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law enforcement users will be able to seamlessly and securely communicate over whatever local point of access is the best fit at any specific location, time, and situation. The second major theme dealt with being able to filter, prioritize, and make sense out of all the new data sent over this network. A common concern was the danger of information overload and how to manage and curate information to make it most useful for various areas of law enforcement, ranging from officers in the field to operations centers and public safety answering points. Specific needs in support of these themes included architectural development, developing guidance for agencies on acquiring, managing, and using new technologies, and conducting research and development on a range of technologies related to bringing about the future hybrid networks and information prioritization.

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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. INTRODUCTION PurposeThis paper illustrates how we have used a computational version of an originally qualitative socialscience model for basic research on heterogeneous information fusion bearing on detection of potential terrorists. The term "heterogeneous" highlights the diverse character of the information being fused-e.g, behavioral observations in an airport, prior-arrest records, and reports from agents of varied quality and reliability. The information is often qualitative, soft, conflicting, and even deceptive. The model assists in using such diverse and fragmentary information to piece together an estimate of the threat of terrorism posed by the individual. With respect to modeling theory, the paper illustrates the potential value of causal social-science models, assuming that they are used with proper respect for both structural and parametric uncertainties. The context is assisting uncertain inference about threat, rather than making point predictions or issuing firm judgments. Such fusion necessarily includes considerable subjectivity and analytic artistry, but it can be given structure and rigor, and it can include extensive and useful uncertainty analysis. Such improved fusion methods could increase the probability of detecting the rare potential terrorist, decrease false alarms, and increase the probability of exonerating individuals who might otherwise be falsely assessed. Future work will determine how much can be achieved. 2586 978-1-4673-9743-8/15/$31.00 ©2015 IEEE