Abstract: The ability to model, forecast, and understand the behavioral dynamics and decision-making patterns of human agents has applications in many contexts. One particularly salient domain is the field of international security where artificial intelligence models can be leveraged to analyze complex and uncertain security situations. Real world datasets can contain 10^30,000 possible behaviors—requiring efficient techniques to manage the confluence of cultural, social, economic, political, and temporal information. In this talk, I will present a probabilistic logic formalism, the Stochastic Opponent Modeling Agents (SOMA) framework, and several scalable reasoning algorithms for modeling behavioral dynamics. SOMA has been used to study the Afghan drug trade, violent ethnopolitical conflicts in the Middle East and Asia Pacific, and the terror organization Lashkar-e-Taiba. Interpreting and using these models in national security settings requires human insight into characteristic relationships of the domain as well as computational methods such to handle the overwhelming quantity of data. I will briefly discuss the Model Analyst’s Toolkit, a software tool designed to leverage both human knowledge and computational power to refine models and aid in decision-making, and the SOMA Terror Organization Portal (STOP), a prototype system that allows users throughout the national security community to analyze the behaviors of violent organizations.