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The datasets provided in the repository (listed in Table 1 of the manuscript): Governance (Andrijevic et al., 2020)* Government effectiveness (Andrijevic et al., 2020)* Violent conflict (Hegre et al., 2016) Rule of law (update to the Soergel et al., 2021) *Please note that these two variables can be found in the same data file.

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.

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.

Sahel Predictive Analytics project

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 p

The Arab States are affected by a wide range of environmental challenges exacerbated by current and projected impacts of climate change, including, among others, depletion of scarce natural resources such as water and arable land, increasing pollution levels, and the growing number and magnitude of extreme weather events. At the same time, the Arab region has been a hotspot for conflicts during the last decades. This highlights the need among policymakers and practitioners of conflict prevention and peacebuilding to better understand how climate change might contribute to current or future dynamics of conflict. This report provides a conceptual framework for analysts and policymakers in the region that shows how the loss of livelihood, economic contraction, resource competition, migration, poor governance, and other social processes (mechanisms) spurred by climate risk are more likely to increase conflict risk when occurring in certain contexts.