Social Learning Equilibria

49 Pages Posted: 23 Feb 2018 Last revised: 29 Sep 2019

See all articles by Elchanan Mossel

Elchanan Mossel

Massachusetts Institute of Technology (MIT)

Manuel Mueller-Frank

University of Navarra, IESE Business School

Allan Sly

Princeton University

Omer Tamuz

California Institute of Technology (Caltech) - Division of the Humanities and Social Sciences

Date Written: September 27, 2019

Abstract

We consider a large class of social learning models in which a group of agents face uncertainty regarding a state of the world, share the same utility function, observe private signals, and interact in a general dynamic setting. We introduce Social Learning Equilibria, a static equilibrium concept that abstracts away from the details of the given extensive form, but nevertheless captures the corresponding asymptotic equilibrium behavior. We establish general conditions for agreement, herding, and information aggregation in equilibrium, highlighting a connection between agreement and information aggregation.

Keywords: Social learning, agreement, herding

Suggested Citation

Mossel, Elchanan and Mueller-Frank, Manuel and Sly, Allan and Tamuz, Omer, Social Learning Equilibria (September 27, 2019). Available at SSRN: https://ssrn.com/abstract=3124385 or http://dx.doi.org/10.2139/ssrn.3124385

Elchanan Mossel

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Manuel Mueller-Frank

University of Navarra, IESE Business School ( email )

Avenida Pearson 21
Barcelona, 08034
Spain

Allan Sly

Princeton University ( email )

22 Chambers Street
Princeton, NJ 08544-0708
United States

Omer Tamuz (Contact Author)

California Institute of Technology (Caltech) - Division of the Humanities and Social Sciences ( email )

1200 East California Blvd.
Pasadena, CA 91125
United States

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

Paper statistics

Downloads
232
Abstract Views
1,313
Rank
255,013
PlumX Metrics