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A crucial challenge for solving problems in conflict research is inleveraging the semi-supervised nature of the data that arise. Observed responsedata such as counts of battle deaths over time indicate latent processes ofinterest such as intensity and duration of conflicts, but defining and labelinginstances of these unobserved processes requires nuance and imprecision. Theavailability of such labels, however, would make it possible to study theeffect of intervention-related predictors - such as ceasefires - directly onconflict dynamics (e.g., latent intensity) rather than through an intermediateproxy like observed counts of battle deaths. Motivated by this problem and thenew availability of the ETH-PRIO Civil Conflict Ceasefires data set, we proposea Bayesian autoregressive (AR) hidden Markov model (HMM) framework as asufficiently flexible machine learning approach for semi-supervised regimelabeling with uncertainty quantification. We motivate our approach byillustrating the way it can be used to study the role that ceasefires play inshaping conflict dynamics. This ceasefires data set is the first systematic andglobally comprehensive data on ceasefires, and our work is the first to analyzethis new data and to explore the effect of ceasefires on conflict dynamics in acomprehensive and cross-country manner.

Bayesian hidden Markov models for latent variable labeling assignments  in conflict research: application to the role ceasefires play in conflict  dynamics