| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP13748 |
| DP13748 The Hard Problem of Prediction for Conflict Prevention | |
| Hannes Mueller; Christopher Rauh | |
| 发表日期 | 2019-05-22 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | 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. |
| 主题 | Development Economics |
| 关键词 | Armed conflict Forecasting Newspaper text Machine learning Topic models Random forest |
| URL | https://cepr.org/publications/dp13748 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/542613 |
| 推荐引用方式 GB/T 7714 | Hannes Mueller,Christopher Rauh. DP13748 The Hard Problem of Prediction for Conflict Prevention. 2019. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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