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The Hard Problem of Prediction for Conflict Prevention

Hannes Felix Mueller and Christopher Rauh ()

No 13748, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: There is a growing interest in better conflict prevention and this provides a strong motivation for better conflict forecasting. A key problem of conflict forecasting for prevention is that predicting the start of conflict in previously peaceful countries is extremely hard. To make progress in this hard problem this project exploits both supervised and unsupervised machine learning. Specifically, the latent Dirichlet allocation (LDA) model is used for feature extraction from 3.8 million newspaper articles and these features are then used in a random forest model to predict conflict. We find that forecasting hard cases is possible and benefits from supervised learning despite the small sample size. Several topics are negatively associated with the outbreak of conflict and these gain importance when predicting hard onsets. The trees in the random forest use the topics in lower nodes where they are evaluated conditionally on conflict history, which allows the random forest to adapt to the hard problem and provides useful forecasts for prevention.

Keywords: Armed Conflict; Forecasting; Machine Learning; Newspaper Text; Random Forest; Topic Models (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cmp and nep-for
Date: 2019-05
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