Date of this Version
Probability Theory and Related Fields
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 Gn = (Vn, En) with |Vn| → ∞ 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.
The final publication is available at Springer via http://dx.doi.org/10.1007/s00440-013-0479-y.
Bayesian learning, social networks, aggregation of information, rational expectatons
Mossel, E., Sly, A., & Tamuz, O. (2014). Asymptotic Learning on Bayesian Social Networks. Probability Theory and Related Fields, 158 (1), 127-157. http://dx.doi.org/10.1007/s00440-013-0479-y
Date Posted: 27 November 2017
This document has been peer reviewed.