Purpose — This paper aims to examine whether changing the clustering of countries within a United States Combatant Command (COCOM) area of responsibility promotes improved forecasting of conflict. Design/methodology/approach — In this paper statistical learning methods are used to create new country clusters that are then used in a comparative analysis of model-based conflict prediction. Findings — In this study a reorganization of the countries assigned to specific areas of responsibility are shown to provide improvements in the ability of models to predict conflict. Research limitations/implications — The study is based on actual historical data and is purely data driven. Practical implications — The study demonstrates the utility of the analytical methodology but carries not implementation recommendations. Originality/value — This is the first study to use the statistical methods employed to not only investigate the re-clustering of countries but more importantly the impact of that change on analytical predictions.
Journal of Defense Analytics and Logistics
Neumann, S., Ahner, D., & Hill, R. R. (2022). Forecasting country conflict using statistical learning methods. Journal of Defense Analytics and Logistics, 6(1), 59–72. https://doi.org/10.1108/JDAL-10-2021-0014
All articles published in JDAL are published Open Access under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. CC BY 4.0
Sourced from the publisher version of record at Emerald. The citation and DOI link are noted below.