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

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

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. The paper can be downloaded freely here. The prediction challenges of the ViEWS team are one of the highlights of my academic career. It is so valuable to see actual out-of-sample predictions clash with reality. Prediction of confidence intervals was not one of our strengths going into the competition but we did extremely well on the national level thanks to the great work of the team and in particular Alexandra Malaga.