  The risk of conflict is exasperated by a multitude of internal and external
factors. Current multivariate analysis paints diverse causal risk profiles that
vary with time. However, these profiles evolve and a universal model to
understand that evolution remains absent. Most of the current conflict analysis
is data-driven and conducted at the individual country or region level, often
in isolation. Consistent consideration of multi-scale interactions and their
non-linear dynamics is missing. Here, we develop a multiplexed network model,
where each city is modelled as a non-linear bi-stable system with stable states
in either war or peace. The causal factor categories which exasperate the risk
of conflict are each modelled as a network layer. We consider 3 layers: (1)
core geospatial network of interacting cities reflecting ground level
interactions, (2) cultural network of interacting countries reflecting cultural
groupings, and (3) political network of interacting countries reflecting
alliances. Together, they act as drivers to push cities towards or pull cities
away from war. Using a variety of data sources relative to 2002-2016, we show,
that our model correctly predicts the transitions from war to peace and peace
to war with F1 score of 0.78 to 0.92 worldwide at the city scale resolution. As
many conflicts during this period are auto-regressive (e.g. the War on Terror
in Afghanistan and Iraq, the Narco War across the Americas), we can predict the
emergence of new war or new peace. We demonstrate successful predictions across
a wide range of conflict genres and we perform causal discovery by identifying
which model component led to the correct prediction. In the cases of Somalia
(2008-13), Myanmar (2013-15), Colombia (2011-14), Libya (2014-16), and Yemen
(2011-13) we identify the set of most likely causal factors and how it may
differ across a country and change over time.
