Naïve learning in social networks with random communication

Jia Ping Huang*, Bernd Heidergott, Ines Lindner

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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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. Our findings, based on Theorem 1, Corollary 1 and simulated examples, suggest the following. 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 seems an illusive concept and bares the danger of mistaking consensus for truth.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalSocial Networks
Early online date7 Feb 2019
Publication statusPublished - Jul 2019


  • Naïve learning
  • Social networks
  • Wisdom of crowds


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