On Tradeoffs between Trust and Survivability Using a Game Theoretic Approach

  • Jin-Hee Cho
  • Ananthram Swami
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 358)

Abstract

Military communities in tactical networks must often maintain high group solidarity based on the trustworthiness of participating individual entities where collaboration is critical to performing team-oriented missions. Group trust is regarded as more important than trust of an individual entity since consensus among or compliance of participating entities with given protocols may significantly affect successful mission completion. This work introduces a game theoretic approach, namely Aoyagi’s game theory based on positive collusion of players. This approach improves group trust by encouraging nodes to meet unanimous compliance with a given group protocol. However, when any group member does not follow the given group protocol, they are penalized by being evicted from the system, resulting in a shorter system lifetime due to lack of available members for mission execution. Further, inspired by aspiration theory in social sciences, we adjust an expected system trust threshold level that should be maintained by all participating entities to effectively encourage benign behaviors. The results show that there exists the optimal trust threshold that can maximize group trust level while meeting required system lifetime (survivability).

Keywords

economic modeling trust network positive collusion aspiration rationality wireless mobile networks 

References

  1. 1.
    Atkinson, J.W.: Motivational determinants of risk-taking behavior. Psychological Review 64(6) part I, 359–372 (1957), available online (May 29, 2007) CrossRefGoogle Scholar
  2. 2.
    Aoyagi, M.: Collusion in dynamic Bertrand oligopoly with correlated private signals and communication. Journal of Economic Theory 102(1), 229–248 (2002)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Berman, Y.: Occupational aspirations of 545 female high school seniors. Journal of Vocational Behavior 2(2), 173–177 (1972), available online (July 27, 2004) CrossRefGoogle Scholar
  4. 4.
    Bellosta, M., Brigui, I., Kornman, S., Vanderpooten, D.: A multi-criteria model for electronic auctions. In: Proc. 2004 ACM Symposium on Applied Computing, Nicosia, Cyprus, pp. 14–17 (March 2004)Google Scholar
  5. 5.
    Ciardo, G., Fricks, R.M., Muppala, J.K., Trivedi, K.S.: SPNP Users Manual Version 6. Department Electrical Engineering. Duke University, Durham (1999)Google Scholar
  6. 6.
    Dash, R.K., Jennings, N.R., Parkes, D.C.: Computational-mechanism design: a call to arms. IEEE Intelligent Systems 18(6), 40–47 (2003)CrossRefGoogle Scholar
  7. 7.
    Diecidue, E., Ven, J.: Aspiration level, probability of success and failure, and expected utility. Int’l Economic Review 49(2), 683–700 (2008)CrossRefGoogle Scholar
  8. 8.
    Festinger, L.: Wish, expectation, and group standards as factors in influencing level of aspiration. Journal of Abnormal and Social Psychology 37(2), 184–200 (1942), available online (May 15, 2007)CrossRefGoogle Scholar
  9. 9.
    Han, Q., Arentze, T., Timmermans, H., Janssens, D., Wets, G.: An agent-based system for simulating dynamic choice-sets. In: Proc. 2008 Spring Simulation Multiconference, Ottawa, ON, Canada, pp. 26–33 (April 13-16, 2008)Google Scholar
  10. 10.
    Hoppe, F.: Success and failure. In: Rivera, D. (ed.) Field Theory as Human Science, pp. 324–422. Gardner Press, New York (1976), originally work published in 1931 Google Scholar
  11. 11.
    Klein, M., Moreno, G.A., Parkes, D.C., Plakosh, D., Seuken, S., Wallnau, K.C.: Handling interdependent values in an auction mechanism for bandwidth allocation in tactical data networks. In: Proc. 3rd Int’l Workshop on Economics of Networked Systems, Seattle, WA, pp. 73–78 (August 2008)Google Scholar
  12. 12.
    Mainland, G., Parkes, D., Welsh, M.: Decentralized, adaptive resource allocation for sensor networks. In: Proc. 2nd Symposium on Networked Systems Design and Implementation, Boston, MA, vol. 2, pp. 315–328 (May 2005)Google Scholar
  13. 13.
    Marti, S., Giuli, T., Lai, K., Baker, M.: Mitigating routing misbehavior in mobile ad hoc networks. In: Pro. 6th Annual ACM/IEEE Mobile Computing and Networking, Boston, MA, pp. 255–265 (August 2000)Google Scholar
  14. 14.
    Ng, S.K., Seah, W.K.G.: Game-theoretic approach for improving cooperation in wireless multihop networks. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics (2010) Google Scholar
  15. 15.
    Perrig, A., Tygar, J.D.: Secure Broadcast Communication in Wired and Wireless Networks. Kluwer Academic Publishers, Boston (2002)Google Scholar
  16. 16.
    Quaglia, R.J., Cobb, C.D.: Toward a theory of student aspirations. Journal of Research in Rural Education 12(3), 127–132 (1996)Google Scholar
  17. 17.
    Rue, R., Pfleeger, S.L.: Making the best use of cyber-security economic models. IEEE Security and Privacy 7(4), 52–60 (2009)CrossRefGoogle Scholar
  18. 18.
    Sahner, R.A., Trivedi, K.S., Puliafito, A.: Performance and Reliability Analysis of Computer Systems. Kluwer Academic Publishers, Boston (1996)MATHGoogle Scholar

Copyright information

© International Federation for Information Processing 2011

Authors and Affiliations

  • Jin-Hee Cho
    • 1
  • Ananthram Swami
    • 1
  1. 1.U.S. Army Research LaboratoryCommunication and Information Sciences DirectorateAdelphiUSA

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