A crucial challenge for solving problems in conflict research is in leveraging the semi-supervised 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 semi-supervised 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.

翻译：冲突研究中的一个关键挑战在于如何有效利用数据中呈现的半监督特性。例如随时间变化的战斗死亡人数等观测响应数据，暗示着冲突强度与持续时间等潜在过程，但定义并标记这些未观测过程的具体实例需要精细判断且难以做到精确无误。然而，若能获得此类标记，将使得研究停火协定等干预性预测因子对冲突动态（如潜在强度）的直接作用成为可能，从而避免通过战斗死亡观测数等中间代理变量进行间接分析。受该问题驱动，结合ETH-PRIO国内冲突停火数据集的新近可用性，我们提出一个贝叶斯自回归隐马尔可夫模型框架，将其作为具备不确定性量化的半监督状态标记机器学习方法。通过阐述该方法如何用于研究停火协定对冲突动态的影响机制，我们验证了其应用价值。该停火数据集是首个系统性、全球性的停火事件综合数据，而本研究则首次对此新数据进行分析，并以跨国家综合视角探索停火对冲突动态的效应。