Do Ceasefires Work? A Bayesian autoregressive hidden Markov model to explore how ceasefires shape the dynamics of violence in civil war
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Despite a growing body of literature focusing on ceasefires, it is unclear if most ceasefires achieve their primary objective of stopping violence. Motivated by this question and the new availability of the ETH-PRIO Civil Conflict Ceasefires Dataset, we develop a Bayesian hidden Markov modeling (HMM) framework for studying the dynamics of violence in civil wars. 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. We find that ceasefires do indeed seem to produce a significant decline in subsequent violence. However, the pre-ceasefire period (the period typically after a ceasefire agreement has been negotiated but before it is in effect) is shown to be prone to periods of intensified violence that are most likely a cause and effect of the subsequent ceasefire. This finding has significant implications for the research and practice community. Moreover, manifesting from the ubiquity of modern data repositories combined with a deficiency in meaningful labels, HMM-based semi-supervised data labeling strategies could pave the way for the next decade of conflict research progress.
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