Naïve learning in social networks with random communication

Research output: Contribution to JournalArticleAcademicpeer-review

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. 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.

LanguageEnglish
Pages1-11
Number of pages11
JournalSocial Networks
Volume58
DOIs
Publication statusPublished - 1 Jul 2019

Fingerprint

Social Support
social network
Communication
Learning
communication
Interpersonal Relations
learning
Consensus
social learning
Weights and Measures
wisdom
privilege
interaction
Social Learning
Society
time

Keywords

  • Naïve learning
  • Social networks
  • Wisdom of crowds

Cite this

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title = "Na{\"i}ve learning in social networks with random communication",
abstract = "We study social learning in a social network setting where agents receive independent noisy signals about the truth. Agents na{\"i}vely 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.",
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Naïve learning in social networks with random communication. / Huang, Jia Ping; Heidergott, Bernd; Lindner, Ines.

In: Social Networks, Vol. 58, 01.07.2019, p. 1-11.

Research output: Contribution to JournalArticleAcademicpeer-review

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