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

Robert A. Blair and Nicholas Sambanis

Journal of Conflict Resolution, 2021, vol. 65, issue 7-8, 1427-1453

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.

Keywords: civil wars; forecasting; big data; machine learning (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jocore:v:65:y:2021:i:7-8:p:1427-1453

DOI: 10.1177/00220027211026748

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