BAYESIAN HIDDEN MARKOV MODELS FOR LATENT VARIABLE LABELING ASSIGNMENTS IN CONFLICT RESEARCH: APPLICATION TO THE ROLE CEASEFIRES PLAY IN CONFLICT DYNAMICS

• 成果类型: Article
• 署名作者: Williams, Jonathan P.; Hermansen, Gudmund H.; Strand, Havard; Clayton, Govinda; Nygard, Havard Mokleiv
• 署名单位: North Carolina State University; University of Oslo; Swiss Federal Institutes of Technology Domain; ETH Zurich; Peace Research Institute Oslo (PRIO)
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1869
• 发表日期: 2024
• 页码: 2034-2061
• 关键词: civil-war; PEACE; VIOLENCE; AGREEMENTS

摘要

A crucial challenge for solving problems in conflict research is in leveraging the semisupervised nature of the data that arise. Observed response data, such as counts of battle deaths over time, indicate latent processes of interest, such as intensity and duration of conflicts, but defining and labeling instances of these unobserved processes requires nuance and imprecision. The availability of such labels, however, would make it possible to study the effect of intervention-related predictors-such as ceasefires-directly on conflict dynamics (e.g., latent intensity) rather than through an intermediate proxy, like observed counts of battle deaths. Motivated by this problem and the new availability of the ETH-PRIO Civil Conflict Ceasefires data set, we propose a Bayesian autoregressive (AR) hidden Markov model (HMM) framework as a sufficiently flexible machine learning approach for semisupervised regime labeling with uncertainty quantification. We motivate our approach by illustrating the way it can be used to study the role that ceasefires play in shaping conflict dynamics. This ceasefires data set is the first systematic and globally comprehensive data on ceasefires, and our work is the first to analyze this new data and to explore the effect of ceasefires on conflict dynamics in a comprehensive and cross-country manner.