Divide to Defend: Collusive Security Games

  • Shahrzad Gholami
  • Bryan Wilder
  • Matthew Brown
  • Dana Thomas
  • Nicole Sintov
  • Milind Tambe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9996)

Abstract

Research on security games has focused on settings where the defender must protect against either a single adversary or multiple, independent adversaries. However, there are a variety of real-world security domains where adversaries may benefit from colluding in their actions against the defender, e.g., wildlife poaching, urban crime and drug trafficking. Given such adversary collusion may be more detrimental for the defender, she has an incentive to break up collusion by playing off the self-interest of individual adversaries. As we show in this paper, breaking up such collusion is difficult given bounded rationality of human adversaries; we therefore investigate algorithms for the defender assuming both rational and boundedly rational adversaries. The contributions of this paper include (i) collusive security games (COSGs), a model for security games involving potential collusion among adversaries, (ii) SPECTRE-R, an algorithm to solve COSGs and break collusion assuming rational adversaries, (iii) observations and analyses of adversary behavior and the underlying factors including bounded rationality, imbalanced- resource-allocation effect, coverage perception, and individualism/collectivism attitudes within COSGs with data from 700 human subjects, (iv) a learned human behavioral model that incorporates these factors to predict when collusion will occur, (v) SPECTRE-BR, an enhanced algorithm which optimizes against the learned behavior model to provide demonstrably better performing defender strategies against human subjects compared to SPECTRE-R.

Keywords

Stackelberg security game Collusion Human behavior model Amazon mechanical turk 

References

  1. 1.
    Bartilow, H.A., Eom, K.: Free traders and drug smugglers: the effects of trade openness on states’ ability to combat drug trafficking. Lat. Am. Polit. Soc. 51(2), 117–145 (2009)CrossRefGoogle Scholar
  2. 2.
    Berg, N.: Behavioral economics. 21st century economics: A reference handbook (2010)Google Scholar
  3. 3.
    Camerer, C.: Behavioral Game Theory. Princeton University Press, Princeton (2003)MATHGoogle Scholar
  4. 4.
    Fang, F., Stone, P., Tambe, M.: When security games go green: designing defender strategies to prevent poaching and illegal fishing. In: IJCAI (2015)Google Scholar
  5. 5.
    Fehr, E., Schmidt, K.M.: A theory of fairness, competition, and cooperation. Q. J. Econ. 114, 817–868 (1999)CrossRefMATHGoogle Scholar
  6. 6.
    Gonzalez, R., Wu, G.: On the shape of the probability weighting function. Cogn. Psychol. 38(1), 129–166 (1999)CrossRefGoogle Scholar
  7. 7.
    Guo, Q., An, B., Vorobeychik, Y., Tran-Thanh, L., Gan, J., Miao, C.: Coalitional security games. In: Proceedings of AAMAS, pp. 159–167 (2016)Google Scholar
  8. 8.
    Johnson, C.: America’s first consumer financial watchdog is on a leash. Cath. UL Rev. 61, 381 (2011)Google Scholar
  9. 9.
    Kahneman, D., Tversky, A.: Prospect theory: an analysis of decision under risk. Econometrica J. Econ. Soc. 47, 263–291 (1979)CrossRefMATHGoogle Scholar
  10. 10.
    Kar, D., Fang, F., Fave, F.D., Sintov, N., Tambe, M.: A game of thrones: when human behavior models compete in repeated stackelberg security games. In: AAMAS (2015)Google Scholar
  11. 11.
    Kiekintveld, C., Jain, M., Tsai, J., Pita, J., Ordóñez, F., Tambe, M.: Computing optimal randomized resource allocations for massive security games. In: AAMAS (2009)Google Scholar
  12. 12.
    Korzhyk, D., Conitzer, V., Parr, R.: Complexity of computing optimal stackelberg strategies in security resource allocation games. In: AAAI (2010)Google Scholar
  13. 13.
    Korzhyk, D., Conitzer, V., Parr, R.: Security games with multiple attacker resources. In: IJCAI Proceedings, vol. 22, p. 273 (2011)Google Scholar
  14. 14.
    McFadden, D.L.: Quantal choice analaysis: a survey. Ann. Econ. Soc. Measur. 5(4), 363–390 (1976). NBERGoogle Scholar
  15. 15.
    McKelvey, R.D., Palfrey, T.R.: Quantal response equilibria for normal form games. Games Econ. Behav. 10(1), 6–38 (1995)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Narrod, C., Tiongco, M., Scott, R.: Current and predicted trends in the production, consumption and trade of live animals and their products. Rev. Sci. Tech. Off. Int. Epiz. 30(1), 31–49 (2011)CrossRefGoogle Scholar
  17. 17.
    Nguyen, T.H., Kar, D., Brown, M., Sinha, A., Tambe, M., Jiang, A.X.: Towards a science of security games. New Frontiers of Multi-Disciplinary Research in STEAM-H (2015)Google Scholar
  18. 18.
    Nguyen, T.H., Sinha, A., Gholami, S., Plumptre, A., Joppa, L., Tambe, M., Driciru, M., Wanyama, F., Rwetsiba, A., Critchlow, R., Beale, C.: Capture: a new predictive anti-poaching tool for wildlife protection. In: AAMAS (2016)Google Scholar
  19. 19.
    Nguyen, T.H., Yang, R., Azaria, A., Kraus, S., Tambe, M.: Analyzing the effectiveness of adversary modeling in security games. In: AAAI (2013)Google Scholar
  20. 20.
    Restrepo, A.L., Guizado, Á.C.: From smugglers to warlords: twentieth century Colombian drug traffickers. Can. J. Lat. Am. Caribb. Stud. 28(55–56), 249–275 (2003)Google Scholar
  21. 21.
    Singelis, T.M., Triandis, H.C., Bhawuk, D.P., Gelfand, M.J.: Horizontal and vertical dimensions of individualism and collectivism: a theoretical and measurement refinement. Cross Cult. Res. 29(3), 240–275 (1995)CrossRefGoogle Scholar
  22. 22.
    Sivadas, E., Bruvold, N.T., Nelson, M.R.: A reduced version of the horizontal and vertical individualism and collectivism scale. J. Bus. Res. 61(1), 201 (2008)CrossRefGoogle Scholar
  23. 23.
    Tambe, M.: Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned. Cambridge University Press, New York (2011)CrossRefMATHGoogle Scholar
  24. 24.
    Triandis, H.C., Gelfand, M.J.: Converging measurement of horizontal and vertical individualism and collectivism. J. Pers. Soc. Psychol. 74(1), 118 (1998)CrossRefGoogle Scholar
  25. 25.
    Tversky, A., Kahneman, D.: Advances in prospect theory: cumulative representation of uncertainty. J. Risk Uncertainty 5(4), 297–323 (1992)CrossRefMATHGoogle Scholar
  26. 26.
    Warchol, G.L., Zupan, L.L., Clack, W.: Transnational criminality: an analysis of the illegal wildlife market in Southern Africa. Int. Crim. Justice Rev. 13(1), 1–27 (2003)CrossRefGoogle Scholar
  27. 27.
    Wyler, L.S., Sheikh, P.A.: International illegal trade in wildlife. DTIC Document (2008)Google Scholar
  28. 28.
    Yang, R.: Human adversaries in security games: integrating models of bounded rationality and fast algorithms. Ph.D. thesis, University of Southern California (2014)Google Scholar
  29. 29.
    Yin, Z., Korzhyk, D., Kiekintveld, C., Conitzer, V., Tambe, M.: Stackelberg vs. nash in security games: interchangeability, equivalence, and uniqueness. In: AAMAS (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Shahrzad Gholami
    • 1
  • Bryan Wilder
    • 1
  • Matthew Brown
    • 1
  • Dana Thomas
    • 1
  • Nicole Sintov
    • 1
  • Milind Tambe
    • 1
  1. 1.Computer Science DepartmentUniversity of Southern CaliforniaLos AngelesUSA

Personalised recommendations