Predicting Terrorism with Machine Learning: Lessons from “Predicting Terrorism: A Machine Learning Approach”
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Atin Basuchoudhary
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James T. Bang
Abstract
This paper highlights how machine learning can help explain terrorism. We note that even though machine learning has a reputation for black box prediction, in fact, it can provide deeply nuanced explanations of terrorism. Moreover, machine learning is not sensitive to the sometimes heroic statistical assumptions necessary when parametric econometrics is applied to the study of terrorism. This increases the reliability of explanations while adding contextual nuance that captures the flavor of individualized case analysis. Nevertheless, this approach also gives us a sense of the replicability of results. We, therefore, suggest that it further expands the role of science in terrorism research.
References
Bang, J., Basuchoudhary, A., David, J., & Mitra, A. (2018). Predicting terrorism: a machine learning approach. Working Paper.Suche in Google Scholar
Bang, J., Basuchoudhary, A., & Mitra, A. (2018). The Machine Learning Political Indicators Dataset. Researchgate. September 27. https://www.researchgate.net/publication/316118794_The_Machine_Learning_Political_Indicators_Dataset.Suche in Google Scholar
Bassetti, T., Caruso, R., & Schneider, F. (2018). The tree of political violence: a GMERT analysis. Empirical Economics, 54, 839–850.10.1007/s00181-016-1214-1Suche in Google Scholar
Basuchoudhary, A., & Shughart, W. F. (2010). On ethnic conflict and the origins of transnational terrorism. Defence and Peace Economics, 21(1), 65–87.10.4324/9781315235691-9Suche in Google Scholar
Basuchoudhary, A., Bang, J. T., Sen, T., & David, J. (2018). Predicting hotspots: using machine learning to predict civil conflict. Lanham: Lexington Books, An imprint of The Rowman and Littlefield Publishing Group.Suche in Google Scholar
Blattman, C., & Miguel, E. (2010). Civil war. Journal of Economic Literature, 48(1), 3–57.10.3386/w14801Suche in Google Scholar
De la Calle, L., & Sanchez-Cuenc, I. (2012). Rebels without a territory: an analysis of nonterritorial conflicts in the world, 1970–1997. Journal of Conflict Resolution, 56(4), 580–603.10.1177/0022002711431800Suche in Google Scholar
Enders, W., & Hoover, G. A. (2012). The nonlinear relationship between terrorism and poverty. American Economic Review, 102(3), 267–272.10.1257/aer.102.3.267Suche in Google Scholar
Enders, W., & Sandler, T. (1993). The effectiveness of anti-terrorism policies: A vector autoregression intervention analysis. American Political Science Review, 87(4), 829–844.10.2307/2938817Suche in Google Scholar
Findley, M., & Young, J. K. (2012). Terrorism and civil war: A spatial and temporal approach to a conceptual problem. Perspectives on Politics, 10(2), 285–305.10.1017/S1537592712000679Suche in Google Scholar
Gassebner, M., & Luechinger, S. (2011). Lock, stock, and barrel: a comprehensive assessment of the determinants of terror. Public Choice, 149(3), 235–261.10.1007/s11127-011-9873-0Suche in Google Scholar
Greenwell, B. M. (2017). pdp: an r package for constructing partial dependence plots. The R Journal, 9(1), 421–436.10.32614/RJ-2017-016Suche in Google Scholar
Jong-A-Pin, R. (2009). On the measurement of political instability and its impact on economic growth. European Journal of Political Economy, 25(1), 15–29.10.1016/j.ejpoleco.2008.09.010Suche in Google Scholar
Kennedy, R. (2015). Making useful conflict predictions: Methods for addressing skewed classes and implementing cost-sensitive learning in the study of state failure. Journal of Peace Research, 52(5), 649–664.10.1177/0022343315585611Suche in Google Scholar
Krueger, A., & Maleckova, J. (2003). Education, poverty, and terrorism: Is there a causal connection? Journal of Economic Perspectives, 17(4), 119–144.10.4324/9781315235691-12Suche in Google Scholar
Muchlinski, D., Siroky, D., He, J., & Kocher, M. (2016). Comparing random forest with logistic regression for predicting class-imbalanced civil war onset data. Political Analysis, 24, 87–103.10.1093/pan/mpv024Suche in Google Scholar
Piazza, J. A. (2011). Poverty, minority economic discrimination and domestic terrorism. Journal of Peace Research, 48(3), 339–353.10.1177/0022343310397404Suche in Google Scholar
Polo, S. M. T., & Gleditsch, K. S. (2016). Twisting arms and sending messages: Terrorist tactics in civil war. Journal of Peace Research, 53(6), 814–829.10.1177/0022343316667999Suche in Google Scholar
Powell, R. (2007). Defending against terrorist attacks with limited resources. American Political Science Review 101(3), 527–541.10.1017/S0003055407070244Suche in Google Scholar
Sandler, T. (2014). The analytical study of terrorism: taking stock. Journal of Peace Research, 51(2), 257–271.10.1177/0022343313491277Suche in Google Scholar
Sandler, T., & Enders, W. (2012). The political economy of terrorism. Cambridge: Cambridge University Press.Suche in Google Scholar
Varian, H. (2014). Big data, new tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28.10.1257/jep.28.2.3Suche in Google Scholar
Ward, M., Greenhill, B. D., & Bakke, K. M. 2010. The perils of policy by p-Value: Predicting Civil Conflict. Journal of Peace Research, 47(4), 363–375.10.1177/0022343309356491Suche in Google Scholar
Article Note
Predicting terrorism: a machine learning approach was presented at the 19th Jan Tinbergen European Peace Science Conference (2018), Verona.
©2018 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Editorial
- Introduction to the Proceedings of the 18th Jan Tinbergen European Peace Science Conference
- Survey or Review
- Systematic Study of Gender, Conflict, and Peace
- Letters and Proceedings
- Political Initiatives and Peacekeeping: Assessing Multiple UN Conflict Resolution Tools
- US Military Response to the Risk of Terrorist Attacks
- Do Foreign Aid Projects Attract Transnational Terrorism?
- Military Spending and Inequality in Autocracies
- Beyond a Bag of Words: Using PULSAR to Extract Judgments on Specific Human Rights at Scale
- Predicting Terrorism with Machine Learning: Lessons from “Predicting Terrorism: A Machine Learning Approach”
- Conflict in Cyber-Space: The Network of Cyber Incidents, 2000–2014
- What do they Want? Rebels’ Objectives and Civil War Mediation
Artikel in diesem Heft
- Editorial
- Introduction to the Proceedings of the 18th Jan Tinbergen European Peace Science Conference
- Survey or Review
- Systematic Study of Gender, Conflict, and Peace
- Letters and Proceedings
- Political Initiatives and Peacekeeping: Assessing Multiple UN Conflict Resolution Tools
- US Military Response to the Risk of Terrorist Attacks
- Do Foreign Aid Projects Attract Transnational Terrorism?
- Military Spending and Inequality in Autocracies
- Beyond a Bag of Words: Using PULSAR to Extract Judgments on Specific Human Rights at Scale
- Predicting Terrorism with Machine Learning: Lessons from “Predicting Terrorism: A Machine Learning Approach”
- Conflict in Cyber-Space: The Network of Cyber Incidents, 2000–2014
- What do they Want? Rebels’ Objectives and Civil War Mediation
