Asymptotic Learning on Bayesian Social Networks

Elchanan Mossel; Allan Sly; Omer Tamuz

doi:10.1007/s00440-013-0479-y

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arXiv · 1207.5893

Asymptotic Learning on Bayesian Social Networks

Elchanan Mossel·Allan Sly·Omer Tamuz

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Abstract

Understanding information exchange and aggregation on networks is a central problem in theoretical economics, probability and statistics. 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| tending to infinity, if with probability tending to 1 as n tends to infinity 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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BibTeX RIS

Elchanan Mossel, Allan Sly, Omer Tamuz. 2012-07-25. Asymptotic Learning on Bayesian Social Networks. https://doi.org/10.1007/s00440-013-0479-y

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