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Comparing Models of Strategic Choice: The Role of Uncertainty and Signaling

Published online by Cambridge University Press:  04 January 2017

Jonathan Wand
Jonathan Wand*
Affiliation:
Department of Political Science, Encina Hall West, Stanford University, Stanford, CA 94305. e-mail: [email protected]
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Abstract

Testing the fit of competing equilibrium solutions to extensive form games crucially depends on assumptions about the distribution of player types. To illustrate the importance of these assumptions for differentiating standard statistical models of strategic choice, I draw on a game previously analyzed by Lewis and Schultz (2003). The differences that they highlight between a pair of perfect Bayesian equilibrium and quantal response equilibrium models are not produced by signaling and updating dynamics as claimed, but are instead produced by different assumptions about the distribution of player types. The method of analysis developed and the issues raised are applicable to a broad range of structural models of conflict and bargaining.

Type
Research Article
Information
Political Analysis , Volume 14 , Issue 1 , Winter 2006 , pp. 101 - 120
Copyright
Copyright © The Author 2005. Published by Oxford University Press on behalf of the Society for Political Methodology 

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References

Aragones, Enriqueta, and Palfrey, Thomas R. 2004. “The Effect of Candidate Quality on Electoral Equilibrium: An Experimental Study.” American Political Science Review 98: 77–90.Google Scholar
Carson, Jamie L. 2003. “Strategic Interaction and Candidate Competition in U.S. House Elections: Empirical Applications of Probit and Strategic Probit Models.” Political Analysis 11: 368–80.Google Scholar
Carson, Jamie L. 2005. “Strategy, Selection, and Candidate Competition in U.S. House and Senate Elections.” Journal of Politics 67: 1–28.Google Scholar
Leblang, David. 2005. “To Devalue or to Defend? The Political Economy of Exchange Rate Policy.” International Studies Quarterly 47: 533–559.CrossRefGoogle Scholar
Lewis, Jeffrey, and Schultz, Kenneth. 2003. “Revealing Preferences: Empirical Estimation of a Crisis Bargaining Game with Incomplete Information.” Political Analysis 11: 345–67.Google Scholar
McKelvey, Richard, and Palfrey, Thomas. 1998. “Quantal Response Equilibria for Extensive Form Games.” Experimental Economics 1: 9–41.Google Scholar
Quinn, Kevin, and Westveld, Anton. 2004. “Bayesian Inference for Semi-parametric Quantal Response Equilibrium Models.” Paper presented at the 2004 Midwest Political Association meetings, Chicago, IL.Google Scholar
Signorino, Curtis S. 1999. “Strategic Interaction and the Statistical Analysis of International Conflict.” American Political Science Review 93: 279–297.Google Scholar
Signorino, Curtis S. 2003. “Structure and Uncertainty in Discrete Choice Models.” Political Analysis 11: 316–344.Google Scholar
Signorino, Curtis S., and Yilmaz, Kuzey. 2003. “Strategic Misspecification in Discrete Choice Models.” American Journal of Political Science 47: 551–66.Google Scholar
Tong, Y. L. 1990. The Multivariate Normal Distribution. New York: Springer-Verlag.Google Scholar