Statistics Papers

Document Type

Journal Article

Date of this Version

2-2014

Publication Source

Probability Theory and Related Fields

Volume

158

Issue

1

Start Page

127

Last Page

157

DOI

10.1007/s00440-013-0479-y

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 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.

Copyright/Permission Statement

The final publication is available at Springer via http://dx.doi.org/10.1007/s00440-013-0479-y.

Keywords

Bayesian learning, social networks, aggregation of information, rational expectatons

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Date Posted: 27 November 2017

This document has been peer reviewed.