Incentives for Repair in Self-Repair Networks

  • Yoshiteru IshidaEmail author
Part of the Intelligent Systems Reference Library book series (ISRL, volume 101)


This chapter discusses when selfish agents begin to cooperate instead of defect, focusing on a specific task of self-maintenance. To consider the incentive for repair in a game theoretic framework, the Prisoner’s Dilemma is introduced in a two-nodes model for the network cleaning problem where a collection of agents capable of repairing other agents by modifying their contents can clean the collection. With this problem, cooperation corresponds to repairing other agents and defect to not repairing. Although both agents defecting is a Nash equilibrium—no agent is willing to repair others when only the repair cost is involved in the payoff—agents may cooperate with each other when system reliability is also incorporated in the payoff and with certain conditions satisfied. The incentive for cooperation will be stronger when a system-wide criterion such as availability is incorporated in the payoff.


Reliability engineering Game theory Mechanism design Nash equilibrium Prisoner’s dilemma Hamilton’s rule Kin selection Multi-agent systems Mutual repair Autonomous distributed systems 


  1. Akella, A., Seshan, S., Karp, R., Shenker, S.: Selfish behavior and stability of the Internet: a game-theoretic analysis of TCP. Comput. Commun. Rev. 32(4), 117–130 (2002)CrossRefGoogle Scholar
  2. Anderson, T., Randell, B.: Computing Systems Reliability. CUP Archive (1979)Google Scholar
  3. Axelrod, R.: The Evolution of Cooperation. Basic Books, New York, NY (1984)zbMATHGoogle Scholar
  4. Axelrod, R.: The evolution of strategies in the iterated prisoner’s dilemma. Dyn. Norms, 199–220 (1987)Google Scholar
  5. Barabási, A.L., Freeh, V.W., Jeong, H.W., Brockman, J.B.: Parasitic computing. Nature 412(6850), 894–897 (2001). doi: 10.1038/35091039 CrossRefGoogle Scholar
  6. Barlow, R.E., Proschan, F.: Statistical theory of reliability and life testing: probability models. I: DTIC Document (1975)Google Scholar
  7. Boutilier, C., Shoham, Y., Wellman, M.P.: Economic principles of multi-agent systems. Artif. Intell. 94(1–2), 1–6 (1997)CrossRefGoogle Scholar
  8. Farber, D.J., Larson, K.: The architecture of a distributed computer system-An informal description, Technical Report. University of California, Irvine, CA (11) (1970)Google Scholar
  9. Feigenbaum, J., Shenker, S.: Distributed algorithmic mechanism design: Recent results and future directions. September (2002)Google Scholar
  10. Feigenbaum J., Papadimitriou C., Sami R: A bgp-based mechanism for lowest-cost routing. In: 21st ACM Symposium on Principles of Distributed Computing, pp. 173–182. ACM Press, Monterey, CA (2002)Google Scholar
  11. Feigenbaum, J., Papadimitriou, C.H., Shenker, S.: Sharing the cost of multicast transmissions. J. Comput. Syst. Sci. 63(1), 21–41 (2001). doi: 10.1006/Jcss.2001.1754 MathSciNetCrossRefzbMATHGoogle Scholar
  12. Foster, I., Kesselman, C.: Computational grids—Invited talk. Lect. Notes Comput. Sci. 1981, 3–37 (2001). (Reprinted from The Grid: Blueprint for a new computing infrastructure, 1998)Google Scholar
  13. Foster, I., Kesselman, C., Tsudik, G., Tuecke, S.: A security architecture for computational grids. In: Proceedings of the 5th ACM Conference on Computer and Communications Security, pp. 83–92. ACM (1998)Google Scholar
  14. Foster, I., Kesselman, C.: The Grid 2: Blueprint for a new computing infrastructure. Morgan Kaufmann, (2003)Google Scholar
  15. Frank, S.A.: Foundations of Social Evolution. Princeton University Press, Princeton (1998)Google Scholar
  16. Hamilton, W.D.: The evolution of altruistic behavior. Am. Nat. 97(896), 354–356 (1963)CrossRefGoogle Scholar
  17. Hamilton, W.D.: The genetical evolution of social behaviour. I. J. Theor. Biol. 7(1), 1–16 (1964)MathSciNetCrossRefGoogle Scholar
  18. Hershberger, J., Suri, S.: Vickrey prices and shortest paths: What is an edge worth? In: Proceedings. 42nd IEEE Symposium on 2001 Foundations of Computer Science, pp 252–259, IEEE (2001)Google Scholar
  19. Hurwicz, L., Reiter, S.: Designing Economic Mechanisms. Cambridge University Press (2006)Google Scholar
  20. Ishida, Y.: A critical phenomenon in a self-repair network by mutual copying. In: Knowledge-Based Intelligent Information and Engineering Systems, pp. 86–92. Springer, Berlin (2005)Google Scholar
  21. Ishida, Y.: A game theoretic analysis on incentive for cooperation in a self-repairing network. In: Innovations and Advanced Techniques in Computer and Information Sciences and Engineering, pp. 505–510. Springer, Berlin (2007)Google Scholar
  22. Ishida, Y.: Immunity-Based Systems: A Design Perspective. Springer, New York Incorporated (2004)Google Scholar
  23. Kodialam, M., Lakshman, T.: Detecting network intrusions via sampling: a game theoretic approach. In: INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies, pp. 1880–1889. IEEE (2003)Google Scholar
  24. Koutsoupias, E., Papadimitriou, C.: Worst-case equilibria. STACS’99—16th. Ann. Symp. Theor. Aspects Comput. Sci. 1563, 404–413 (1999)Google Scholar
  25. Maskin, E.S.: Mechanism design: How to implement social goals. Am. Econ. Rev. 98(3), 567–576 (2008)CrossRefGoogle Scholar
  26. Maynard Smith, J.: Evolution and the Theory of Games. Cambridge University Press, Cambridge; New York (1982)Google Scholar
  27. Myerson, R.B.: Mechanism design. Center for Mathematical Studies in Economics and Management Science, Northwestern University, (1988)Google Scholar
  28. Myerson, R.B.: Perspectives on mechanism design in economic theory. Am. Econ. Rev. 586–603 (2008)Google Scholar
  29. Nash, J.: Non-cooperative games. Ann. Math. 54(2), 286–295 (1951)MathSciNetCrossRefzbMATHGoogle Scholar
  30. Nash, J.: Two-person cooperative games. Econometrica: J. Econometric Soc. 128–140 (1953)Google Scholar
  31. Nash, J.F.: Equilibrium points in n-person games. Proc. Natl. Acad. Sci. 36(1), 48–49 (1950b)Google Scholar
  32. Nash, J.F.: The bargaining problem. Econometrica: J. Econometric Soc. 155–162 (1950a)Google Scholar
  33. Nisan, N., Ronen, A.: Algorithmic mechanism design. In: Proceedings of the Thirty-First Annual ACM Symposium on Theory of Computing, pp. 129–140. ACM (1999)Google Scholar
  34. Nowak, M.A., May, R.M.: Evolutionary games and spatial chaos. Nature 359(6398), 826–829 (1992)CrossRefGoogle Scholar
  35. Papadimitriou, C.H.: Algorithms, games, and the internet. Automata Lang. Program, Proc. 2076, 1–3 (2001)zbMATHGoogle Scholar
  36. Parkes, D.C., Ungar, L.H.: Iterative combinatorial auctions: Theory and practice. In: Proceedings of the National Conference on Artificial Intelligence 2000, pp. 74–81. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999Google Scholar
  37. Shooman, M.L.: Probabilistic Reliability: An Engineering Approach, vol. 968. McGraw-Hill, New York (1968)Google Scholar
  38. Smith, J.M., Price, G.: The logic of animal conflict. Nature 246, 15 (1973)CrossRefGoogle Scholar
  39. Walsh, W.E., Wellman, M.P.: A market protocol for decentralized task allocation. In: Proceedings. International Conference on 1998 Multi Agent Systems, pp. 325–332. IEEE (1998)Google Scholar

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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceToyohashi University of TechnologyToyohashiJapan

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