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Strategic Interaction and Interstate Crises: A Bayesian Quantal Response Estimator for Incomplete Information Games

Published online by Cambridge University Press:  24 February 2008

Justin Esarey ,
Bumba Mukherjee  and
Will H. Moore
Justin Esarey*
Affiliation:
Department of Political Science, Florida State University, 556 Bellamy Building, Tallahassee, FL 32306
Bumba Mukherjee
Affiliation:
Department of Political Science and Department of Economics and Econometrics, 407 Decio Hall, University of Notre Dame, South Bend, IN 46556, e-mail: [email protected]
Will H. Moore
Affiliation:
Department of Political Science, Florida State University, 561 Bellamy Building, Tallahassee, FL 32306, e-mail: [email protected]
*
e-mail: [email protected] (corresponding author)
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Abstract

Private information characteristics like resolve and audience costs are powerful influences over strategic international behavior, especially crisis bargaining. As a consequence, states face asymmetric information when interacting with one another and will presumably try to learn about each others' private characteristics by observing each others' behavior. A satisfying statistical treatment would account for the existence of asymmetric information and model the learning process. This study develops a formal and statistical framework for incomplete information games that we term the Bayesian Quantal Response Equilibrium Model (BQRE model). Our BQRE model offers three advantages over existing work: it directly incorporates asymmetric information into the statistical model's structure, estimates the influence of private information characteristics on behavior, and mimics the temporal learning process that we believe takes place in international politics.

Type
Research Article
Information
Political Analysis , Volume 16 , Issue 3 , Summer 2008 , pp. 250 - 273
Copyright
Copyright © The Author 2008. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Author's note: For their assistance with the Phoenix cluster at Florida State University (FSU) we wish to thank Jim Wilgenbusch and Jeff MacDonald of the Computational and Information Science Technology Department. The Department of Political Science at FSU graciously provided us with access to Quantify, its shared parallel computing resource. We also wish to thank Ken Schultz, David Siegel, Curt Signorino, Jeff Staton, our anonymous reviewers, and the participants and attendees of our 2007 APSA conference panel and 2007 Political Methodology conference poster session for their helpful comments and suggestions.

References

Aldrich, John, and Alt, James. 2003. Introduction to the special issue. Political Analysis 11: 309–15.CrossRefGoogle Scholar
Bas, Muhammet, Signorino, Curtis, and Walker, Robert. 2007. Statistical backwards induction: A simple method for estimating strategic models. Political Analysis doi:10.1093/pan/mpm029.Google Scholar
Carrubba, Clifford J., Yuen, Amy, and Zorn, Christopher. 2007. In defense of comparative statics: Specifying empirical tests of models of strategic interaction. Political Analysis 15: 465–82.Google Scholar
Fearon, James. 1994a. Domestic political audiences and the escalation of international disputes. American Political Science Review 88: 577–92.CrossRefGoogle Scholar
Fearon, James. 1994b. Signaling versus the balance of power and interests: An empirical test of a crisis bargaining model. Journal of Conflict Resolution 38: 236–70.Google Scholar
Fearon, James D. 1995. Rationalist explanations for war. International Organization 49: 379–414.Google Scholar
Fey, Mark, and Ramsay, Kristopher. 2006. The common priors assumption: A comment on bargaining and the nature of war. Journal of Conflict Resolution 50: 607–13.Google Scholar
Gelpi, Christopher F., and Griesdorf, Michael. 2001. Winners or losers? Democracies in international crisis, 1918-94. American Political Science Review 95: 633–47.Google Scholar
Granato, Jim, and Scioli, Frank. 2004. Puzzles, proverbs and omega matrices: The Scientific and Social Significance and Empirical Implications of Theoretical Models (EITM). Perspectives on Politics 2: 313–23.Google Scholar
Greene, William. 2003. Econometric Analysis. 5th ed. New York: Prentice Hall.Google Scholar
Hsiao, Cheng. 2003. Analysis of panel data. 2nd ed. New York: Cambridge University Press.Google Scholar
Keohane, Robert O. 1984. After hegemony. Princeton, NJ: Princeton University Press.Google Scholar
Lewis, Jeffrey B., and Schultz, Kenneth A. 2003. Revealing preferences: Empirical estimation of a crisis bargaining game with incomplete information. Political Analysis 11: 345–67.Google Scholar
Martin, Andrew, and Quinn, Kevin. 2006. MCMCpack. http://mcmcpack.wustl.edu/wiki/index.php/Main_Page.Google Scholar
McKelvey, Richard, and Palfrey, Thomas. 1995. Quantal response equilibria in normal form games. Games and Economic Behavior 10: 6–38.Google Scholar
McKelvey, Richard, and Palfrey, Thomas. 1998. Quantal response equilibria in extensive form games. Experimental Economics 1(1): 9–41.Google Scholar
Morrow, James D. 1989. Capabilities, uncertainty, and resolve: A limited information model of crisis bargaining. American Journal of Political Science 33: 941–72.Google Scholar
Morrow, James D. 1999. The strategic setting of choices: Signaling, commitment, and negotiations in international politics. In Strategic choice and international relations, ed. Lake, D. A. and Powell, R. 77–114. Princeton, NJ: Princeton University Press.Google Scholar
Palfrey, Thomas R. 2007. McKelvey and quantal response equilibrium. In Positive changes in political science: The legacy of richard d. mckelvey's most influential writings, ed. Alt, James, Aldrich, John, and Lupia, Arthur. Ann Arbor, MI: University of Michigan Press.Google Scholar
Partell, Peter J., and Palmer, Glenn. 1999. Audience costs and interstate crises: An empirical assessment of Fearon's model of dispute outcomes. International Studies Quarterly 43: 389–405.Google Scholar
Powell, Robert R. 2004. Bargaining and learning while fighting. American Journal of Political Science 48: 344–61.Google Scholar
Reed, William. 2003. Information, power and war. American Political Science Review 97: 633–41.Google Scholar
Reiter, Dan, and Stam, Allan C. III. 1998. Democracy, war initiation, and victory. American Political Science Review 92: 377–90.CrossRefGoogle Scholar
Reiter, Dan, and Stam, Allan C. III. 2002. Democracies at war, Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Rossini, Anthony, Tierney, Luke, and Li, Na. 2003. Simple parallel statistical computing in R. UW Biostatistics Working Paper Series. Working paper 193. http://www.bepress.com/uwbiostat/paper193.Google Scholar
Russett, Bruce M., and Oneal, John R. 1999. The classic liberals were right: Democracy, interdependence, and conflict, 1950-1985. International Studies Quarterly 41: 267–94.Google Scholar
Russett, Bruce M., and Oneal, John R. 2001. Triangulating peace: Democracy, interdependence, and international organizations. New York: Norton.Google Scholar
Sartori, Anne E., and Meirowitz, Adam. 2006. Secrecy and war: The origins of private information. Unpublished manuscript, Princeton University.Google Scholar
Schelling, Thomas C. 1966. Arms and influence. New Haven, CT: Yale University Press.Google Scholar
Schultz, Kenneth. 1999. Do democratic institutions constrain or inform? Contrasting two institutional perspectives on democracy and war. International Organization 53: 233–66.Google Scholar
Sekhon, Jasjeet S., and Mebane, Walter R. Jr. 1998. Genetic optimization using derivatives. Political Analysis 7: 187–210.Google Scholar
Sevcikova, Hana, and Rossini, Tony. 2004. Package rlecuyer. http://cran.r-project.org/src/contrib/Descriptions/rlecuyer.html (accessed September 14, 2006).Google Scholar
Signorino, Curtis S. 1999. Strategic interaction and the statistical analysis of international conflict. American Political Science Review 99: 279–97.Google Scholar
Signorino, Curtis S. 2003. Structure and uncertainty in discrete choice models. Political Analysis 11: 316–44.Google Scholar
Signorino, Curtis S. 2007. On formal theory and statistical methods: A response to Carrubba, Yuen, and Zorn. Political Analysis 15: 483–501.Google Scholar
Signorino, Curtis S., and Yilmaz, Kuzey. 2003. Strategic misspecification in regression models. American Journal of Political Science 47: 551–66.Google Scholar
Slantchev, Branislav. 2003. The power to hurt: Costly conflict with completely informed states. American Political Science Review 47: 123–35.Google Scholar
Smith, Alastair. 1999. Testing theories of strategic choice: The example of crisis escalation. American Journal of Political Science 43: 1254–83.CrossRefGoogle Scholar
Smith, Alastair, and Stam, Allan. 2004. Bargaining and the nature of war. Journal of Conflict Resolution 48: 783–813.Google Scholar
Snyder, Glenn, and Deising, Paul. 1977. Conflict among nations. Princeton, NJ: Princeton University Press.Google Scholar
Wand, Jonathan. 2006. Comparing models of strategic choice: The role of uncertainty and signaling. Political Analysis 14: 101–20.Google Scholar