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Over the past decade, the field of conflict forecasting has undergone a remarkable metamorphosis, transforming from a series of isolated efforts with low predictive power into large, globe-spanning projects with impressive performance. However, despite this evolution, many challenges still remain. First, while we are good at predicting absolute risks, we are poor at predicting conflict dynamics (onsets, escalations, de-escalations and terminations). Second, we are over-reliant on spatio-temporal features and mechanistic models due to the nature of the event-data we use, thus excluding actor agency. Third, we do not handle either data or model uncertainty. Fourth, we are lagging behind the state-of-the-art in machine-learning. This dissertation attempts to resolve some of these salient difficulties, by contributing to six core elements of current-generation forecasting systems. First, time, by looking at the substantive effects and uncertainties of the temporal distance between data and forecast horizons. Second, space, by looking at the inherent uncertainties of high-resolution geospatial data and proposing a statistical method to address this. Third, feature space, by tackling the extreme feature sparsity in event-data and proposing a novel, deep active learning approach to mine features from existing large conflict-related text corpora. Fourth, substantive knowledge, by combining findings from the previous papers to take a fresh look at the microdynamics of conflict escalation. Fifth, the forecasting process itself, by building models that directly forecast from text, eliminating the intermediate step of manual data curation. Finally, the frontier of event-data, by looking at whether the news-media heavy way we collect violent fatal events can be extended to the collection of non-violent events. Methodologically, the dissertation introduces state-of-the art methods to the field, including the use of large language models, Gaussian processes, active learning and deep time series modelling. The six papers in the dissertation exhibit significant performance improvement, especially in forecasting dynamics.

The proliferation of large-scale, geographically disaggregated data on armed conflicts, protests, and similar events has opened new avenues of research, but has also introduced significant data quality challenges. A notable yet often overlooked issue involves observations with “known geographic imprecision” (KGI), where event locations are unknown and instead arbitrarily assigned by dataset authors. Although this issue is widely recognized and accounts for up to a quarter of observations in datasets like UCDP GED, it is rarely addressed by users. This paper presents a stochastic method derived from the multiple-imputation literature, employing spatio-temporal Gaussian processes and leveraging latent actor-conflict features in the data to enhance location accuracy. Extensive Monte-Carlo simulations demonstrate that this approach substantially enhances the accuracy of these observations and improves predictive performance beyond the state-of-the-art when applied out-of-sample. Additionally, an adapted version of the UCDP GED dataset that employs this new procedure is provided, showcasing the practical application and benefits of the methodology.

Can we predict civil war? This article sheds light on this question by evaluating 9 years of, at the time, future predictions made by Hegre et al. (2013) in 2011. We evaluate the ability of this study to predict observed conflicts in the 2010–2018 period, using multiple metrics. We also evaluate the original performance evaluation, i.e., whether the performance measures presented by Hegre et al. hold in this new 9-year window. Overall, we conclude that Hegre et al. were able to produce meaningful and reasonably accurate predictions of armed conflict. Of course, they did not always hit the mark. We find that the model has performed worse in predicting low level incidence of conflict than in predicting major armed conflict. The model also failed to predict some important broader regional shifts. These, however, represent important insights for future research and illustrate the utility in predictive models for both testing and developing theory.

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 a new, monthly updated dataset on organized violence—the Uppsala Conflict Data Program Candidate Events Dataset. It contains recent observations of candidate events, a majority of which are eventually included in the Uppsala Conflict Data Program Georeferenced Event Dataset as part of its annual update after a careful vetting process. We describe the definitions, sources and procedures employed to code the candidate events, and a set of issues that emerge when coding data on organized violence in near-real time. Together, the Uppsala Conflict Data Program Candidate and Georeferenced Event Datasets minimize an inherent trade-off between update speed and quality control. Having monthly updated conflict data is advantageous for users needing near-real time monitoring of violent situations and aiming to anticipate future developments. To demonstrate this, we show that including them in a conflict forecasting system yields distinct improvements in terms of predictive performance: Average precision increases by 20–40% relative to using the Uppsala Conflict Data Program Georeferenced Event Dataset only. We also show that to ensure quality and consistency, revisiting the initial coding making use of sources that become available later is absolutely necessary.

Several studies show a beneficial effect of peacekeeping operations (PKOs). However, by looking at individual effect pathways (intensity, duration, recurrence, diffusion) in isolation, they underestimate the peacekeeping impact of PKOs. We propose a novel method of evaluating the combined impact across all pathways based on a statistical model of the efficacy of UN PKOs in preventing the onset, escalation, continuation, and recurrence of internal armed conflict. We run a set of simulations based on the statistical estimates to assess the impact of alternative UN policies for the 2001-13 period. If the UN had invested US$200 billion in PKOs with strong mandates, major armed conflict would have been reduced by up to two-thirds relative to a scenario without PKOs and 150,000 lives would have been saved over the 13-year period compared to a no-PKO scenario. UN peacekeeping is clearly a cost-effective way of increasing global security.