Forecasting country conflict using statistical learning methods

Abstract

PurposeThis 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/approachIn 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.FindingsIn 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/implicationsThe study is based on actual historical data and is purely data driven.Practical implicationsThe study demonstrates the utility of the analytical methodology but carries not implementation recommendations.Originality/valueThis 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.