Abstract
Illicit nuclear trafficking networks are a national security threat. These networks can directly lead to nuclear proliferation, as state or non-state actors attempt to identify and acquire nuclear weapons-related expertise, technologies, components, and materials. The ability to characterize and anticipate the key nodes, transit routes, and exchange mechanisms associated with these networks is essential to influence, disrupt, interdict or destroy the function of the networks and their processes. The complexities inherent to the characterization and anticipation of illicit nuclear trafficking networks requires that a variety of modeling and knowledge technologies be jointly harnessed to construct an effective analytical and decision making workflow in which specific case studies can be built in reasonable time and with realistic effort. In this paper, we explore a solution to this challenge that integrates evidentiary and dynamic modeling with knowledge management and analytical gaming, and demonstrate its application to a geopolitical region at risk.
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References
International Atomic Energy Agency, Illicit Trafficking Database, Fact Sheet: January 1993–December 2006 (2009)
Arlt, R., Brutscher, J., Gunnink, R., Ivanov, V., Parnham, K., Soldner, S., Stein, J.: Use of CdZnTe detectors in hand-held and portable isotope identifiers to detect illicit trafficking of nuclear material and radioactive sources. In: Nuclear Science Symposium Conference Record, vol. 1, pp. 4/18–4/23. IEEE, Los Alamitos (2000)
Nedialko, B.D., Gonzalez, M.A., Michalopoulos, D.P., Morton, D.P., Nehme, M.V., Popova, E., Schneider, E.A., Thoreson, G.G.: Interdiction Modeling For Smuggled Nuclear Material. In: Proceedings of the 49th Annual Meeting of the Institute of Nuclear Materials Management (July 2008)
McDonald, J., Coursey, B., Carter, M.: Detecting Illicit Radioactive Sources. Physics Today, 36–41 (November 2004)
Peterson, D., Sanfilippo, A., Baddeley, B., Franklin, L.: Proactive Intelligence for Nuclear Nonproliferation. In: Proceedings of the Annual Meeting of the ISA’s 49th Annual Convention, San Francisco, CA, USA, March 26 (2008), http://www.allacademic.com
Coles, G.A., Brothers, A.J., Olson, J., Whitney, P.D.: Assessing State Nuclear Weapons Proliferation: Using Bayesian Network Analysis of Social Factors. In: Conference Proceedings from Pacific Northwest International Conference on Global Nuclear Security - The Decade Ahead (2010)
Sanfilippo, A.P., Cowell, A.J., Malone, E.L., Riensche, R.M., Thomas, J.J., Unwin, S.D., Whitney, P.D., Wong, P.C.: Technosocial Predictive Analytics in Support of Naturalistic Decision Making. In: Proceedings of The 9th Bi-annual International Conference on Naturalistic Decision Making, Covent Garden, London, UK, June 23-26 (2009)
Technosocial Predictive Analytics Initiative website, http://predictiveanalytics.pnl.gov
Whitney, P.D., Walsh, S.J.: Calibrating Bayesian Network Representations of Social-Behavioral Models. In: Social Computing, Behavior Modeling and Prediction, pp. 338–345
Whitney, P., White, A., Walsh, S., Dalton, A., Brothers, A.: Bayesian Networks for Social Modeling. In: Proceedings of SBP-11 (2011)
Walsh, S., Dalton, A., White, A., Whitney, P.: Parameterizing Bayesian Network Representations of Social-Behavioral Models by Expert Elicitation: A Novel Application of Conjoint Analysis. In: Proceedings of Workshop on Current Issues in Predictive Approaches to Intelligent and Security Analytics, pp. 227–232. IEEE Press, Los Alamitos (2010)
Jarman, K.D., Brothers, A.J., Whitney, P.D., Young, J., Niesen, D.A.: Integrating System Dynamics and Bayesian Networks with Application to Counter-IED Scenarios. In: 10th International Probabilistic Safety Assessment & Management Conference (2010)
Cowell, A.J., Gregory, M.L., Marshall, E.J., McGrath, L.R.: Knowledge Encapsulation Framework for Collaborative Social Modeling. In: AAAI Spring Symposium on Technosocial Predictive Analytics. AAAI Press, Menlo Park (2009)
Cowell, A.J., Gregory, M.L., Marshall, E.J., McGrath, L.: Knowledge Encapsulation and Collaboration. In: LP Pipeline for Special Track on Applied Natural Language Processing at 22th International FLAIRS 2009 (2009)
Riensche, R.M., Paulson, P.R., Danielson, G.R., Unwin, S.D., Butner, R.S., Miller, S.M., Franklin, L., Zuljevic, N.: Serious Gaming for Predictive Analytics. In: AAAI Spring Symposium on Technosocial Predictive Analytics. Association for the Advancement of Artificial Intelligence (AAAI), San Jose (2009)
Sanfilippo, A.P., Riensche, R.M., Unwin, S.D., Amaya, J.P.: Bridging the Gap between Human Judgment and Automated Reasoning in Predictive Analytics. In: International Probabilistic Safety Assessment & Management Conference, Seattle, WA (2010)
Sanfilippo, A., Cowell, A.J., Tratz, S., Boek, A., Cowell, A.K., Posse, C., Pouchard, L.: Content Analysis for Proactive Intelligence: Marshaling Frame Evidence. In: Proceedings of the 2007 AAAI Conference (2007)
South Africa: Nuclear Case Closed? National Security Archive. 1993-12-19 (1993), http://www.gwu.edu/~nsarchiv/NSAEBB/NSAEBB181/sa34.pdf (retrieved 11/15/2010)
Jarman, K.D., Walsh, S.J., Whitney, P.D.: Parameter Estimation for Agent Based Models using Sequential Monte Carlo Methods. PNNL-SA-75391, Pacific Northwest National Laboratory, Richland, WA (2010)
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Sanfilippo, A. et al. (2011). Technosocial Predictive Analytics for Illicit Nuclear Trafficking. In: Salerno, J., Yang, S.J., Nau, D., Chai, SK. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2011. Lecture Notes in Computer Science, vol 6589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19656-0_51
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DOI: https://doi.org/10.1007/978-3-642-19656-0_51
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