The Hard Problem of Prediction for Conflict Prevention

• 成果类型: Article
• 署名作者: Mueller, Hannes; Rauh, Christopher
• 署名单位: Barcelona School of Economics; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Institut d'Analisi Economica (IAE); University of Cambridge
• 刊物名称: JOURNAL OF THE EUROPEAN ECONOMIC ASSOCIATION
• ISSN/ISSBN: 1542-4766
• DOI: 10.1093/jeea/jvac025
• 发表日期: 2022
• 页码: 2440-2467
• 关键词: policy; climate

摘要

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.