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Quantitative risk‐based analysis for military counterterrorism systems

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  • Edouard Kujawski
  • Gregory A. Miller

Abstract

This paper presents a realistic and practical approach to quantitatively assess the risk‐reduction capabilities of military counterterrorism systems in terms of damage cost and casualty figures. The comparison of alternatives is thereby based on absolute quantities rather than an aggregated utility or value provided by multicriteria decision analysis methods. The key elements of the approach are (1) the use of decision‐attack event trees for modeling and analyzing scenarios, (2) a portfolio model approach for analyzing multiple threats, and (3) the quantitative probabilistic risk assessment matrix for communicating the results. Decision‐attack event trees are especially appropriate for modeling and analyzing terrorist attacks where the sequence of events and outcomes are time‐sensitive. The actions of the attackers and the defenders are modeled as decisions and the outcomes are modeled as probabilistic events. The quantitative probabilistic risk assessment matrix provides information about the range of the possible outcomes while retaining the simplicity of the classic safety risk assessment matrix based on Mil‐Std‐882D. It therefore provides a simple and reliable tool for comparing alternatives on the basis of risk including confidence levels rather than single point estimates. This additional valuable information requires minimal additional effort. The proposed approach is illustrated using a simplified but realistic model of a destroyer operating in inland restricted waters. The complex problem of choosing a robust counterterrorism protection system against multiple terrorist threats is analyzed by introducing a surrogate multi‐threat portfolio. The associated risk profile provides a practical approach for assessing the robustness of different counterterrorism systems against plausible terrorist threats. The paper documents the analysis for a hypothetical case of three potential threats. © 2007 Wiley Periodicals, Inc. Syst Eng 10: 273–289, 2007

Suggested Citation

  • Edouard Kujawski & Gregory A. Miller, 2007. "Quantitative risk‐based analysis for military counterterrorism systems," Systems Engineering, John Wiley & Sons, vol. 10(4), pages 273-289, December.
    • Handle: RePEc:wly:syseng:v:10:y:2007:i:4:p:273-289
    DOI: 10.1002/sys.20075
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    References listed on IDEAS

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    1. Edouard Kujawski & Mariana L. Alvaro & William R. Edwards, 2004. "Incorporating psychological influences in probabilistic cost analysis," Systems Engineering, John Wiley & Sons, vol. 7(3), pages 195-216.
    2. Edouard Kujawski, 2002. "Selection of technical risk responses for efficient contingencies," Systems Engineering, John Wiley & Sons, vol. 5(3), pages 194-212.
    3. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    4. Stanley Kaplan, 1981. "On The Method of Discrete Probability Distributions in Risk and Reliability Calculations–Application to Seismic Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 1(3), pages 189-196, September.
    5. Stanley Kaplan & B. John Garrick, 1981. "On The Quantitative Definition of Risk," Risk Analysis, John Wiley & Sons, vol. 1(1), pages 11-27, March.
    6. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    7. Robin L. Dillon & Richard John & Detlof von Winterfeldt, 2002. "Assessment of Cost Uncertainties for Large Technology Projects: A Methodology and an Application," Interfaces, INFORMS, vol. 32(4), pages 52-66, August.
    8. Harry F. Martz & Mark E. Johnson, 1987. "Risk Analysis of Terrorist Attacks," Risk Analysis, John Wiley & Sons, vol. 7(1), pages 35-47, March.
    9. Yossi Sheffi, 2005. "The Resilient Enterprise: Overcoming Vulnerability for Competitive Advantage," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262693496, April.
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    Cited by:

    1. Edouard Kujawski, 2016. "A Probabilistic Game‐Theoretic Method to Assess Deterrence and Defense Benefits of Security Systems," Systems Engineering, John Wiley & Sons, vol. 19(6), pages 549-566, November.

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