Information Heterogeneity and the Speed of Learning in Social Networks

38 Pages Posted: 19 May 2013 Last revised: 13 Jul 2013

See all articles by Ali Jadbabaie

Ali Jadbabaie

Institute for Data, Systems, and Society, Massachusetts Institute of Technology

Pooya Molavi

Northwestern University

Alireza Tahbaz-Salehi

Northwestern University - Kellogg School of Management; Centre for Economic Policy Research (CEPR)

Date Written: May 18, 2013

Abstract

This paper examines how the structure of a social network and the quality of information available to different agents determine the speed of social learning. To this end, we study a variant of the seminal model of DeGroot (1974), according to which agents linearly combine their personal experiences with the views of their neighbors. We show that the rate of learning has a simple analytical characterization in terms of the relative entropy of agents’ signal structures and their eigenvector centralities. Our characterization establishes that the way information is dispersed throughout the social network has non-trivial implications for the rate of learning. In particular, we show that when the informativeness of different agents’ signal structures are comparable in the sense of Blackwell (1953), then a positive assortative matching of signal qualities and eigenvector centralities maximizes the rate of learning. On the other hand, if information structures are such that each individual possesses some information crucial for learning, then the rate of learning is higher when agents with the best signals are located at the periphery of the network. Finally, we show that the extent of asymmetry in the structure of the social network plays a key role in the long-run dynamics of the beliefs.

Keywords: Social learning, relative entropy, eigenvector centrality

JEL Classification: D83, D85, Z13

Suggested Citation

Jadbabaie, Ali and Molavi, Pooya and Tahbaz-Salehi, Alireza, Information Heterogeneity and the Speed of Learning in Social Networks (May 18, 2013). Columbia Business School Research Paper No. 13-28, Available at SSRN: https://ssrn.com/abstract=2266979 or http://dx.doi.org/10.2139/ssrn.2266979

Ali Jadbabaie

Institute for Data, Systems, and Society, Massachusetts Institute of Technology ( email )

77 Massachusetts Ave E18-309C
E18-309C
02139, MA MA 02139
United States
6172537339 (Phone)
6172537339 (Fax)

HOME PAGE: http://web.mit.edu/www/jadbabai

Pooya Molavi

Northwestern University ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

Alireza Tahbaz-Salehi (Contact Author)

Northwestern University - Kellogg School of Management ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
282
Abstract Views
1,359
rank
128,983
PlumX Metrics