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Electoral violence remains a significant challenge worldwide. It not only threatens to undermine the legitimacy and fairness of electoral outcomes, but often has serious repercussions on political stability broadly. The ability to prevent electoral violence is critical for safeguarding democracy and ensuring peaceful transitions of political power. Predicting which elections are at risk of violence is a crucial step in effective prevention. In this study, we develop a set of machine-learning models to forecast the likelihood of electoral violence worldwide. Using diverse data sources, which include economic indicators, the history of electoral violence, political instability, and digital vulnerability, we predict the risk of electoral violence on a scale ranging from no violence to severe violence. Our final forecasts are produced by combining constituent models into an ensemble using a genetic algorithm. Out-of-sample evaluation of the system shows that the final model accurately distinguishes between different levels of risk. After validating our system on historical data, we generate out-of-sample probabilistic forecasts for national-level elections in 2025 and 2026. This research contributes to the field of political violence prediction by providing a medium-term data-driven forecasting tool for electoral violence.

Event datasets, such as those provided by the Uppsala Conflict Data Program (UCDP), provide high-quality data on conflict fatalities. However, such data are likely to suffer from an unknown extent of bias and uncertainties in the reports they are based on. Although a substantial literature documents reporting bias in conflict research, analyses that quantify this bias are mostly limited to single countries. Here, we combine a survey with UCDP coders and statistical modeling to derive a distribution of plausible number of fatalities given the number of battle-related deaths and the type of violence documented by the UCDP. We provide a generalizable, cross-national measure of uncertainty around UCDP reported fatalities that is more robust and realistic than UCDP’s documented low and high estimates, countering UCDP’s intrinsic tendency to under-estimate fatalities, and we make available a dataset and R package that can be applied to future releases of the UCDP data.

Protest is a low-intensity form of political conflict that can precipitate intrastate armed conflict. Data on protests should therefore be informative in systems that provide early warnings of armed conflict. However, since most protests do not escalate to armed conflict, we first need theory to inform our prediction models. We identify three theoretical explanations relating to protest-repression dynamics, political institutions and economic development as the basis for our models. Based on theory, we operationalize nine models and leverage the political Violence Early Warning System (ViEWS) to generate subnational forecasts for intrastate armed conflict in Africa. Results show that protest data substantially improves conflict incidence and onset predictions compared to baseline models that account for conflict history. Moreover, the results underline the centrality of theory for conflict forecasting: our theoretically informed protest models outperform naive models that treat all protests equally.

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.

We review and compare conflict early warning systems on three dimensions: transparency and accessibility, key parameters, and forecasts. The review reveals a need for improved transparency and accessibility of data and code, considerable variation in key parameters across systems, and significant overlaps in countries with the highest risk. We propose that developing standards and platforms that promote transparency, accessibility, and inter-system cooperation can improve knowledge proliferation and system development to mitigate and prevent political violence.

Early warning systems purport to help decision makers act more effectively. But what does it take to develop, deliver and use warnings or predictions for rare and uncertain events in data-poor and rapidly changing environments? What key lessons can be learned from teams who have confronted these challenges over the last 10 years? The paper answers these questions using in-depth analysis of the experiences of teams developing and applying early warning systems (EWS) for coups, organised violence, mass atrocities, and child soldier recruitment. The purpose of this paper is to improve understanding of the development and use of conflict-related EWS.

Armed conflict and economic growth are inherently coupled; armed conflict substantially reduces economic growth, while economic growth is strongly correlated with a reduction in the propensity of armed conflict. Here, we simulate the incidence of armed conflict and its effect on economic growth simultaneously along the economic pathways defined by the shared socioeconomic pathways (SSPs). We argue that gross domestic product per capita projections through the 21st century currently in use are too optimistic since they disregard the harm to growth caused by conflict. Our analysis indicates that the correction required to account for this is substantial—expected income is 25% lower on average across countries when taking conflict into account. The correction is particularly strong for the more pessimistic SSP3 and SSP4 where expected future incidence of armed conflict is high. There are strong regional patterns with countries with contemporaneous conflicts experiencing much higher conflict burdens and reduced economic growth by the end of the century. The implications of this research indicate that today's most marginalized societies will be substantially more vulnerable to the impact of climate change than indicated by existing income projections.

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.