Non-Bayesian social learning

Ali Jadbabaie, Pooya Molavi, Alvaro Sandroni*, Alireza Tahbaz-Salehi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

192 Scopus citations

Abstract

We develop a dynamic model of opinion formation in social networks when the information required for learning a parameter may not be at the disposal of any single agent. Individuals engage in communication with their neighbors in order to learn from their experiences. However, instead of incorporating the views of their neighbors in a fully Bayesian manner, agents use a simple updating rule which linearly combines their personal experience and the views of their neighbors. We show that, as long as individuals take their personal signals into account in a Bayesian way, repeated interactions lead them to successfully aggregate information and learn the true parameter. This result holds in spite of the apparent naïveté of agents' updating rule, the agents' need for information from sources the existence of which they may not be aware of, worst prior views, and the assumption that no agent can tell whether her own views or those of her neighbors are more accurate.

Original languageEnglish (US)
Pages (from-to)210-225
Number of pages16
JournalGames and Economic Behavior
Volume76
Issue number1
DOIs
StatePublished - Sep 1 2012

Keywords

  • Information aggregation
  • Learning
  • Social networks

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics

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