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Nature plays with dice - terrorists do not: Allocating resources to counter strategic versus probabilistic risks


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  • Golany, Boaz
  • Kaplan, Edward H.
  • Marmur, Abraham
  • Rothblum, Uriel G.
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    Probabilistic uncertainty is caused by "chance", whereas strategic uncertainty is caused by an adverse interested party. Using linear impact functions, the problems of allocating a limited resource to defend sites that face either probabilistic risk or strategic risk are formulated as optimization problems that are solved explicitly. The resulting optimal policies differ - under probabilistic risk, the optimal policy is to focus the investment of resources on priority sites where they yield the highest impact, while under strategic risk, the best policy is to spread the resources so as to decrease the potential damage level of the most vulnerable site(s). Neither solution coincides with the commonly practiced proportionality allocation scheme.

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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 192 (2009)
    Issue (Month): 1 (January)
    Pages: 198-208

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    Handle: RePEc:eee:ejores:v:192:y:2009:i:1:p:198-208

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    Keywords: Risk analysis Game theory Resource allocation;


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    Cited by:
    1. Kaplan, Edward H., 2013. "Staffing models for covert counterterrorism agencies," Socio-Economic Planning Sciences, Elsevier, Elsevier, vol. 47(1), pages 2-8.
    2. Haphuriwat, N. & Bier, V.M., 2011. "Trade-offs between target hardening and overarching protection," European Journal of Operational Research, Elsevier, Elsevier, vol. 213(1), pages 320-328, August.
    3. Michael Brzoska & Raphael Bossong & Eric van Um, 2011. "Security Economics in the European Context: Implications of the EUSECON Project," Economics of Security Working Paper Series 58, DIW Berlin, German Institute for Economic Research.
    4. Perea, Federico & Puerto, Justo, 2013. "Revisiting a game theoretic framework for the robust railway network design against intentional attacks," European Journal of Operational Research, Elsevier, Elsevier, vol. 226(2), pages 286-292.
    5. Wenzel, Lars & Wolf, André, 2013. "Protection against major catastrophes: An economic perspective," HWWI Research Papers 137, Hamburg Institute of International Economics (HWWI).
    6. Wang, Xiaofang & Zhuang, Jun, 2011. "Balancing congestion and security in the presence of strategic applicants with private information," European Journal of Operational Research, Elsevier, Elsevier, vol. 212(1), pages 100-111, July.
    7. Hendel, Ulrich, 2012. ""Look like the innocent flower, but be the serpent under't": Mimicking behaviour of growth-oriented terrorist organizations," Discussion Papers in Economics 13998, University of Munich, Department of Economics.
    8. Meyer Sunniva F., 2011. "Preventing Mass Killings: Determining the Optimal Allocation of Security Resources between Crowded Targets," Peace Economics, Peace Science, and Public Policy, De Gruyter, De Gruyter, vol. 17(1), pages 1-37, September.
    9. Shan, Xiaojun & Zhuang, Jun, 2013. "Hybrid defensive resource allocations in the face of partially strategic attackers in a sequential defender–attacker game," European Journal of Operational Research, Elsevier, Elsevier, vol. 228(1), pages 262-272.


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