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Learning How to Cooperate: Optimal Play in Repeated Coordination Games
Abstract
This paper proposes a characterization of optimal strategies for playing certain repeated coordination games whose players have identical preferences. Players' optimal coordination strategies reflect their uncertainty about how their partners will respond to multiple-equilibrium problems; this uncertainty constrains the statistical relationships between their strategy choices players can bring about. The authors show that optimality is nevertheless consistent with subgame-perfect equilibrium. Examples are analyzed in which players use precedents as focal points to achieve and maintain coordination, and in which they play dominated strategies with positive probability in early stages in the hope of generating a useful precedent. Copyright 1990 by The Econometric Society.
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Citations
Cited by
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The Global Diffusion of Public Policies: Social Construction, Coercion, Competition, or Learning?
Dobbin, F., Simmons, B., & Garrett, G. (2007) · Annual Review of Sociology · doi:10.1146/annurev.soc.33.090106.142507 · Cited by 1,379
2. Public Goods: A Survey of Experimental Research
Ledyard, J. O. (1995) · The Handbook of Experimental Economics · doi:10.1515/9780691213255-004 · Cited by 1,033
Patents and Cumulative Innovation: Causal Evidence from the Courts *
Galasso, A., & Schankerman, M. (2015) · The Quarterly Journal of Economics · doi:10.1093/qje/qju029 · Cited by 448
Homo Moralis--Preference Evolution Under Incomplete Information and Assortative Matching
(2013) · Econometrica · doi:10.3982/ecta10637 · Cited by 371
Metadata
- Authors
- Crawford, V. P., & Haller, H.
- Venue
- Econometrica
- Publisher
- JSTOR
- Published
- 1990-05
- Volume
- 58
- Issue
- 3
- Pages
- 571
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W2064882565
- Cited by
- 377
- References
- 0
- FWCI
- 12.23
- Type
- journal article
- Language
- en
- Open access
- closed
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- Crawford, V. P., & Haller, H. (1990). Learning How to Cooperate: Optimal Play in Repeated Coordination Games. Econometrica. https://doi.org/10.2307/2938191
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.