Discovering the mesoscale for chains of conflict

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.

arXiv comment17 pages

References in corpus (11)

• Power-law distributions in empirical data
• Measuring Information Transfer
• Crackling Noise
• Granger causality and transfer entropy are equivalent for Gaussian variables
• Dynamics of conflicts in Wikipedia
• Coarse--graining, fixed points, and scaling in a large population of neurons
• Reducing the Bias of Causality Measures
• Monitoring War Destruction from Space: A Machine Learning Approach
• Unveiling causal activity of complex networks
• Evidence of strategic periodicities in collective conflict dynamics
• A scaling theory of armed conflict avalanches

Cited by in corpus (1)

• Common indicators hurt armed conflict prediction