Abstract
Analyzing the political, military, economic, social, information, and infrastructure (PMESII) effects in a sociocultural system requires models that capture the causal and predictive dynamics. However, given the complexity of PMESII factors and the diversity of available data sources, accurately modeling causal relationships requires incorporating multiple domains of study and a variety of analytic methods. In this paper, we present an ensemble approach to modeling causal relationships of sociocultural systems, applying insights from machine learning where ensembles consistently outperform individual approaches. We describe three different types of ensemble models and combinations and explore the application of this approach in experiments using both synthetic and real-world datasets.
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References
Granger, C.W.: Investigating causal relations by econometric models and cross-spectral methods. Economet.: J. Economet. Soc. 424–438 (1969)
Granger, C.W.: Testing for causality: a personal viewpoint. J. Econ. Dyn. Control 2, 329–352 (1980)
Sugihara, G., May, R., Ye, H., Hsieh, C., Deyle, E., Fogarty, M., Munch, S.: Detecting causality in complex ecosystems. Science 338(6106), 496–500 (2012)
Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press (2009)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA (1988)
Mooij, J.M., Peters, J., Janzing, D., Zsceischler, J., Schölkopf, B.: Distinguishing cause from effect using observational data: methods and benchmarks. J. Mach. Learn. Res. (To Appear)
Peters, J., Mooij, J.M., Janzing, D., Schölkopf, B.: Causal discovery with continuous additive noise models. J. Mach. Learn. Res. 15(1), 2009–2053 (2014)
Goldthorpe, J.H.: Causation, statistics, and sociology. Eur. Sociol. Rev. 17(1), 1–20 (2001)
Sliva, A., Reilly, S.N., Casstevens, R., Chamberlain, J.: Tools for validating causal and predictive claims in social science models. Proceedings of the 4th international conference on cross cultural decision-making. Proc. Manuf. 3, 3925–3932 (2015)
Sliva, A., Neal Reilly, S.: A big data methodology for bridging quantitative and qualitative political science research. In: American Political Science Association Annual Meeting (2014)
Collier, P., Elliott, V.L., Hegre, H., Hoeffler, A., Reynal-Querol, M., Sambanis, N.: Breaking the Conflict Trap: Civil War and Development Policy. World Bank and Oxford University Press, Washington, DC (2003)
Collier, P., Hoeffler, A.: Greed and grievances in civil wars. Oxford Econ. Papers 56 (2004)
Verma, T., Pearl, J.: Equivalence and synthesis of causal models. In: Proceedings of Sixth Conference on Uncertainty in Artificial Intelligence, pp. 220–227 (1990)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search. MIT Press (2001)
Ruvinsky, A., Lustick, I., Levey, B., Siedlecki, T.: Leveraging big data and social science theory for sociocultural modeling (Special presentation). In: Proceedings of the 4th International Conference on Cross-Cultural Decision Making, AHFE International (2015)
Opitz, D., Maclin, R.: Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 169–198 (1999)
Hancock, Gregory R.: Fortune cookies, measurement error, and experimental design. J. Modern Appl. Stat. Methods 2(2), Article 3 (2003)
Levis, A.: Multi-formalism modeling of human organizations. In: 29th European Conference on Modelling and Simulation (2015)
Acknowledgments
This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA). The views, opinions, and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the US Government.
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Sliva, A., Reilly, S.N., Blumstein, D., Hookway, S., Chamberlain, J. (2017). Modeling Causal Relationships in Sociocultural Systems Using Ensemble Methods. In: Schatz, S., Hoffman, M. (eds) Advances in Cross-Cultural Decision Making. Advances in Intelligent Systems and Computing, vol 480. Springer, Cham. https://doi.org/10.1007/978-3-319-41636-6_4
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DOI: https://doi.org/10.1007/978-3-319-41636-6_4
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