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Is Theory Useful for Conflict Prediction? A Response to Beger, Morgan, and Ward

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  • Robert A. Blair
  • Nicholas Sambanis

Abstract

Beger, Morgan, and Ward (BM&W) call into question the results of our article on forecasting civil wars. They claim that our theoretically-informed model of conflict escalation under-performs more mechanical, inductive alternatives. This claim is false. BM&W’s critiques are misguided or inconsequential, and their conclusions hinge on a minor technical question regarding receiver operating characteristic (ROC) curves: should the curves be smoothed, or should empirical curves be used? BM&W assert that empirical curves should be used and all of their conclusions depend on this subjective modeling choice. We extend our original analysis to show that our theoretically-informed model performs as well as or better than more atheoretical alternatives across a range of performance metrics and robustness specifications. As in our original article, we conclude by encouraging conflict forecasters to treat the value added of theory not as an assumption, but rather as a hypothesis to test.

Suggested Citation

  • Robert A. Blair & Nicholas Sambanis, 2021. "Is Theory Useful for Conflict Prediction? A Response to Beger, Morgan, and Ward," Journal of Conflict Resolution, Peace Science Society (International), vol. 65(7-8), pages 1427-1453, August.
    • Handle: RePEc:sae:jocore:v:65:y:2021:i:7-8:p:1427-1453
    DOI: 10.1177/00220027211026748
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    References listed on IDEAS

    as
    1. Andreas Beger & Richard K. Morgan & Michael D. Ward, 2021. "Reassessing the Role of Theory and Machine Learning in Forecasting Civil Conflict," Journal of Conflict Resolution, Peace Science Society (International), vol. 65(7-8), pages 1405-1426, August.
    2. Chiba, Daina & Metternich, Nils W. & Ward, Michael D., 2015. "Every Story Has a Beginning, Middle, and an End (But Not Always in That Order): Predicting Duration Dynamics in a Unified Framework," Political Science Research and Methods, Cambridge University Press, vol. 3(3), pages 515-541, September.
    3. Beger, Andreas & Dorff, Cassy L. & Ward, Michael D., 2016. "Irregular leadership changes in 2014: Forecasts using ensemble, split-population duration models," International Journal of Forecasting, Elsevier, vol. 32(1), pages 98-111.
    4. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    5. Nils W. Metternich & Cassy Dorff & Max Gallop & Simon Weschle & Michael D. Ward, 2013. "Antigovernment Networks in Civil Conflicts: How Network Structures Affect Conflictual Behavior," American Journal of Political Science, John Wiley & Sons, vol. 57(4), pages 892-911, October.
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