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Scoring from Contests

Published online by Cambridge University Press:  04 January 2017

Keith E. Schnakenberg  and
Elizabeth Maggie Penn
Keith E. Schnakenberg*
Affiliation:
Department of Political Science, Washington University in St. Louis, 1 Brookings Drive, Campus Box 1063, St. Louis, MO 63105
Elizabeth Maggie Penn
Affiliation:
Department of Political Science, Washington University in St. Louis e-mail: penn@wustl.edu
*
e-mail: keschnak@wustl.edu (corresponding author)
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Abstract

This article presents a new model for scoring alternatives from “contest” outcomes. The model is a generalization of the method of paired comparison to accommodate comparisons between arbitrarily sized sets of alternatives in which outcomes are any division of a fixed prize. Our approach is also applicable to contests between varying quantities of alternatives. We prove that under a reasonable condition on the comparability of alternatives, there exists a unique collection of scores that produces accurate estimates of the overall performance of each alternative and satisfies a well-known axiom regarding choice probabilities. We apply the method to several problems in which varying choice sets and continuous outcomes may create problems for standard scoring methods. These problems include measuring centrality in network data and the scoring of political candidates via a “feeling thermometer.” In the latter case, we also use the method to uncover and solve a potential difficulty with common methods of rescaling thermometer data to account for issues of interpersonal comparability.

Type
Research Article
Information
Political Analysis , Volume 22 , Issue 1 , Winter 2014 , pp. 86 - 114
Copyright
Copyright © The Author 2013. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: We are grateful for the helpful comments of David Darmofal, Mark Fey, Holger Kern, John Patty, and especially Stephane Wolton, along with seminar participants at Washington University, the Harris School of Public Policy, the University of South Carolina, the Stanford Graduate School of Business, the University of Maryland, and the University of Pittsburgh. This research was supported by NIH Grant (#1RC4LM010958-01), and we are particularly indebted to William Shannon, Elena Deych, Skye Buckner-Petty, and Berkley Shands at the Washington University School of Medicine for their support. Replication materials for this article are available from the Political Analysis Dataverse at http://hdl.handle.net/1902.1/21998.

References

Aitchison, John. 1986. The statistical analysis of compositional data. Monographs on Statistics and Applied Probability. London: Chapman & Hall.Google Scholar
Aitchison, J., and Egozcue, J. J. 2005. Compositional data analysis: Where are we and where should we be heading? Mathematical Geology 37(7): 829–50.CrossRefGoogle Scholar
Alvarez, R. Michael, and Nagler, Jonathan. 1995. Economics, issues and the Perot candidacy: Voter choice in the 1992 presidential election. American Journal of Political Science 39(3): 714–44.CrossRefGoogle Scholar
Alvarez, R. Michael, and Nagler, Jonathan. 1998. Economics, entitlements, and social issues: Voter choice in the 1996 presidential election. American Journal of Political Science 42(4): 1349–63.CrossRefGoogle Scholar
Alvarez, R. Michael, and Nagler, Jonathan. 2000. A new approach for modelling strategic voting in multiparty elections. British Journal of Political Science 30: 57–75.CrossRefGoogle Scholar
Bechtel, Michael, and Scheve, Kenneth. 2012. Public support for global climate cooperation. Working paper.Google Scholar
Bradley, Ralph Allan, and Terry, Milton E. 1952. Rank analysis of incomplete block designs: I. The method of paired comparisons. Biometrika 39 (3/4): 324–45.Google Scholar
Clark, Derek J., and Riis, Christian. 1998. Contest success functions: An extension. Economic Theory 11(1): 201–4.CrossRefGoogle Scholar
Davidson, Roger R. 1970. On extending the Bradley-Terry model to accommodate ties in paired comparison experiments. Journal of the American Statistical Association 65(329): 317–28.CrossRefGoogle Scholar
Debreu, Gerard. 1960. Review of RD Luce, individual choice behavior: A theoretical analysis. American Economic Review 50(1): 186–88.Google Scholar
Efron, B. 1982. The jackknife, the bootstrap and other resampling plans. Vol. 38 of CBMS-NSF Regional Conference Series in Applied Mathematics. Philadelphia: SIAM.Google Scholar
Ford, L. R. Jr 1957. Solution of a ranking problem from binary comparisons. American Mathematical Monthly 64(8): 28–33.CrossRefGoogle Scholar
Fowler, J. H., Johnson, T. R., Spriggs, J. F. II, Jeon, S., and Paul, J. 2007. Network analysis and the law: Measuring the legal importance of Supreme Court precedents. Political Analysis 15(3): 324–46.CrossRefGoogle Scholar
Glasgow, Garrett. 2001. Mixed logit models for multiparty elections. Political Analysis 9(2): 116–36.CrossRefGoogle Scholar
Greene, William H. 2008. Econometric analysis. 6th ed. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Jech, Thomas. 1983. The ranking of incomplete tournaments: A mathematician's guide to popular sports. American Mathematical Monthly 90(4): 246–64, 265–66.CrossRefGoogle Scholar
Jost, John T., Federico, Christopher M., and Napier, Jaime L. N.d. Forthcoming.Google Scholar
Katz, Jonathan, and King, Gary. 1999. A statistical model for multiparty electoral data. American Political Science Review 93: 15–32.CrossRefGoogle Scholar
Laslier, Benoit, and Laslier, Jean-François. 2013. Reinforcement learning from comparisons: Three alternatives is enough, two is not. Working paper.Google Scholar
Laslier, Jean-François. 1997. Tournament solutions and majority voting. Studies in Economic Theory Series. Berlin: Springer.Google Scholar
Luce, R. Duncan. 1959. Individual choice behavior: A theoretical analysis. New York: Wiley.Google Scholar
Malhotra, Neil, and Margalit, Yotam. 2010. Short-term communication effects or longstanding dispositions? The publics response to the financial crisis of 2008. Journal of Politics 72: 852–67.CrossRefGoogle Scholar
McFadden, Daniel. 1974. Conditional logit analysis of qualitative choice behavior. In Frontiers in econometrics, ed. Zarembka, P., 105–42. New York: Academic Press.Google Scholar
Page, S. E. 2007. The difference: How the power of diversity creates better groups, firms, schools, and societies. Princeton, NJ: Princeton University Press.Google Scholar
Patty, John W., Penn, Elizabeth M., and Schnakenberg, Keith E. 2013. Measuring the latent quality of precedent: Scoring vertices in a network. In Advances in political economy: Institutions, modelling and empirical analysis, eds. Kselman, Daniel, Caballero, Gonzalo, and Schofield, Norman. Berlin: Springer.Google Scholar
Pendergrass, Robert N., and Bradley, Ralph A. 1960. Ranking in triple comparisons. In Contributions to probability and statistics, eds. Olkin, O., Ghurye, S. G., Hoeffding, W., Madow, W. G., and Mann, H. B., 331–51. Stanford, CA: Stanford University Press.Google Scholar
Rubinstein, Ariel. 1980. Ranking the participants in a tournament. SIAM Journal on Applied Mathematics 38(1): 108–11.CrossRefGoogle Scholar
Schnakenberg, Keith. 2013. Replication data for: Replication materials for “Scoring from Contests”. http://hdl.handle.net/1902.1/21998 IQSS Dataverse Network. V1.Google Scholar
Stob, Michael. 1984. A Supplement to “A Mathematician's Guide to Popular Sports.” American Mathematical Monthly 91(5): 277–82.Google Scholar
Tversky, Amos. 1972. Elimination by aspects: A theory of choice. Psychological Review 79(4): 281.CrossRefGoogle Scholar
Varadhan, Ravi, and Gilbert, Paul. 2009. BB: An R package for solving a large system of nonlinear equations and for optimizing a high-dimensional nonlinear objective function. Journal of Statistical Software 32(4): 1–26.CrossRefGoogle Scholar
Winter, Nicholas, and Berinsky, Adam. 1999. What's your temperature? Thermometer ratings and political analysis. Working paper.Google Scholar
Yamamoto, Teppei. 2011. A multinomial response model for varying choice sets, with application to partially contested multiparty elections. Working paper.Google Scholar
Zermelo, Ernst. 1929. Die berechnung der turnier-ergebnisse als ein maximumproblem der wahrscheinlichkeitsrechnung. Mathematische Zeitschrift 29: 346–60.CrossRefGoogle Scholar