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The hard problem of prediction for conflict prevention

Hannes Mueller and Christopher Rauh ()

No 2019-02, Cahiers de recherche from Universite de Montreal, Departement de sciences economiques

Abstract: There is a rising interest in conflict prevention and this interest 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 several features are negatively associated with the outbreak of conflict and these gain importance when predicting hard onsets. This is because the decision tree uses the text features 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.

Date: 2019-04
New Economics Papers: this item is included in nep-big, nep-cmp and nep-for
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Downloads: (external link)
http://hdl.handle.net/1866/21631

Related works:
Working Paper: The Hard Problem of Prediction for Conflict Prevention (2021) Downloads
Working Paper: The Hard Problem of Prediction for Conflict Prevention (2021) Downloads
Working Paper: The Hard Problem of Prediction for Conflict Prevention (2020) Downloads
Working Paper: The Hard Problem of Prediction for Conflict Prevention (2019) Downloads
Working Paper: The Hard Problem of Prediction for Conflict Prevention (2019) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:mtl:montde:2019-02

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