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A network approach to measuring state preferences

Published online by Cambridge University Press:  20 January 2021

Max Gallop  and
Shahryar Minhas Open the ORCID record for Shahryar Minhas [Opens in a new window]
Max Gallop
Affiliation:
Departments of Political Science, University of Strathclyde, Glasgow, UK
Shahryar Minhas*
Affiliation:
Department of Political Science, Michigan State University, East Lansing, Michigan (e-mail: max.gallop@strath.ac.uk)
*
*Corresponding author. Email: minhassh@msu.edu
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Abstract

State preferences play an important role in international politics. Unfortunately, actually observing and measuring these preferences are impossible. In general, scholars have tried to infer preferences using either UN voting or alliance behavior. The two most notable measures of state preferences that have flowed from this research area are ideal points (Bailey et al., 2017) and S-scores (Signorino & Ritter, 1999). The basis of both these models is a spatial weighting scheme that has proven useful but discounts higher-order effects that might be present in relational data structures such as UN voting and alliances. We begin by arguing that both alliances and UN voting are simply examples of the multiple layers upon which states interact with one another. To estimate a measure of state preferences, we utilize a tensor decomposition model that provides a reduced-rank approximation of the main patterns across the layers. Our new measure of preferences plausibly describes important state relations and yields important insights on the relationship between preferences, democracy, and international conflict. Additionally, we show that a model of conflict using this measure of state preferences decisively outperforms models using extant measures when it comes to predicting conflict in an out-of-sample context.

Keywords

international relationsstate preferencesmeasurementmultilayer networks
Type
Research Article
Information
Network Science , Volume 9 , Issue 2 , June 2021 , pp. 135 - 152
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

Action Editor: Stanley Wasserman

**

We would like to thank comments from discussants and audience members at the International Studies Association and Midwest Political Science Association Conferences as well as participants from the Speaker Series at the University College London. We are also grateful for comments from Michael D. Ward, Scott de Marchi, and three anonymous reviewers who provided thoughtful insight to help us improve our paper.

References

Airoldi, E. M., Blei, D. M., Fienberg, S. E., & Xing, E. P. (2008). Mixed membership stochastic blockmodels. Journal of Machine Learning Research, 9, 1981–2014.Google ScholarPubMed
Akdemir, D., & Gupta, A. K. (2011). Array variate random variables with multiway kronecker delta covariance matrix structure. Journal of Algebraic Statistics, 2, 98–113.CrossRefGoogle Scholar
Bailey, M. A., Strezhnev, A., & Voeten, E. (2017). Estimating dynamic state preferences from United Nations voting data. Journal of Conflict Resolution, 61, 430–456.CrossRefGoogle Scholar
BBC News (2015). Turkey’s downing of russian warplane - what we know. https://www.bbc.com/news/world-middle-east-34912581.Google Scholar
Beardsley, K., Liu, H., Mucha, P. J., Siegel, D. A., & Tellez, J. (2018). Hierarchy and the Provision of Order in International Politics. Technical Report. Working paper, Duke University.Google Scholar
Bennett, D. S., & Rupert, M. C. (2003). Comparing measures of political similarity: An empirical comparison of S versus τ b in the study of international conflict. Journal of Conflict Resolution, 47, 367–393.Google Scholar
Braumoeller, B. F. (2008). Systemic politics and the origins of great power conflict. American Political Science Review, 102, 77–93.CrossRefGoogle Scholar
Bueno de Mesquita, B. (1983). The war trap. New Haven, CT: Yale University Press.Google Scholar
Bueno De Mesquita, B., & Lalman, D. (2008). War and reason: Domestic and international imperatives. New Haven, CT: Yale University Press.Google Scholar
Carter, D. B., & Signorino, C. S. (2010). Back to the future: Modeling time dependence in binary data. Political Analysis, 18, 271–292.CrossRefGoogle Scholar
Clinton, J., Jackman, S., & Rivers, D. (2004). The statistical analysis of roll call data. American Political Science Review, 98, 355–370.CrossRefGoogle Scholar
Cranmer, S. J., Desmarais, B. A., & Kirkland, J. H. (2012). Toward a network theory of alliance formation. International Interactions, 38, 295–324.CrossRefGoogle Scholar
Cranmer, S. J., Menninga, E., & Mucha, P. (2015). Kantian fractionalization predicts the conflict propensity of the international system. Proceedings of the National Academy of Sciences, 112, 11812–11816.CrossRefGoogle ScholarPubMed
DeRouen, K., & Heo, U. (2004). Reward, punishment or inducement? US economic and military aid, 1946–1996. Defence and Peace Economics, 15, 453–470.CrossRefGoogle Scholar
Durante, D., Dunson, D. B., & Vogelstein, J. T. (2017). Nonparametric bayes modeling of populations of networks. Journal of the American Statistical Association, 112, 1516–1530.CrossRefGoogle Scholar
Farber, H., & Gowa, J. (1995). Politics and peace. International Security, 20, 123–146.CrossRefGoogle Scholar
Fariss, C. J. (2014). Respect for human rights has improved over time: Modeling the changing standard of accountability. American Political Science Review, 108, 297–318.CrossRefGoogle Scholar
Gallop, M. (2017). More dangerous than dyads: How a third party enables rationalist explanations for war. Journal of Peace Research, 29, 353–381.Google Scholar
Gartzke, E. (1998). Kant we all just get along? Opportunity, willingness, and the origins of the democratic peace. American Journal of Political Science, 42, 1–27.CrossRefGoogle Scholar
Gartzke, E. (2000). Preferences and the democratic peace. International Studies Quarterly, 44, 191–212.CrossRefGoogle Scholar
Gartzke, E. (2007). The capitalist peace. American Journal of Political Science, 51, 166–191.CrossRefGoogle Scholar
Gibler, D. M., & Sarkees, M. (2004). Measuring alliances: The correlates of war formal interstate alliance data set, 1816-2000. Journal of Peace Research, 41, 211–222.CrossRefGoogle Scholar
Goldenberg, A., Zheng, A. X., Fienberg, S. E., & Airoldi, E. M. (2010). A survey of statistical network models. Foundations and Trends in Machine Learning, 2, 129–233.CrossRefGoogle Scholar
Häge, F. M. (2011). Choice or circumstance? Adjusting measures of foreign policy similarity for chance agreement. Political Analysis, 19, 287–305.CrossRefGoogle Scholar
Hoff, P. D. (2011). Separable covariance arrays via the tucker product, with applications to multivariate relational data. Bayesian Analysis, 6, 179–196.CrossRefGoogle Scholar
Hoff, P. D. (2015). Multilinear tensor regression for longitudinal relational data. The Annals of Applied Statistics, 9, 1169–1193.CrossRefGoogle ScholarPubMed
Kastner, S. L. (2007). When do conflicting political relations affect international trade? Journal of Conflict Resolution, 51, 664–688.CrossRefGoogle Scholar
Kim, B., Lee, K. H., Xue, L., & Niu, X. (2018). A review of dynamic network models with latent variables. Statistics Surveys, 12, 105–135.CrossRefGoogle ScholarPubMed
Kinne, B. J., & Bunte, J. B. (2018). Guns or money? defense co-operation and bilateral lending as coevolving networks. British Journal of Political Science, 50, 1–22.Google Scholar
Kolda, T. G. (2006). Multilinear operators for higher-order decompositions. Technical Report. Sandia National Laboratories.Google Scholar
Kolda, T. G., & Bader, B. W. (2009). Tensor decompositions and applications. SIAM Review, 51, 455–500.CrossRefGoogle Scholar
König, T., Marbach, M., & Osnabrügge, M. (2013). Estimating party positions across countries and time—a dynamic latent variable model for manifesto data. Political Analysis, 21, 468–491.CrossRefGoogle Scholar
Kydd, A. (2003). Which side are you on? Bias, credibility, and mediation. American Journal of Political Science, 47, 597–611.CrossRefGoogle Scholar
Linzer, D. A., & Staton, J. K. (2015). A global measure of judicial independence, 1948–2012. Journal of Law and Courts, 3, 223–256.CrossRefGoogle Scholar
Martin, A. D., & Quinn, K. M. (2002). Dynamic ideal point estimation via markov chain, monte carlo for the U.S. Supreme Court, 1953-1999. Political Analysis, 10, 134–153.CrossRefGoogle Scholar
Minhas, S., Hoff, P. D., & Ward, M. D. (2016). A new approach to analyzing coevolving longitudinal networks in international relations. Journal of Peace Research, 53, 491–505.CrossRefGoogle Scholar
Minhas, S., Hoff, P. D., & Ward, M. D. (2017). Influence networks in international relations. URL: https://arxiv.org/abs/1706.09072. working paper.Google Scholar
Minhas, S., Hoff, P. D., & Ward, M. D. (2019). Inferential approaches for network analysis: Amen for latent factor models. Political Analysis, 27, 208–222. doi: 10.1017/pan.2018.50.CrossRefGoogle Scholar
Moody, J., & White, D. R. (2003). Structural cohesion and embeddedness: A hierarchical concept of social groups. American Sociological Review, 68, 103–127.CrossRefGoogle Scholar
Oneal, J., & Russett, B. M. (1999). Is the liberal peace just an artifact of cold war interests? Assessing recent critiques. International Interactions, 25, 213–241.CrossRefGoogle Scholar
Oneal, J. R., & Russett, B. M. (1997). The classical liberals were right: Democracy, interdependence, and conflict, 1950-1985. International Studies Quarterly, 41, 267–293.CrossRefGoogle Scholar
Poole, K. T., & Rosenthal, H. (1985). A spatial model for legislative roll call analysis. American Journal of Political Science, 29, 357–384.CrossRefGoogle Scholar
Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581–592.CrossRefGoogle Scholar
Sewell, D. K., & Chen, Y. (2015). Latent space models for dynamic networks. Journal of the American Statistical Association, 110, 1646–1657.CrossRefGoogle Scholar
Signorino, C., & Ritter, J. (1999). Tau-b or not tau-b. International Studies Quarterly, 43, 115–144.CrossRefGoogle Scholar
Singer, J. D., & Small, M. (1995). National military capabilities data. Ann Arbor, MI: Correlates of War Project.Google Scholar
Snijders, T. A., & Nowicki, K. (1997). Estimation and prediction for stochastic blockmodels for graphs with latent block structure. Journal of Classification, 14, 75–100.CrossRefGoogle Scholar
Sohn, Y., & Park, J. H. (2017). Bayesian approach to multilayer stochastic blockmodel and network changepoint detection. Network Science, 5, 164–186.CrossRefGoogle Scholar
Stone, R. W. (2004). The political economy of IMF lending in Africa. American Political Science Review, 98, 577–591.CrossRefGoogle Scholar
Voeten, E. (2013). Data and analyses of voting in the United Nations. Routledge. Chapter 4.Google Scholar
Warren, T. C. (2010). The geometry of security: Modeling interstate alliances as evolving networks. Journal of Peace Research, 47, 697–709.CrossRefGoogle Scholar
Warren, T. C. (2016). Modeling the co-evolution of international and domestic institutions: Alliances, democracy, and the complex path to peace. Journal of Peace Research, 53, tbd.CrossRefGoogle Scholar
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Wolford, S. (2014). Showing restraint, signaling resolve: Coalitions, cooperation, and crisis bargaining. American Journal of Political Science, 58, 144–156.CrossRefGoogle Scholar