Building predictive models of counterinsurgent deaths using robust clustering and regression

Abstract

Counterinsurgencies are conflicts where an insurgent organization conducts violence to replace or influence a recognized government. Furthering our understanding of the conditions that influence violence in different types of counterinsurgencies is important to government leaders who must deploy scarce resources efficiently. Subject matter experts (SMEs) have developed classification schemes that divide counterinsurgencies into similar groups, but no data-driven methods have ever been developed. Using the robust partitioning around medoids (PAM) algorithm, we cluster counterinsurgencies based on distances among independent variables measured on each counterinsurgency. We apply several criteria for choosing the optimal number of clusters, and then we take these groups of counterinsurgencies and build regression models for counterinsurgent deaths, an annual measure of conflict status. We evaluate these schemes using cross-validation to select the grouping whose regression models best predict counterinsurgent deaths. This approach produces a set of data-driven clusters whose predictive ability is similar to the best existing SME classification scheme, but reduces error in the assignment of a new counterinsurgency to a cluster.