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Conflicts, like many social processes, are related events that span multiplescales in time, from the instantaneous to multi-year developments, and inspace, from one neighborhood to continents. Yet, there is little systematicwork on connecting the multiple scales, formal treatment of causality betweenevents, and measures of uncertainty for how events are related to one another.We develop a method for extracting related chains of events that addressesthese limitations with armed conflict. Our method explicitly accounts for anadjustable spatial and temporal scale of interaction for clustering individualevents from a detailed data set, the Armed Conflict Event & Location DataProject. With it, we discover a mesoscale ranging from a week to a few monthsand from tens to a few hundred kilometers, where long-range correlations andnontrivial dynamics relating conflict events emerge. Importantly, clusters inthe mesoscale, while extracted only from conflict statistics, are identifiablewith causal mechanism cited in field studies. We leverage our technique toidentify zones of causal interaction around conflict hotspots that naturallyincorporate uncertainties. Thus, we show how a systematic, data-drivenprocedure extracts social objects for study, providing a scope for scrutinizingand predicting conflict amongst other processes.

Discovering the mesoscale for chains of conflict