The 2023/24 VIEWS Prediction Challenge: Predicting the Number of Fatalities in Armed Conflict, with Uncertainty

arXiv - STAT - Computation Pub Date : 2024-07-08 DOI:arxiv-2407.11045

Håvard HegrePeace Research Institute OsloDepartment of Peace and Conflict Research, Uppsala University, Paola VescoPeace Research Institute OsloDepartment of Peace and Conflict Research, Uppsala University, Michael ColaresiDepartment of Peace and Conflict Research, Uppsala UniversityUniversity of Pittsburgh, Jonas VestbyPeace Research Institute Oslo, Alexa TimlickPeace Research Institute Oslo, Noorain Syed KazmiPeace Research Institute Oslo, Friederike BeckerInstitute of Statistics, Marco BinettiCenter for Crisis Early Warning, University of the Bundeswehr Munich, Tobias BodentienInstitute of Statistics, Tobias BohneCenter for Crisis Early Warning, University of the Bundeswehr Munich, Patrick T. BrandtSchool of Economic, Political, and Policy Sciences, University of Texas, Dallas, Thomas ChadefauxTrinity College Dublin, Simon DrauzInstitute of Statistics, Christoph DworschakUniversity of York, Vito D'OrazioWest Virginia University, Cornelius FritzPennsylvania State University, Hannah FrankTrinity College Dublin, Kristian Skrede GleditschUniversity of EssexPeace Research Institute Oslo, Sonja HäffnerCenter for Crisis Early Warning, University of the Bundeswehr Munich, Martin HoferUniversity College London, Finn L. KlebeUniversity College London, Luca MacisDepartment of Economics and Statistics Cognetti de Martiis, University of Turin, Alexandra MalagaInstitute for Economic Analysis, Barcelona, Marius MehrlUniversity of Leeds, Nils W. MetternichUniversity College London, Daniel MittermaierCenter for Crisis Early Warning, University of the Bundeswehr Munich, David MuchlinskiGeorgia Tech, Hannes MuellerInstitute for Economic Analysis, BarcelonaBarcelona School of Economics, Christian OswaldCenter for Crisis Early Warning, University of the Bundeswehr Munich, Paola PisanoDepartment of Economics and Statistics Cognetti de Martiis, University of Turin, David RandahlDepartment of Peace and Conflict Research, Uppsala University, Christopher RauhUniversity of Cambridge, Lotta RüterInstitute of Statistics, Thomas SchincariolTrinity College Dublin, Benjamin SeimonFundació Economia Analitica, Elena SilettiDepartment of Economics and Statistics Cognetti de Martiis, University of Turin, Marco TagliapietraDepartment of Economics and Statistics Cognetti de Martiis, University of Turin, Chandler ThornhillGeorgia Tech, Johan VegeliusDepartment of Medical Sciences, Uppsala University, Julian WalterskirchenCenter for Crisis Early Warning, University of the Bundeswehr Munich

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Abstract

This draft article outlines a prediction challenge where the target is to forecast the number of fatalities in armed conflicts, in the form of the UCDP `best' estimates, aggregated to the VIEWS units of analysis. It presents the format of the contributions, the evaluation metric, and the procedures, and a brief summary of the contributions. The article serves a function analogous to a pre-analysis plan: a statement of the forecasting models made publicly available before the true future prediction window commences. More information on the challenge, and all data referred to in this document, can be found at https://viewsforecasting.org/research/prediction-challenge-2023.

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2023/24 VIEWS 预测挑战赛：在不确定情况下预测武装冲突中的死亡人数

本文草案概述了一项预测挑战，其目标是以 UCDP "最佳 "估计值的形式预测武装冲突中的死亡人数，并将其汇总到 VIEWS 分析单元。文章介绍了贡献的格式、评估指标和程序，并对贡献进行了简要总结。这篇文章的作用类似于分析前计划：在真正的未来预测窗口开始之前，公布预测模型的说明。有关挑战赛的更多信息以及本文件中提到的所有数据，请访问：https://viewsforecasting.org/research/prediction-challenge-2023。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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arXiv - STAT - Computation

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