Learning to Coordinate in Social Networks

34 Pages Posted: 11 Jul 2013 Last revised: 23 Jun 2016

See all articles by Pooya Molavi

Pooya Molavi

Northwestern University

Ceyhun Eksin

University of Pennsylvania - School of Engineering & Applied Science

Alejandro Ribeiro

University of Pennsylvania - School of Engineering & Applied Science

Ali Jadbabaie

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

Date Written: November 5, 2014

Abstract

We study a dynamic game in which short-run players repeatedly play a symmetric, strictly supermodular game whose payoff depends on a fixed unknown state of nature. Each short-run player inherits the beliefs of his immediate predecessor in addition to observing the actions of the players in his social neighborhood in the previous stage. Due to the strategic complementary between their actions, players have the incentive to coordinate with, and learn from others. We show that in any Markov Perfect Bayesian Equilibrium of the game, players eventually reach consensus in their actions. They also asymptotically receive similar payoffs in spite of initial differences in their access to information. We further show that, if the players' payoffs can be represented by a quadratic function, then the private observations are optimally aggregated in the limit for generic specifications of the game. Therefore, players asymptotically coordinate on choosing the best action given the aggregate information available throughout the network. We provide extensions of our results to the case of changing networks and endogenous private signals.

Keywords: Consensus, information aggregation, supermodular games, social networks

JEL Classification: C73, D83, D85

Suggested Citation

Molavi, Pooya and Eksin, Ceyhun and Ribeiro, Alejandro and Jadbabaie, Ali, Learning to Coordinate in Social Networks (November 5, 2014). Available at SSRN: https://ssrn.com/abstract=2292124 or http://dx.doi.org/10.2139/ssrn.2292124

Pooya Molavi (Contact Author)

Northwestern University ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

Ceyhun Eksin

University of Pennsylvania - School of Engineering & Applied Science ( email )

220 S 33rd St
Philadelphia, PA 19104
United States

Alejandro Ribeiro

University of Pennsylvania - School of Engineering & Applied Science ( email )

220 S 33rd St
Philadelphia, PA 19104
United States

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

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