Assessing different Bayesian neural network models for militarized interstate dispute - Outcomes and variable influences
This article develops and compares two Bayesian neural network models, a more restrictive Bayesian framework using Gaussian approximation and a less restrictive one using a hybrid version of Markov Chain Monte Carlo method (HMC), for the prediction of militarized interstate disputes (MIDs). In addition, to compare and analyze different Bayesian models for international conflict, the authors introduce a new measurement to interpret the relative influence of the model variables on the MIDs. The results indicate that the Gaussian approximation and HMC models are not statistically different in their performance. However HMC correctly recognized a marginally higher number of militarized disputes whose classification is important for policy purpose. On the variable effect, both models indicate similar patter of influences, where the two key liberal variables, democracy and economic interdependence, produce a strong dynamic feedback loop among each other, which greatly increases or decreases the probability of MIDs.
bayesian neural network militarized interstate dispute conflict analysis