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