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Handling Emergent Resource Use Oscillations

  • Mark Klein
  • Richard Metzler
  • Yaneer Bar-Yam
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3215)

Abstract

Business and engineering systems are increasingly being created as collections of many autonomous (human or software) agents cooperating as peers. Peer-to-peer coordination introduces, however, unique and potentially serious challenges. When there is no one ’in charge’, dysfunctions can emerge as the collective effect of locally reasonable decisions. In this paper, we consider the dysfunction wherein inefficient resource use oscillations occur due to delayed status information, and describe novel approaches, based on the selective use of misinformation, for dealing with this problem.

Keywords

Status Delay Resource Request Consumer Agent Minority Game Message Traffic 
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.

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References

  1. 1.
    Jensen, D., Lesser, V.: Social pathologies of adaptive agents. In: The proceedings of the Safe Learning Agents Workshop in the 2002 AAAI Spring Symposium, pp. 13–19. AAAI Press, Menlo Park (2002)Google Scholar
  2. 2.
    Chia, M.H., Neiman, D.E., Lesser, V.R.: Poaching and distraction in asynchronous agent activities. In: The proceedings of the Third International Conference on Multi-Agent Systems, Paris, France, pp. 88–95 (1998)Google Scholar
  3. 3.
    Hardin, G.: The Tragedy of the Commons. Science 162, 1243–1248 (1968)CrossRefGoogle Scholar
  4. 4.
    Youssefmir, M., Huberman, B.: Resource contention in multi-agent systems. In: The proceedings of the First International Conference on Multi-Agent Systems (ICMAS 1995), pp. 398–403. AAAI Press, San Francisco (1995)Google Scholar
  5. 5.
    Sterman, J.D.: Learning in and about complex systems. In: Alfred, P. (ed.) Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Mass, vol. 51 (1994)Google Scholar
  6. 6.
    Kephart, J.O., Hanson, J.E., Greenwald, A.R.: Dynamic pricing by software agents. Computer Networks: the International Journal of Distributed Informatique 32(6), 731–752 (2000)Google Scholar
  7. 7.
    Ranjan, P., et al.: Decision Making in Logistics: A Chaos Theory Based Analysis. In: The proceedings of the AAAI Spring Symposium on Diagnosis, Prognosis and Decision Making (2002)Google Scholar
  8. 8.
    Klein, M., et al.: The Dynamics of Collaborative Design: Insights From Complex Systems and Negotiation Research. Concurrent Engineering Research & Applications 11(3), 201–210 (2003)CrossRefGoogle Scholar
  9. 9.
    Pham, H., Ye, Y.: Knowledgeable Objects as Data Agents for Business Automation. In: The proceedings of the 2002 International Conference on Artificial Intelligence, pp. 1341–1347. CSREA Press (2002)Google Scholar
  10. 10.
    Hewitt, C., Jong, P.D.: Open Systems. Working Report Massachusetts Institute of Technology (1982)Google Scholar
  11. 11.
    Hogg, T.: Controlling chaos in distributed computational systems. In: SMC 1998 Conference Proceedings, pp. 632–637 (1998) (98CH36218)Google Scholar
  12. 12.
    Osborne, M.J., Rubinstein, A.: A course in game theory. MIT Press, Cambridge (1994), xv, 352zbMATHGoogle Scholar
  13. 13.
    Challet, D., Zhang, Y.-C.: Emergence of Cooperation and Organization in an Evolutionary Game. arXiv:adap-org/9708006 2(3) (1997)Google Scholar
  14. 14.
    Zhang, Y.-C.: Modeling Market Mechanism with Evolutionary Games. arXiv:cond-mat/9803308 1(25) (1998)Google Scholar
  15. 15.
    Hogg, T., Huberman, B.: Controlling chaos in distributed systems. IEEE Transactions on Systems, Man & Cybernetics 21(6), 1325–1332 (1991)CrossRefGoogle Scholar
  16. 16.
    Youssefmir, M., Huberman, B.A.: Clustered volatility in multiagent dynamics. Journal of Economic Behavior & Organization 32(1), 101–118 (1997)CrossRefGoogle Scholar
  17. 17.
    Klein, M., Bar-Yam, Y.: Handling Resource Use Oscillation in Multi-Agent Markets. In: The proceedings of the AAMAS Workshop on Agent-Mediated Electronic Commerce V, Melbourne, Australia (2003)Google Scholar
  18. 18.
    Metzler, R., Klein, M., Bar-Yam, Y.: Efficiency Through Disinformation, New England Complex Systems Institute (2004), http://www.arxiv.org/abs/cond-mat/0312266
  19. 19.
    Bar-Yam, Y.: Dynamics of complex systems. Addison-Wesley, Reading (1997), xvi, 848zbMATHGoogle Scholar
  20. 20.
    Braden, B., et al.: Recommendations on Queue Management and Congestion Avoidance in the Internet. Working Report: 2309. Network Working Group (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Mark Klein
    • 1
  • Richard Metzler
    • 2
  • Yaneer Bar-Yam
    • 2
  1. 1.Massachusetts Institute of Technology 
  2. 2.New England Complex Systems Institute 

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