Forecasting effects of MISO actions: An ABM methodology

Abstract

Agent-based models (ABM) have been used successfully in the field of generative social science to discover parsimonious sets of factors that generate social behavior. This methodology provides an avenue to explore the spread of anti-government sentiment in populations and to compare the effects of potential Military Information Support Operations (MISO) actions. We develop an ABM to investigate factors that affect the growth of rebel uprisings in a notional population. Our ABM expands the civil violence model developed by Epstein by enabling communication between agents through a genetic algorithm and by adding the ability of agents to form friendships based on shared beliefs. We examine the distribution of opinion and size of sub-populations of rebel and imprisoned civilians, and compare two counter-propaganda strategies. Analysis identifies several factors with effects that can explain some real-world observations, and provides a methodology for MISO operators to compare the effectiveness of potential actions.