Abstract
The modern global political environment is growing increasingly complex, characterized by webs of interdependency, interaction, and conflict that are difficult to untangle. Technological expansion has led to an explosion in the information available, as well as the need for more sophisticated analysis methods. In this security and information environment, behaviors in the domain of counterterrorism and conflict can be understood as the confluence of many dynamic factors—cultural, economic, social, political, and historical—in an extremely complex system. Behavioral models and forecasts can be leveraged to manage the analytic complexity of these situations, providing intelligence analysts and policy-makers with decision support for developing security strategies. In this chapter, we develop the Stochastic Opponent Modeling Agents (SOMA) framework as a stochastic model of terror group behavior, presenting several scalable forecasting algorithms and a methodology for creating behavioral models from relational data.
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Acknowledgements
Some of the authors of this paper were funded in part by AFOSR grant FA95500610405, ARO grant W911NF0910206 and ONR grant N000140910685.
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Sliva, A., Simari, G., Martinez, V., Subrahmanian, V.S. (2013). SOMA: Stochastic Opponent Modeling Agents for Forecasting Violent Behavior. In: Subrahmanian, V. (eds) Handbook of Computational Approaches to Counterterrorism. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5311-6_6
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DOI: https://doi.org/10.1007/978-1-4614-5311-6_6
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