Görzen & Dennis Kundisch, 2019. "When in Doubt Follow the Crowd: How Idea Quality Moderates the Effect of an Anchor on Idea Evaluation," Working Papers Dissertations 57, Paderborn University, Faculty of Business Administration and Economics.
    8. Krishna Dasaratha & Kevin He, 2019. "Aggregative Efficiency of Bayesian Learning in Networks," Papers 1911.10116, arXiv.org, revised Sep 2024.
    9. Ilan Lobel & Evan Sadler, 2016. "Preferences, Homophily, and Social Learning," Operations Research, INFORMS, vol. 64(3), pages 564-584, June.
    10. Bary Pradelski, 2019. "Control by social influence: durables vs. non-durables," Post-Print hal-03100218, HAL.
    11. Arieli, Itai & Babichenko, Yakov & Peretz, Ron & Young, H. Peyton, 2020. "The speed of innovation diffusion in social networks," LSE Research Online Documents on Economics 102538, London School of Economics and Political Science, LSE Library.
    12. Pantelis P. Analytis & Francesco Cerigioni & Alexandros Gelastopoulos & Hrvoje Stojic, 2022. "Sequential Choice and Self-Reinforcing Rankings," Working Papers 1318, Barcelona School of Economics.
    13. H Peyton Young & Itai Arieli & Yakov Babichenko & Ron Peretz, 2019. "The Speed of Innovation Diffusion in Social Networks," Economics Series Working Papers 884, University of Oxford, Department of Economics.
    14. Gallo, E. & Langtry, A., 2020. "Social Networks, Confirmation Bias and Shock Elections," Cambridge Working Papers in Economics 2099, Faculty of Economics, University of Cambridge.
    15. Ariana Paola Cortés Ángel & Mustafa Hakan Eratalay, 2022. "Deep diving into the S&P Europe 350 index network and its reaction to COVID-19," Journal of Computational Social Science, Springer, vol. 5(2), pages 1343-1408, November.
    16. Comola, Margherita & Rusinowska, Agnieszka & Villeval, Marie Claire, 2024. "Competing for Influence in Networks through Strategic Targeting," IZA Discussion Papers 17315, Institute of Labor Economics (IZA).
    17. Eger, Steffen, 2016. "Opinion dynamics and wisdom under out-group discrimination," Mathematical Social Sciences, Elsevier, vol. 80(C), pages 97-107.
    18. Hellmann, Tim & Staudigl, Mathias, 2014. "Evolution of social networks," European Journal of Operational Research, Elsevier, vol. 234(3), pages 583-596.
    19. Polanski, Arnold & Vega-Redondo, Fernando, 2023. "Homophily and influence," Journal of Economic Theory, Elsevier, vol. 207(C).
    20. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Misinterpreting Others and the Fragility of Social Learning," Econometrica, Econometric Society, vol. 88(6), pages 2281-2328, November.

    More about this item

    Keywords

    Wisdom of crowds; social networks; information cascades; naive learning;
    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
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:tin:wpaper:20180018. 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: Tinbergen Office +31 (0)10-4088900 (email available below). General contact details of provider: https://edirc.repec.org/data/tinbenl.html .

    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.