Causal Models and Exploratory Analysis in Heterogeneous Information Fusion for Detecting Potential Terrorists
Nov 17, 2015
Published in: Proceedings of the 2015 Winter Simulation Conference / L. Yilmaz, W.K.V. Chan, I. Man, T.M.K. Roeder, C. Macal, M.D. Rossett, eds
Posted on RAND.org on December 14, 2015
We describe basic research that uses a causal, uncertainty-sensitive computational model rooted in qualitative social science to fuse disparate pieces of threat information. It is a cognitive model going beyond rational-actor methods. Having such a model has proven useful when information is uncertain, fragmentary, indirect, soft, conflicting, and even deceptive. Inferences from fusion must then account for uncertainties about the model, the credibility of information, and the fusion methods--i.e. we must consider both structural and parametric uncertainties, including uncertainties about the uncertainties. We use a novel combination of (1) probabilistic and parametric methods, (2) alternative models and model structures, and (3) alternative fusion methods that include nonlinear algebraic combination, variants of Bayesian inference, and a new entropy-maximizing approach. Initial results are encouraging and suggest that such an analytically flexible and model-based approach to fusion can simultaneously enrich thinking, enhance threat detection, and reduce harmful false alarms.