Social Network Modeling and Agent-Based Simulation in Support of Crisis De-Escalation

Decision makers need capabilities to quickly model and effectively assess consequences of actions and reactions in crisis de-escalation environments. The creation and what-if exercising of such models has traditionally had onerous resource requirements. This research demonstrates fast and viable ways to build such models in operational environments. Through social network extraction from texts, network analytics to identify key actors, and then simulation to assess alternative interventions, advisors can support practicing and execution of crisis de-escalation activities. We describe how we used this approach as part of a scenario-driven modeling effort. We demonstrate the strength of moving from data to models and the advantages of data-driven simulation, which allow for iterative refinement. We conclude with a discussion of the limitations of this approach and anticipated future work.