Why (and when) do small conflicts become big wars? We develop a Bayesian hidden Markov modeling (HMM) framework for the studying the dynamics of violence in civil wars. The key feature of an HMM for studying such a process is that an it is defined on top of a latent state space constructed to represent the domain scientist intuition for the processes being studied. To learn a latent state space of varying intensity of conflict we use count data of weekly conflict related deaths over time in a nation as an emitted response variable, and construct an autoregressive model of order 1 to describe its evolution. Using event-level data for all civil wars from 1989 to the present, this framework allows us to study transitions in the latent intensity, e.g. from escalating conflicts to stable and/or deescalating conflicts. In particular, we examine the effect of declaring a ceasefire on the underlying dynamics of conflict. Accounting for the effects of covariates for the relative degree of democracy, GDP per capita, and population in a country, we are able to quantify the uncertainty for the underlying intensity of a conflict at any given point in time.
Horn Hermansen, Gudmund; Håvard Mokleiv Nygård; Jonathan Williams; Siri Aas Rustad & Govinda Clayton (2020) The Dynamics of Civil Wars: A Bayesian hidden Markov model applied to the pattern of conflict and the role of ceasefires, presented at PolMeth XXXVII: the 2020 Annual Meeting of the Society for Political Methodology, 14-17 July 2020.