A Method to Derive Local Interaction Strategies for Improving Cooperation in Self-Organizing Systems

  • Christopher Auer
  • Patrick Wüchner
  • Hermann de Meer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5343)


To achieve a preferred global behavior of self-organizing systems, suitable local interaction strategies have to be found. In general, this is a non-trivial task. In this paper, a general method is proposed that allows to systematically derive local interaction strategies by specifying the preferred global behavior. In addition, the resulting strategies can be evaluated using Markovian analysis. Then, by applying the proposed method exemplarily to the iterated prisoner’s dilemma, we are able to systematically generate a cooperation-fostering strategy which can be shown to behave similar to the “tit for tat with forgiveness” strategy that, under certain circumstances, outperforms the well-known “tit for tat” strategy used, for instance, in BitTorrent peer-to-peer file-sharing networks.


Global Knowledge Interaction Strategy Mutual Cooperation Markovian Analysis Global Cooperation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Caro, G.D., Dorigo, M.: AntNet: a mobile agents approach to adaptive routing. Technical Report IRIDIA/97-12, Université Libre de Bruxelles, BelgiumGoogle Scholar
  2. 2.
    Cohen, B.: Incentives build robustness in BitTorrent (2003)Google Scholar
  3. 3.
    Jiang, J., Bai, H., Wang, W.: Trust and cooperation in peer-to-peer systems. In: Li, M., Sun, X.-H., Deng, Q.-n., Ni, J. (eds.) GCC 2003. LNCS, vol. 3033, pp. 371–378. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Lai, K., Feldman, M., Stoica, Chuang, J.: Incentives for cooperation in peer-to-peer networks. In: Workshop on Economics of Peer-to-Peer Systems (2003)Google Scholar
  5. 5.
    Jun, S., Ahamad, M.: Incentives in BitTorrent induce free riding. In: P2PECON 2005: Proceeding of the 2005 ACM SIGCOMM workshop on Economics of peer-to-peer systems, pp. 116–121. ACM Press, New York (2005)CrossRefGoogle Scholar
  6. 6.
    Axelrod, R.: The Evolution of Cooperation. Basic Books, Inc., Publishers, New York (1984)zbMATHGoogle Scholar
  7. 7.
    Yew Chong, S., Humble, J., Kendall, G., Li, M., Yao, X.: The Iterated Prisoner’s Dilemma: 20 Years On. In: The Iterated Prisoner’s Dilemma: 20 Years On. World Scientific, Singapore (2006)Google Scholar
  8. 8.
    Shalizi, C.R.: Causal Architecture, Complexity and Self-Organization in Time Series and Cellular Automata. PhD thesis, University of Wisconsin, Supervisor: Martin Olsson (2001)Google Scholar
  9. 9.
    Shalizi, C.R., Shalizi, K.L.: Blind construction of optimal nonlinear recursive predictors for discrete sequences. In: AUAI 2004: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, Arlington, Virginia, United States, pp. 504–511. AUAI Press (2004)Google Scholar
  10. 10.
    Perkins, C., Bhagwat, P.: Highly dynamic destination-sequenced distance-vector routing (DSDV) for mobile computers. In: ACM SIGCOMM 1994 Conference on Communications Architectures, Protocols and Applications, pp. 234–244 (1994)Google Scholar
  11. 11.
    Clausen, T., Jacquet, P.: Optimized link state routing protocol, OLSR (2003)Google Scholar
  12. 12.
    Diamantopoulos, F., Economides, A.A.: A performance study of DSDV-based CLUSTERPOW and DSDV routing algorithms for sensor network applications. In: ISWPC 2006: Proceedings of the 1st Internation Symposium on Wireless Pervasive Computing, pp. 1–6. IEEE Press, Los Alamitos (2006)CrossRefGoogle Scholar
  13. 13.
    Eugster, P.T., Guerraoui, R., Kermarrec, A.M., Massoulieacute, L.: Epidemic information dissemination in distributed systems. Computer 37(5), 60–67 (2004)CrossRefGoogle Scholar
  14. 14.
    Jelasity, M., Babaoglu, O.: T-Man: Gossip-based overlay topology management. In: Brueckner, S.A., Di Marzo Serugendo, G., Hales, D., Zambonelli, F. (eds.) ESOA 2005. LNCS, vol. 3910. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Lv, Q., Cao, P., Cohen, E., Li, K., Shenker, S.: Search and replication in unstructured peer-to-peer networks. In: ICS 2002: Proceedings of the 16th International Conference on Supercomputing, pp. 84–95. ACM, New York (2002)Google Scholar
  16. 16.
    Haas, Z.J., Halpern, J.Y., Li, L.: Gossip-based ad hoc routing. IEEE/ACM Trans. Netw. 14(3), 479–491 (2006)CrossRefGoogle Scholar
  17. 17.
    Osborne, M.J., Rubinstein, A.: A Course in Game Theory. The MIT Press, Cambridge (1994)zbMATHGoogle Scholar
  18. 18.
    Bolch, G., Greiner, S., de Meer, H., Trivedi, K.S.: Queueing Networks and Markov Chains, 2nd edn. John Wiley & Sons, Inc., Hoboken (2006)CrossRefzbMATHGoogle Scholar
  19. 19.
    Rapoport, A., Cammah, A.M.: Prisoner’s Dilemma: A Study in Conflict and Cooperation. University of Michigan Press (1965)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Christopher Auer
    • 1
  • Patrick Wüchner
    • 1
  • Hermann de Meer
    • 1
  1. 1.Faculty of Informatics and MathematicsUniversity of PassauPassauGermany

Personalised recommendations