Asymptotic learning on Bayesian social networks

Elchanan Mossel, Allan Sly, Omer Tamuz

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


We study a standard model of economic agents on the nodes of a social network graph who learn a binary "state of the world" S, from initial signals, by repeatedly observing each other's best guesses. Asymptotic learning is said to occur on a family of graphs Gn = (Vn,En) with {pipe}Vn{pipe} → ∞ if with probability tending to 1 as n → ∞ all agents in Gn eventually estimate S correctly. We identify sufficient conditions for asymptotic learning and contruct examples where learning does not occur when the conditions do not hold.

Original languageEnglish (US)
Pages (from-to)127-157
Number of pages31
JournalProbability Theory and Related Fields
Issue number1-2
StatePublished - Feb 2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Analysis
  • Statistics and Probability
  • Statistics, Probability and Uncertainty


  • Aggregation of information
  • Bayesian learning
  • Rational expectations
  • Social networks


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