Modeling and Controlling Interstate Conflict

TL;DR

This paper employs Bayesian neural networks and control theory to model and identify key factors influencing interstate conflict, proposing methods to manipulate variables for peace outcomes.

Contribution

It introduces a novel application of Bayesian neural networks combined with control theory to predict and control interstate conflict based on multiple input factors.

Findings

Using all controllable variables prevents conflict.

Dependency alone can avoid conflict.

Capabilities alone can prevent conflict.

Abstract

Bayesian neural networks were used to model the relationship between input parameters, Democracy, Allies, Contingency, Distance, Capability, Dependency and Major Power, and the output parameter which is either peace or conflict. The automatic relevance determination was used to rank the importance of input variables. Control theory approach was used to identify input variables that would give a peaceful outcome. It was found that using all four controllable variables Democracy, Allies, Capability and Dependency; or using only Dependency or only Capabilities avoids all the predicted conflicts.