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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.

Stabilisation, as a concept and set of practices, has proliferated over the past two decades and is now implicitly integrated into a range of global frameworks. However, this enthusiasm has at times risked turning this increasingly common, albeit contested, idea into a piece of jargon that discounts its unique facets: a focus on all sorts of violence, not just conflict, that create political instability and human harm and a problem-solving approach that draws selectively on various forms of intervention (e.g., statebuilding, counterinsurgency, peacekeeping, etc.) without being beholden to any one toolkit. The pragmatism inherent within the concept of stabilisation will grow increasingly important as new security challenges emerge or proliferate. These include the fragmentation and regionalisation of conflict systems, transnational organised crime, large-scale migration and new, disruptive technologies. Novel approaches rooted in big data and technology will increasingly need to be applied. Most importantly, in foreign policy, military and development communities often driven by perceptions about what causes, ends or prevents violence, stabilisation must maintain its agnostic, problem-solving roots and allegiance to evidence over ideology.