Asymptotic Learning on Bayesian Social Networks
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.
Cite
@article{arxiv.1207.5893,
title = {Asymptotic Learning on Bayesian Social Networks},
author = {Elchanan Mossel and Allan Sly and Omer Tamuz},
journal= {arXiv preprint arXiv:1207.5893},
year = {2014}
}
Comments
28 pages, 2 figures. arXiv admin note: substantial text overlap with arXiv:1105.4765