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26 - Evaluating Deployed Decision Support Systems for Security: Challenges, Analysis, and Approaches

Published online by Cambridge University Press:  13 December 2017

Ali E. Abbas
Affiliation:
University of Southern California
Milind Tambe
Affiliation:
University of Southern California
Detlof von Winterfeldt
Affiliation:
University of Southern California
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Publisher: Cambridge University Press
Print publication year: 2017

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References

Baker, G. H. (2005). A vulnerability assessment methodology for critical infrastructure sites. In DHS symposium: R and D partnerships in homeland security.Google Scholar
Berejikian, J. (2002). A cognitive theory of deterrence. Journal of Peace Research, 39, 165183.Google Scholar
Bier, V. M. (2007). Choosing what to protect. Risk Analysis, 27(3), 607620.Google Scholar
Bier, V. M., Cox, J. L. A., & Azaiez, M. N. (2009). Why both game theory and reliability theory are important in defending infrastructure against intelligent attacks. In Game theoretic risk analysis and security threats, vol. 128. Springer US.CrossRefGoogle Scholar
Bier, V. M., Haphuriwat, N., Menoyo, J., Zimmerman, R., & Culpen, A. M. (2008). Optimal resource allocation for defense of targets based on differing measures of attractiveness. Risk Analysis, 28(3), 763770.CrossRefGoogle ScholarPubMed
Bland, M. J., & Kerry, S. M. (1998). Weighted comparison of means. BMJ: British Medical Journal, 316, 125129.Google Scholar
Blundell, R., & Costa-Dias, M. (2009). Alternative approaches to evaluation in empirical microeconomics. Journal of Human Resources.Google Scholar
Conitzer, V., & Sandholm, T. (2006). Computing the optimal strategy to commit to. In Proceedings of EC.Google Scholar
Cox, J. L. A. (2008). Some limitations of “risk = threat x vulnerability x consequence” for risk analysis of terrorist attacks. Risk Analysis, 28(6), 17491761.CrossRefGoogle ScholarPubMed
Cruz, F. S. (2009, August 20). Personal communication.Google Scholar
Doane, C., & DiRezno, III, J. (2012). The port resilience operational/tactical enforcement to combat terrorism model. Coast Guard Outlook 2012 Summer Edition.Google Scholar
Edmunds, T., & Wheeler, R. (2009). Setting priorities. In Maurer, S. M. (Ed.), WMD terrorism (chapter 7, pp. 191–209). Cambridge, MA: MIT Press.Google Scholar
Erev, I., Roth, A. E., Slonim, R. L., & Barron, G. (2002). Predictive value and usefulness of game theoretic models. International Journal of Forecasting, 18(3), 359368.CrossRefGoogle Scholar
Fave, F. M. D., Brown, M., Shieh, E., Zhang, C., Jiang, A. X., Rosoff, H., Tambe, M., & Sullivan, J. P. (2014). Security games in the field: Deployments on a transit system. Lecture Notes in Artificial Intelligence (LNAI).Google Scholar
Fave, F. M. D., Jiang, A. X., Yin, Z., Zhang, C., Tambe, M., Kraus, S., & Sullivan, J. (2014). Game-theoretic security patrolling with dynamic execution uncertainty and a case study on a real transit system. Journal of Artificial Intelligence Research, 50, 321367.Google Scholar
Gerson, M., & Boyars, J. (2007). The future of U.S. deterrence: Constructing effective strategies to deter state and non-state actors. www.cna.org/documents/D0017171.A2.pdf.Google Scholar
Goldberg, L. R., Kercheval, A. N., & Lee, K. (2005). T-statistics for weighted means in credit risk modeling. Journal of Risk Finance, 6(4), 349365.Google Scholar
Jacobson, S. H., Karnai, T., & Kobza, J. E. (2005). Assessing the impact of deterrence on aviation checked baggage screening strategies. International Journal of Risk Assessment and Management, 5(1), 115.Google Scholar
Jiang, A. X., Yin, Z., Zhang, C., Tambe, M., & Kraus, S. (2013). Game-theoretic randomization for security patrolling with dynamic execution uncertainty. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).Google Scholar
Jose, V. R. R., & Zhuang, J. (2013). Technology adoption, accumulation, and competition in multi-period attacker-defender games. Military Operations Research, 18(2), 3347.CrossRefGoogle Scholar
Kearns, M., & Ortiz, L. E. (2003). Algorithms for interdependent security games. In Neural information processing systems (NIPS).Google Scholar
Kiekintveld, C., Jain, M., Tsai, J., Pita, J., Ordóñez, F., & Tambe, M. (2009). Computing optimal randomized resource allocations for massive security games. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).Google Scholar
Kiekintveld, C., Marecki, J., & Tambe, M. (2011). Approximation methods for infinite Bayesian Stackelberg games: Modeling distributional payoff uncertainty. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).CrossRefGoogle Scholar
Kiekintveld, C., & Wellman, M. (2008). Selecting strategies using empirical game models: An experimental analysis of meta-strategies. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).Google Scholar
Kou, Y., Lu, C., Sinvongwattana, S., &Huang, Y. (2004). Survey of fraud detection techniques. In Proceedings of IEEE Networking.Google Scholar
Lazarick, R. (1999). Airport vulnerability assessment – a methodology evaluation. In Proceedings of 33rd IEEE International Carnahan Conference on Security Technology.Google Scholar
McKelvey, R. D., & Palfrey, T. R. (1995). Quantal response equilibria for normal form games. Games and Economic Behavior, 10(1), 638.Google Scholar
Murr, , A. (2007, September). Random checks. In Newsweek National News. www.newsweek.com/id/43401.Google Scholar
Paruchuri, P., Pearce, J. P., Marecki, J., Tambe, M., Ordóñez, F., & Kraus, S. (2008). Playing games with security: An efficient exact algorithm for Bayesian Stackelberg games. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).Google Scholar
Pita, J., Jain, M., Tambe, M., Ordóñez, F., Kraus, S., & Magori-Cohen, R. (2009). Effective solutions for real-world Stackelberg games: When agents must deal with human uncertainties. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).Google Scholar
Pita, J., Jain, M., Western, C., Portway, C., Tambe, M., Ordóñez, F., Kraus, S., & Paruchuri, P. (2008). Deployed ARMOR protection: The application of a game theoretic model for security at the Los Angeles International Airport. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).Google Scholar
Rogers, B. W., Palfrey, T. R., & Camerer., C. F. (2009). Heterogeneous quantal response equilibrium and cognitive hierarchies. Journal of Economic Theory, 144, 14401467.Google Scholar
Scerri, P., Goten, T. V., Fudge, J., Owens, S., & Sycara, K. (2008). Transitioning multiagent technology to UAV applications. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (Industry Track).Google Scholar
Shan, X., & Zhuang, J. (2013). Hybrid defensive resource allocations in the face of partially strategic attackers in a sequential defender-attacker game. European Journal of Operational Research, 228(1), 262272.CrossRefGoogle Scholar
Shieh, E., An, B., Yang, R., Tambe, M., Baldwin, C., DiRenzo, J., Maule, B., & Meyer, G. (2012). PROTECT: A deployed game theoretic system to protect the ports of the United States. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).Google Scholar
Stevens, D., Hamilton, T., Schaffer, M., Dunham-Scott, D. Medby, J. J., Chan, E. W., Gibson, J., Eisman, M., Mesic, R., Kelly, C. T., Kim, J., LaTourrette, T., & Riley, K. J. (2009). Implementing security improvement options at Los Angeles International Airport. www.rand.org/pubs/documented_briefings/2006/RAND_DB499-1.pdf.Google Scholar
Taquechel, E. F. (2010, March). Validation of rational deterrence theory: Analysis of U.S. government and adversary risk propensity and relative emphasis on gain or loss. Master’s thesis, Naval Postgraduate School.Google Scholar
Tengs, T. O., & Graham, J. D. (1996). Risks, costs, and lives saved: Getting better results from regulation. In The opportunity costs of haphazard social investments in lifesaving. Washington, DC: American Enterprise Institute.Google Scholar
Tsai, J., Rathi, S., Kiekintveld, C., Ordóñez, F., & Tambe, M. (2009). IRIS: Tools for strategic security allocation in transportation networks. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (Industry Track).Google Scholar
T. U. S. G. A. O. GAO. (2009, January). Aviation security: Federal air marshal service has taken actions to fulfill its core mission and address workforce issues, but additional actions are needed to improve workforce survey. GAO-09-273.Google Scholar
Weibull, J. (2004). Testing game theory. In Huck, S. (Ed.), Advances in understanding strategic behavior: Game theory, experiments and bounded rationality (pp. 85104). Palgrave MacMillan.CrossRefGoogle Scholar
Yang, R., Kiekintveld, C., Ordóñez, F., Tambe, M., & John, R. (2011). Improving resource allocation strategy against human adversaries in security games. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI) (pp. 458464).Google Scholar
Yang, R., Tambe, M., & Ordóñez, F. (2012). Computing optimal strategy against quantal response in security games. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).Google Scholar
Yin, Z., & Tambe, M. (2012). A unified method for handling discrete and continuous uncertainty in Bayesian Stackelberg games. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).Google Scholar

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