Published November 2020 | Version Submitted
Journal Article Open

Repeated Coordination with Private Learning

  • 1. ROR icon California Institute of Technology
  • 2. ROR icon Pennsylvania State University
  • 3. ROR icon Instituto Tecnológico Autónomo de México

Abstract

We study a repeated game with payoff externalities and observable actions where two players receive information over time about an underlying payoff-relevant state, and strategically coordinate their actions. Players learn about the true state from private signals, as well as the actions of others. They commonly learn the true state (Cripps et al., 2008), but do not coordinate in every equilibrium. We show that there exist stable equilibria in which players can overcome unfavorable signal realizations and eventually coordinate on the correct action, for any discount factor. For high discount factors, we show that in addition players can also achieve efficient payoffs.

Additional Information

© 2020 Elsevier Inc. Received 23 March 2019, Revised 1 August 2020, Accepted 19 August 2020, Available online 28 August 2020. We thank Nageeb Ali, Sylvain Chassang, Drew Fudenberg, George Mailath, Manuel Mueller-Frank, Roger Myerson, Philipp Strack, Adam Wierman, and Yuichi Yamamoto for comments. We are grateful to the editor, Xavier Vives, and two anonymous referees for valuable suggestions. This work was supported by a grant from the Simons Foundation (#419427, Omer Tamuz).

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Additional details

Identifiers

Eprint ID
96805
DOI
10.1016/j.jet.2020.105106
Resolver ID
CaltechAUTHORS:20190628-075628753

Funding

Simons Foundation
419427

Dates

Created
2019-06-28
Created from EPrint's datestamp field
Updated
2021-11-16
Created from EPrint's last_modified field

Caltech Custom Metadata

Caltech groups
Mathematics Department