Asymptotic learning on Bayesian social networks

Abstract

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 G_n=(V_n,E_n) with |V_n|→∞ if with probability tending to 1 as n→∞ all agents in G_n 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.

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