The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia

• 成果类型: Article
• 署名作者: Bazzi, Samuel; Blair, Robert A.; Blattman, Christopher; Dube, Oeindrila; Gudgeon, Matthew; Peck, Richard
• 署名单位: Boston University; Brown University; University of Chicago; United States Military Academy; United States Department of Defense; United States Army; Northwestern University
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_01016
• 发表日期: 2022-07
• 页码: 764-779
• 关键词: civil conflict; armed conflict; LESSONS; FUTURE; shocks; MODEL

摘要

How feasible is violence early-warning prediction? Colombia and Indonesia have unusually fine-grained data. We assemble two decades of local violent events alongside hundreds of annual risk factors. We attempt to predict violence one year ahead with a range of machine learning techniques. Our models reliably identify persistent, high-violence hot spots. Violence is not simply autoregressive, as detailed histories of disaggregated violence perform best, but socioeconomic data substitute well for these histories. Even with unusually rich data, however, our models poorly predict new outbreaks or escalations of violence. These best-case scenarios with annual data fall short of workable early-warning systems.