The decision to engage in military
conflict is shaped by many factors, including state- and dyad-level
characteristics as well as the state’s membership in geopolitical
coalitions. Supporters of the democratic peace theory, for example,
hypothesize that the community of democratic states is less likely
to wage war with each other. Such theories explain the ways in which
nodal and dyadic characteristics affect the evolution of conflict
patterns over time via their effects on group memberships. To test
these arguments, we develop a dynamic model of network data by
combining a hidden Markov model with a mixed-membership stochastic
blockmodel that identifies latent groups underlying the network
structure. Unlike existing models, we incorporate covariates that
predict dynamic node memberships in latent groups as well as the
direct formation of edges between dyads. While prior substantive
research often assumes the decision to engage in international
militarized conflict is independent across states and static over
time, we demonstrate that conflict is driven by states’ evolving
membership in geopolitical blocs. Our analysis of militarized
disputes from 1816 to 2010 identifies two distinct blocs of
democratic states, only one of which exhibits unusually low rates of
conflict. Changes in monadic covariates like democracy shift states
between coalitions, making some states more pacific but others more
belligerent. The proposed methodology, which relies on a
variational approximation to a collapsed posterior distribution as
well as stochastic optimization for scalability, is implemented
through an
open-source software
package.