Abstract

http://ssrn.com/abstract=2292124
 
 

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Learning to Coordinate in Social Networks


Pooya Molavi


Department of Economics, Massachusetts Institute of Technology

Ceyhun Eksin


University of Pennsylvania - School of Engineering & Applied Science

Alejandro Ribeiro


University of Pennsylvania - School of Engineering & Applied Science

Ali Jadbabaie


University of Pennsylvania - Department of Electrical and Systems Engineering

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.

Number of Pages in PDF File: 34

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

JEL Classification: C73, D83, D85

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Date posted: July 11, 2013 ; Last revised: November 6, 2014

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: http://ssrn.com/abstract=2292124 or http://dx.doi.org/10.2139/ssrn.2292124

Contact Information

Pooya Molavi (Contact Author)
Department of Economics, Massachusetts Institute of Technology ( email )
77 Massachusetts Avenue E19-750
Cambridge, MA 02139
United States
215-834-7153 (Phone)
Ceyhun Eksin
University of Pennsylvania - School of Engineering & Applied Science ( email )
Philadelphia, PA
United States
Alejandro Ribeiro
University of Pennsylvania - School of Engineering & Applied Science ( email )
Philadelphia, PA
United States
Ali Jadbabaie
University of Pennsylvania - Department of Electrical and Systems Engineering ( email )
200 South 33rd Street
Moore Bldg Room 203
Philadelphia, PA 19104
United States
215 898-8105 (Phone)
215 573-2068 (Fax)
HOME PAGE: http://www.seas.upenn.edu/~jadbabai
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