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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 detrimental impacts of wars on human development are well documented across research domains, from public health to micro-economics. However, these impacts are studied in compartmentalized silos, which limits a comprehensive understanding of the consequences of conflicts, hampering our ability to effectively sustain human development. This article takes a first step in addressing this gap by reviewing the literature on conflict impacts through the lens of an inter-disciplinary theoretical framework. We review the literature on the consequences of conflicts across 9 dimensions of human development: health, schooling, livelihood and income, growth and investments, political institutions, migration and displacement, socio-psychological wellbeing and capital, water access, and food security. The study focuses on both direct and indirect impacts of violence, reviews the existing evidence on how impacts on different dimensions of societal wellbeing and development may intertwine, and suggests plausible mechanisms to explain how these connections materialize. This exercise leads to the identification of critical research gaps and reveals that systematic empirical testing of how the impacts of war spread across sectors is severely lacking. By streamlining the literature on the impacts of war across multiple domains, this review represents a first step to build a common language that can overcome disciplinary silos and achieve a deeper understanding of how the effects of war reverberate across society. This multidisciplinary understanding of conflict impacts may eventually help to reconcile divergent estimates and enable forward-looking policies that minimize the costs of war.

Preventing armed conflict is key to promoting development and well-being in the Sahel. To strengthen its work to promote peace globally, the United Nations have recently stressed the importance of early action and early warning about impending conflict. This report presents the ViEWS system, a systematic, data-driven conflict early-warning system developed for Africa and the Middle East. Designed to complement expert assessments drawing on qualitative methods, the system produces estimates of the probability that armed conflict events will occur in countries and sub-national locations during each of the next 1–36 months. The report outlines the main features of the system, discusses the uncertainties involved and how well the system handles these, and presents the latest forecasts for the UNISS countries of the Sahel. The ViEWS forecasts are updated on a monthly basis and made available in full through an API, and in summarised form on the ViEWS website. The report also outlines some possible future developments and improvements of the system. 

Recent research on the forecasting of violence has mostly focused on predicting the presence or absence of conflict in a given location, while much less attention has been paid to predicting changes in violence. We organized a prediction competition to forecast changes in state-based violence both for the true future and for a test partition. We received contributions from 15 international teams. The models leverage new insight on the targeted problem, insisting on methodological advances, new data and features, and innovative frameworks which contribute to the research frontiers from various perspectives. This article introduces the competition, presents the main innovations fostered by the teams and discusses ways to further expand and improve upon this wisdom of the crowd. We show that an optimal modeling approach builds on a good number of the presented contributions and new evaluation metrics are needed to capture substantial models' improvements and reward unique insights. La investigacion reciente sobre la prevision de la violencia se ha centrado principalmente en predecir la presencia o ausencia de conflictos en un determinado lugar, mientras que se ha prestado mucha menos atencion a predecir los cambios en la violencia. Organizamos una competencia de prediccion para predecir los cambios en la violencia estatal tanto para el futuro cierto como para una division del analisis. Recibimos aportes de quince equipos internacionales. Los modelos aprovechan las nuevas ideas sobre el problema especifico insistiendo en los avances metodologicos, los nuevos datos y caracteristicas, asi como en los marcos innovadores que contribuyen a las fronteras de la investigacion desde diversas perspectivas. Este articulo presenta la competencia y las principales innovaciones que los equipos fomentan, y analiza maneras de expandirse y mejorar aun mas a partir de esta sabiduria del publico. Mostramos que un enfoque de modelacion optimo se crea a partir de un buen numero de aportes presentados y que se necesitan nuevas metricas de evaluacion para capturar las mejoras considerables de los modelos y para premiar las ideas unicas. Les recherches recentes sur la prevision de la violence se sont principalement concentrees sur la prediction de la presence ou de l'absence de conflit dans un lieu donne, alors que beaucoup moins d'attention a ete accordee a la prediction des evolutions de la violence. Nous avons organise un concours de predictions dont l'objectif etait de prevoir les evolutions de la violence etatique a la fois pour le futur reel et pour une partition test. Nous avons recu des contributions de 15 equipes internationales. Les modeles concernes tirent profit de nouveaux renseignements sur le probleme cible en insistant sur les progres methodologiques, sur de nouvelles donnees et caracteristiques et sur des cadres innovants contribuant a elargir les frontieres des recherches de divers points de vue. Cet article presente le concours et les principales innovations proposees par les equipes et aborde les moyens d'etendre et d'ameliorer cette sagesse de la foule. Nous montrons qu'une approche optimale de la modelisation repose sur bon nombre des contributions presentees et que de nouvelles metriques d'evaluation sont necessaires pour saisir les ameliorations substantielles des modeles et recompenser les idees uniques.

This article presents results and lessons learned from a prediction competition by ViEWS to improve collective scientific knowledge on forecasting (de-)escalation on the African continent. The competition call asked participants to forecast changes in state-based violence for the true future (October 2020 – March 2021) as well as for a held-out test partition. An external scoring committee, independent from both the organizers and participants, was formed to evaluate the models based on both qualitative and quanti- tative criteria, including performance, novelty, uniqueness and replicability. All models contributed to advance the research frontier by providing novel methodological or theo- retical insight, including new data, or adopting innovative model specifications. While we discuss several facets of the competition that could be improved moving forward, the collection passes an important test. When we build a simple ensemble prediction model – which draws on the unique insights of each contribution to differing degrees – we can measure an improvement in the prediction from the group, over and above what the average individual model can achieve. This wisdom of the crowd effect suggests that future competitions that build on both the successes and failures of ours, can contribute to scientific knowledge by incentivising diverse contributions as well as focusing a group’s attention on a common problem.

Although substantive agreement exists on the role of climate variability and food scarcity in increasing violence, a limited number of studies have investigated how food resources affect violent conflict. This article explores the complex linkages between climate variability, agricultural production and conflict onset, by focusing on the spatial distribution of crop production in a cross-country setting. We hypothesize that spatial differences in crop production within countries are a relevant factor in shaping the impact of climate variability on conflict in agriculturally -dependent countries. To test this hypothesis, we rely on high-resolution global gridded data on the local yield of four main crops for the period 1982–2015 and aggregate the grid-cell information on crop production to compute an empirical indicator of the spatial concentration of agricultural production within countries. Our results show that the negative impacts of climate variability lead to an increase in the spatial concentration of agricultural production within countries. In turn, the combined effect of climate extremes and crop production concentration increases the predicted probability of conflict onset by up to 14% in agriculturally dependent countries.

This article presents an update to the ViEWS political Violence Early-Warning System. This update introduces (1) a new infrastructure for training, evaluating, and weighting models that allows us to more optimally combine constituent models into ensembles, and (2) a number of new forecasting models that contribute to improve overall performance, in particular with respect to effectively classifying high- and low-risk cases. Our improved evaluation procedures allow us to develop models that specialize in either the immediate or the more distant future. We also present a formal, 'retrospective' evaluation of how well ViEWS has done since we started publishing our forecasts from July 2018 up to December 2019. Our metrics show that ViEWS is performing well when compared to previous out-of-sample forecasts for the 2015-17 period. Finally, we present our new forecasts for the January 2020-December 2022 period. We continue to predict a near-constant situation of conflict in Nigeria, Somalia, and DRC, but see some signs of decreased risk in Cameroon and Mozambique.