Reflections on the Evolution of Conflict Early Warning

Conflict early warning is supposed to identify and trigger actions to reduce the onset, duration, intensity, and effects of multiple forms of political violence. While the commitment of nations to broader conflict prevention was not universally shared in the twentieth century, the concept of conflict prevention – and by extension, conflict early warning – has acquired salience in international relations over the last 30 years. This growing engagement, coupled with advances in computing, has triggered increased investment in enhanced early warning mechanisms with increasingly sophisticated temporal and spatial dimensions. Yet, the practical operationalization of conflict prevention and conflict early warning lags behind its theoretical development for several reasons. These include, inter alia, limitations in early warning assessments; the limited availability, coverage, quality and verifiability of real-time data; complex modelling challenges emerging from endogeneity inherent in conflict processes; and, not least, an inherent lack of political will among relevant actors to act upon robust and compelling evidence of heightened risks of organized violence. The latter is the core of the so-called ‘warning-response’ gap. Despite these challenges, investments in advanced data collection and analysis techniques including machine learning, natural language processing and artificial intelligence are influencing the practice of early warning and response. This article offers a descriptive review of the form and function of conflict early warning systems over the past four decades. In the process, it provides insight into why many of these systems have yet to live up to expectations.