Supplementary material from "Data-driven conflict classification exposes weak predictive indicators"

Models and theories of armed conflict are effective when tailored to distinct conflict types, but existing classifications are often heuristic. We introduce a data-driven classification that is empirically grounded, reproducible, and consistent across multiple scales. We leverage fine-grained conflict data, which we map to climate, geography, infrastructure, economics, raw demographics, and demographic composition in Africa. Using an unsupervised learning model, we identify three overarching conflict types: ``major-unrest" at densely populated, riparian regions with well developed infrastructure; ``local-conflict" in moderately populated, socio-economically diverse regions and often confined within country borders; and ``sporadic-spillover events" in low-population, underdeveloped areas. The three types stratify into a hierarchy of factors that highlights population, infrastructure, economics, and geography, respectively, as the most discriminative indicators. Specifying conflict type negatively impacts the predictability of conflict intensity such as fatalities, conflict duration, and other measures of conflict size. The competitive effect is a general consequence of weak statistical dependence. Hence, the empirical and bottom-up approach reveals how armed conflicts stratify into three archetypes, yet cautions us about the inclusion of commonly used indicators into predictive modeling.