Exploitation of Ambiguous Cues to Infer Terrorist Activity

To aid intelligence analysts in processing ambiguous data regarding nuclear terrorism threats, we develop a methodology that captures and accounts for the uncertainty in new information and incorporates prior beliefs on likely nuclear terrorist activity. This methodology can guide the analyst when making difficult decisions regarding what data are most critical to examine and what threats require greater attention. Our methodology is based on a Bayesian statistical approach that incorporates ambiguous cues to update prior beliefs of adversary activity. We characterize the general process of a nuclear terrorist attack on the United States and describe, using a simplified example, how this can be represented by an event tree. We then define hypothetical cues for the example and give notional strengths to each cue. We also perform sensitivity analysis and show how cue strengths can affect inference. The method can be used to help support decisions regarding resource allocation and interdiction.