The 2023/24 VIEWS Prediction challenge: Predicting the number of fatalities in armed conflict, with uncertainty

Journal article

Hegre, Håvard; Paola Vesco; Michael Colaresi; Jonas Vestby; Alexa Timlick; Noorain Syed Kazmi; Angelica Lindqvist-McGowan; Friederike Becker; Marco Binetti; Tobias Bodentien; Tobias Bohne; Patrick Brandt; Thomas Chadefaux; Simon Drauz; Christopher Daase; Vito D’Orazio; Hannah Frank; Cornelius Fritz; Kristian Skrede Gleditsch; Sonja Häffner; Martin Hofer; Finn L. Klebe; Luca Macis; Alexandra Malaga; Marius Mehrl; Nils W. Metternich; Daniel Mittermaier; David Muchlinski; Hannes Mueller; Christian Oswald; Paola Pisano; David Randahl; Christopher Rauh; Lotta Rüter; Thomas Schincariol; Benjamin Seimon; Elena Siletti; Marco Tagliapietra; Chandler Thornhill; Johan Vegelius & Julian Walterskirchen (2025) The 2023/24 VIEWS Prediction challenge: Predicting the number of fatalities in armed conflict, with uncertainty, Journal of Peace Research. DOI: https://doi.org/10.1177/00223433241300862.

Read the article here (Open Access)

Governmental and nongovernmental organizations have increasingly relied on early-warning systems of conflict to support their decisionmaking. Predictions of war intensity as probability distributions prove closer to what policymakers need than point estimates, as they encompass useful representations of both the most likely outcome and the lower-probability risk that conflicts escalate catastrophically. Point-estimate predictions, by contrast, fail to represent the inherent uncertainty in the distribution of conflict fatalities. Yet, current early warning systems are preponderantly focused on providing point estimates, while efforts to forecast conflict fatalities as a probability distribution remain sparse. Building on the predecessor VIEWS competition, we organize a prediction challenge to encourage endeavours in this direction. We invite researchers across multiple disciplinary fields, from conflict studies to computer science, to forecast the number of fatalities in state-based armed conflicts, in the form of the UCDP ‘best’ estimates aggregated to two units of analysis (country-months and PRIO-GRID-months), with estimates of uncertainty. This article introduces the goal and motivation behind the prediction challenge, presents a set of evaluation metrics to assess the performance of the forecasting models, describes the benchmark models which the contributions are evaluated against, and summarizes the salient features of the submitted contributions.

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