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Probabilistic Graphical Modeling of Terrorism Threat Recognition Using Bayesian Networks and Monte Carlo Simulation


Probabilistic Graphical Modeling of Terrorism Threat Recognition Using Bayesian Networks and Monte Carlo Simulation

Abstract: 

The objective of this study was to identify and model the comprehension and decision making of law enforcement personnel with respect to terrorism-centric behaviors. In this study, 44 participants provided 1,496 judgments ranked on an 11-point Likert-type suspicion scale about individual text-based scenario components emulating real-world events encountered during routine policing. The data were analyzed to assess the influence of jurisdiction, training, experience, and terrorism familiarity on the recognition of terrorism-centric behaviors. Measurements of those factors were used to formulate a Bayesian network, and Monte Carlo simulation was performed to estimate their effect on terrorism-centric cognitive judgment and decision making, defined by the response to a wide variety of simulated terrorism-centric behaviors using text-based scenarios.

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Full Citation: 

Regens, James L., Nick Mould, Carl J. Jensen, Melissa A. Graves and David N. Edger. 2015. "Probabilistic Graphical Modeling of Terrorism Threat Recognition Using Bayesian Networks and Monte Carlo Simulation." Journal of Cognitive Engineering and Decision Making (June): 1-17. http://edm.sagepub.com/content/early/2015/06/24/1555343415592730.abstract

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