Bayesian learning in social networks

Douglas Gale, Shachar Kariv

    Research output: Contribution to journalArticlepeer-review


    We extend the standard model of social learning in two ways. First, we introduce a social network and assume that agents can only observe the actions of agents to whom they are connected by this network. Secondly, we allow agents to choose a different action at each date. If the network satisfies a connectedness assumption, the initial diversity resulting from diverse private information is eventually replaced by uniformity of actions, though not necessarily of beliefs, in finite time with probability one. We look at particular networks to illustrate the impact of network architecture on speed of convergence and the optimality of absorbing states. Convergence is remarkably rapid, so that asymptotic results are a good approximation even in the medium run.

    Original languageEnglish (US)
    Pages (from-to)329-346
    Number of pages18
    JournalGames and Economic Behavior
    Issue number2
    StatePublished - Nov 2003


    • Herd behavior
    • Informational cascades
    • Networks
    • Social learning

    ASJC Scopus subject areas

    • Finance
    • Economics and Econometrics


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