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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
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    References listed on IDEAS

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    3. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331, January.
    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)

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    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

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