Improving Defense Against Intelligent Adversaries

  • Louis Anthony CoxJr.
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 185)

Abstract

This is the first of four chapters devoted to public-sector applications of risk analysis and possible ways to improve them. The applications we consider are defending against attacks by terrorists or other intelligent adversaries (this chapter), assessing and promoting food safety (next chapter), and assessing the public health benefits and fairness of cleaner air (Chaps. 7 and 8). These exemplify the roles of government in providing public goods, enforcing product safety, and reducing negative externalities, respectively. Risk analysis is now used extensively in each of these areas to help allocate resources and set priorities in pursuing these roles with limited budgets.

Keywords

Expected Loss Initial Attack Game Tree Successful Attack Probabilistic Risk Assessment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Aghassi M, Bertsimas D (2006) Robust game theory. Math Program 107(1):231–273CrossRefGoogle Scholar
  2. Aumann R (1976) Agreeing to disagree. Ann Stat 4(6):1236–1239 Bier VM (2007) Choosing what to protect. Risk Anal 27(3):607–620CrossRefGoogle Scholar
  3. Bier VM, Cox LA Jr, Azaiez MN (2009) Why both game theory and reliability theory are important in defending infrastructure against intelligent attacks. Chapter 1. In: Bier VM, Azaiez MN (eds) Game theoretic risk analysis of security threats. Springer, New York, Jun;27(3):607–620CrossRefGoogle Scholar
  4. Bordley R, Hazen G (1992) Nonlinear utility models arising from unmodelled small world intercorrelations. Manag Sci 38(7):1010–1017CrossRefGoogle Scholar
  5. Brown GG, Carlyle WM, Wood RK (2008) Optimizing department of homeland security defense investments: applying defender-attacker (−defender) optimization to terror risk assessment and mitigation. Appendix E of: National Research Council, 2008, Department of homeland security bioterrorist risk assessment: a call for change. National Academies, Washington, DC. http://faculty.nps.edu/kwood/docs/applyingattackerdefenderattackertoterror.pdf
  6. Cox LA (2008) Some limitations of “Risk  =  Threat  ×  Vulnerability  ×  Consequence” for risk ­analysis of terrorist attacks. Risk Anal 28(6):1749–1761CrossRefGoogle Scholar
  7. Dresher M (1961) Games of strategy: theory and applications. Prentice-Hall, Englewood Cliffs. Republished as (1981) The mathematics of games of strategy: theory and applications. Dover Publications, New YorkGoogle Scholar
  8. Ezell B, Bennett S, von Winterfeldt D, Sokolowski J, Collins A (2010) Probabilistic risk analysis and terrorism risk. Risk Anal 30(4):575–589CrossRefGoogle Scholar
  9. Gintis H (2000) Game theory evolving: a problem-centered introduction to modeling strategic interaction. Princeton University Press, PrincetonGoogle Scholar
  10. Hall JR (2009) The elephant in the room is called game theory. Risk Anal 29(8):1061CrossRefGoogle Scholar
  11. Harsanyi J (1982) Subjective probability and the theory of games: comments on Kadane and Larkey’s paper. Manag Sci 28(2):120–124CrossRefGoogle Scholar
  12. Joyce J (1999) The foundations of causal decision theory. Cambridge University Press, Cambridge, UK, p 170CrossRefGoogle Scholar
  13. Laskey K, Lehner P (1994) Metareasoning and the problem of small worlds. IEEE Trans Syst Man Cybern 24(11):1643–1652CrossRefGoogle Scholar
  14. Luce RD, Raiffa H (1957) Games and decisions. Wiley, New YorkGoogle Scholar
  15. Shubik M (1983) The confusion of is and ought in game theoretic contexts – comment. Manag Sci 29(12):1380–1383CrossRefGoogle Scholar
  16. Tetlock P (2005) Expert political judgement: how good is it? How can we know?. Princeton University Press, Princeton. For an amusing and substantive review, see: www.newyorker.com/archive/2005/12/05/051205crbo_books1. Accessed 27 June 2010
  17. Young HP (2004) Strategic learning and its limits. Oxford University Press, New YorkCrossRefGoogle Scholar
  18. Young HP (2007) The possible and the impossible in multi-agent learning. J Artif Intell 171(7):429–433CrossRefGoogle Scholar

Copyright information

© Louis Anthony Cox, Jr 2012

Authors and Affiliations

  • Louis Anthony CoxJr.
    • 1
  1. 1.Cox AssociatesDenverUSA

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