Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Peace Economics, Peace Science and Public Policy

Editor-in-Chief: Caruso, Raul

Ed. by Bove, Vincenzo / Kibris, Arzu / Sekeris, Petros

4 Issues per year


CiteScore 2017: 0.54

SCImago Journal Rank (SJR) 2017: 0.304
Source Normalized Impact per Paper (SNIP) 2017: 0.540

Online
ISSN
1554-8597
See all formats and pricing
More options …
Ahead of print

Issues

Volume 17 (2011)

Volume 4 (1996)

Volume 3 (1995)

Volume 2 (1994)

Volume 1 (1993)

Predicting Terrorism with Machine Learning: Lessons from “Predicting Terrorism: A Machine Learning Approach”

Atin Basuchoudhary / James T. Bang
Published Online: 2018-11-29 | DOI: https://doi.org/10.1515/peps-2018-0040

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.

Keywords: predicting terrorism; terrorism risk; Machine Learning; Artificial Intelligence; predictive analytics

References

  • Bang, J., Basuchoudhary, A., David, J., & Mitra, A. (2018). Predicting terrorism: a machine learning approach. Working Paper.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.

  • Bassetti, T., Caruso, R., & Schneider, F. (2018). The tree of political violence: a GMERT analysis. Empirical Economics, 54, 839–850.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.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.Google Scholar

  • Blattman, C., & Miguel, E. (2010). Civil war. Journal of Economic Literature, 48(1), 3–57.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.Google Scholar

  • Enders, W., & Hoover, G. A. (2012). The nonlinear relationship between terrorism and poverty. American Economic Review, 102(3), 267–272.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.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.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.Google Scholar

  • Greenwell, B. M. (2017). pdp: an r package for constructing partial dependence plots. The R Journal, 9(1), 421–436.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.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.Google Scholar

  • Krueger, A., & Maleckova, J. (2003). Education, poverty, and terrorism: Is there a causal connection? Journal of Economic Perspectives, 17(4), 119–144.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.Google Scholar

  • Piazza, J. A. (2011). Poverty, minority economic discrimination and domestic terrorism. Journal of Peace Research, 48(3), 339–353.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.Google Scholar

  • Powell, R. (2007). Defending against terrorist attacks with limited resources. American Political Science Review 101(3), 527–541.Google Scholar

  • Sandler, T. (2014). The analytical study of terrorism: taking stock. Journal of Peace Research, 51(2), 257–271.Google Scholar

  • Sandler, T., & Enders, W. (2012). The political economy of terrorism. Cambridge: Cambridge University Press.Google Scholar

  • Varian, H. (2014). Big data, new tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28.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.Google Scholar

About the article

Published Online: 2018-11-29


Citation Information: Peace Economics, Peace Science and Public Policy, 20180040, ISSN (Online) 1554-8597, DOI: https://doi.org/10.1515/peps-2018-0040.

Export Citation

©2018 Walter de Gruyter GmbH, Berlin/Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in