Discovering the mesoscale for chains of conflict

TL;DR

This paper introduces a method to identify mesoscale patterns in conflict events, revealing intermediate spatial and temporal scales where long-range correlations and causal mechanisms emerge, aiding understanding and prediction of conflicts.

Contribution

It develops a systematic, data-driven approach to extract conflict chains at mesoscale, linking statistical clusters with causal mechanisms and incorporating uncertainty.

Findings

Discovered mesoscale ranges from weeks to months and tens to hundreds of kilometers.

Identified conflict zones with causal interactions around hotspots.

Clusters are linked to known causal mechanisms from field studies.

Abstract

Conflicts, like many social processes, are related events that span multiple scales in time, from the instantaneous to multi-year developments, and in space, from one neighborhood to continents. Yet, there is little systematic work on connecting the multiple scales, formal treatment of causality between events, and measures of uncertainty for how events are related to one another. We develop a method for extracting related chains of events that addresses these limitations with armed conflict. Our method explicitly accounts for an adjustable spatial and temporal scale of interaction for clustering individual events from a detailed data set, the Armed Conflict Event & Location Data Project. With it, we discover a mesoscale ranging from a week to a few months and from tens to a few hundred kilometers, where long-range correlations and nontrivial dynamics relating conflict events emerge. Importantly, clusters in the mesoscale, while extracted only from conflict statistics, are identifiable with causal mechanism cited in field studies. We leverage our technique to identify zones of causal interaction around conflict hotspots that naturally incorporate uncertainties. Thus, we show how a systematic, data-driven procedure extracts social objects for study, providing a scope for scrutinizing and predicting conflict amongst other processes.