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Machine learningForecasting / early warning for conflict

Conflict Forecasting

Conflict forecasting is the enterprise of producing calibrated, regularly updated probabilistic predictions of where and when armed conflict will occur, to support early warning and prevention. Exemplified by operational systems such as ViEWS (Hegre et al. 2019), it combines historical conflict data and predictors at fine spatial and temporal resolution, fits and ensembles multiple models, and forecasts violence months ahead — then rigorously evaluates those forecasts against what actually happens. It differs from explanatory conflict analysis by being transparent, prospective, and judged on out-of-sample accuracy rather than on coefficients.

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Kilder

  1. Hegre, H., Allansson, M., Basedau, M., Colaresi, M., Croicu, M., Fjelde, H., et al. (2019). ViEWS: A political violence early-warning system. Journal of Peace Research, 56(2), 155–174. DOI: 10.1177/0022343319823860

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ScholarGate. (2026, June 22). Conflict Forecasting and Political Violence Early Warning. ScholarGate. https://scholargate.app/da/international-relations/conflict-forecasting

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Refereret af

ScholarGateConflict Forecasting (Conflict Forecasting and Political Violence Early Warning). Hentet 2026-06-24 fra https://scholargate.app/da/international-relations/conflict-forecasting · Datasæt: https://doi.org/10.5281/zenodo.20539026