Identifying Motifs and Patterns in War Fatalities: A Matrix Profile Application

• In Event: What We Can Know about the Future of War and Its Humanitarian Impacts?

Abstract

Armed conflicts are complex processes characterised by non-linearities, feedback loops, and recurrent patterns of occurrence and diffusion. Recent progress in statistical and computational methods coupled with an increasing availability of highly granular data have increased our ability to forecast armed conflict occurrence. However, conflict dynamics and related variations in conflict related fatalities remain poorly understood. Improving our understanding of the spatiotemporal patterns and repeating temporal patterns that characterise conflicts is paramount to better understand and forecast conflict dynamics. This paper attempts to fill this gap to identify recurrent trends and patterns underlying casualties in interstate wars. We apply matrix profiles on monthly data on battle death estimates to uncover, cluster, and classify repeating patterns over time — so-called ‘motifs’ — in interstate wars. Next, we empirically assess the contribution of the derived clusters in forecasting the future course of casualties, and discuss their implications for theoretical developments. The identification of underlying motifs characterising war fatalities enable us to increase forecasting performance and improve our understanding of conflict processes.

Authors

• Thomas Schincariol, Trinity College Dublin

• Paola Vesco, Peace Research Institute Oslo (PRIO)

• Thomas Chadefaux, Trinity College Dublin

• Bear F. Braumoeller, Ohio State University