Learning in Social Networks

41 Pages Posted: 18 Feb 2017 Last revised: 10 Apr 2017

See all articles by Benjamin Golub

Benjamin Golub

Harvard University

Evan Sadler

Columbia University, Graduate School of Arts and Sciences, Department of Economics

Date Written: February 16, 2017

Abstract

This survey covers models of how agents update behaviors and beliefs using information conveyed through social connections. We begin with sequential social learning models, in which each agent makes a decision once and for all after observing a subset of prior decisions; the discussion is organized around the concepts of diffusion and aggregation of information. Next, we present the DeGroot framework of average-based repeated updating, whose long- and medium-run dynamics can be completely characterized in terms of measures of network centrality and segregation. Finally, we turn to various models of repeated updating that feature richer optimizing behavior, and conclude by urging the development of network learning theories that can deal adequately with the observed phenomenon of persistent disagreement.

Keywords: social learning, informational herding, cascades, DeGroot, Bayesian, consensus, imitation, homophily, speed of convergence

Suggested Citation

Golub, Benjamin and Sadler, Evan, Learning in Social Networks (February 16, 2017). Available at SSRN: https://ssrn.com/abstract=2919146 or http://dx.doi.org/10.2139/ssrn.2919146

Benjamin Golub (Contact Author)

Harvard University ( email )

Littauer Center, Dept of Economics
1805 Cambridge Street
Cambridge, MA 02138
United States

Evan Sadler

Columbia University, Graduate School of Arts and Sciences, Department of Economics ( email )

420 W. 118th Street
New York, NY 10027
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
1,016
Abstract Views
3,240
rank
21,158
PlumX Metrics