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The Hard Problem of Prediction for Conflict Prevention

Hannes Mueller and Christopher Rauh ()

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

Abstract: In this article we propose a framework to tackle conflict prevention, an issue which has received interest in several policy areas. A key challenge of conflict forecasting for prevention is that outbreaks of conflict in previously peaceful countries are rare events and therefore hard to predict. To make progress in this hard problem, this project summarizes more than four million newspaper articles using a topic model. The topics are then fed into a random forest to predict conflict risk, which is then integrated into a simple static framework in which a decision maker decides on the optimal number of interventions to minimize the total cost of conflict and intervention. According to the stylized model, cost savings compared to not intervening pre-conflict are over US$1 trillion even with relatively ineffective interventions, and US$13 trillion with effective interventions.

JEL-codes: C53 C55 F21 (search for similar items in EconPapers)
Date: 2021-01-06
New Economics Papers: this item is included in nep-big and nep-for
Note: cr542
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https://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe2103.pdf

Related works:
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
Working Paper: The Hard Problem of Prediction for Conflict Prevention (2019) Downloads
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