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.

中文翻译：

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2023/24 年度 VIEWS 预测挑战赛：预测武装冲突中的死亡人数，但存在不确定性


政府和非政府组织越来越依赖冲突早期预警系统来支持他们的决策。事实证明，将战争强度预测为概率分布比点估计更接近政策制定者的需求，因为它们包含最可能的结果和冲突灾难性升级的较低概率风险的有用表示。相比之下，点估计预测无法代表冲突死亡人数分布的内在不确定性。然而，目前的早期预警系统主要侧重于提供点估计，而将冲突死亡人数作为概率分布进行预测的努力仍然很少。在前身 VIEWS 比赛的基础上，我们组织了一次预测挑战赛，以鼓励朝着这个方向努力。我们邀请从冲突研究到计算机科学等多个学科领域的研究人员，以 UCDP“最佳”估计的形式预测国家武装冲突中的死亡人数，该估计汇总为两个分析单位（国家-月和 PRIO-GRID-月），并带有不确定性估计。本文介绍了预测挑战背后的目标和动机，提出了一组评估指标来评估预测模型的性能，描述了评估贡献的基准模型，并总结了所提交贡献的突出特点。