Coarse-graining armed conflict

Large-scale armed conflict between groups is a defining phenomenon of modern human civilization, but the absence of a compelling model of conflict that agrees with the data means that prediction of conflict remains rudimentary. In a simple model, the spread of conflict might be described as a propagating avalanche or percolating component that extends across time and space and through a network of related actors. The presence of near power-law statistics in the sizes, durations, and actor network components of conflict suggests that such an abstracted model could provide both useful intuition and quantitative predictions about the structure of conflict on large scales. We explore this perspective in detail by performing a renormalization scheme on the surface of the Earth to generate statistics of conflict avalanches along coarse-grained spatiotemporal scales. We show that some kinds of armed conflict may obey scaling laws that could provide a basis for a predictive theory of conflict based on ideas from statistical physics.