An automated pattern recognition system for conflict

This article introduces an automated pattern recognition system for conflict. The monitoring system aims to uncover, cluster, and classify temporal patterns of escalation to improve future forecasts and better understand the causes of escalation toward war. It identifies important temporal patterns in conflict data using novel pattern detection methods and new data. These patterns are used to forecast conflict, with live predictions released in real time. Finally, the discovery of recurring motifs—prototypes—can inform new or existing theoretical frameworks. In this article, I discuss the methodological innovations required to achieve these goals and the path to creating an autonomous conflict monitoring system. I also report on promising results obtained using these methods, which show that they perform well on true out-of- sample forecasts of the count of the number of fatalities per month from state-based conflict. The monitoring system has important implications for computational diplomacy, as it can alert diplomats of geopolitical risks.