Asynchronous Learning in Decentralized Environments: A Game-Theoretic Approach

  • Eric J. Friedman


Many of the chapters in this book consider collectives that are cooperative; all agents work together to achieve a common goal—maximizing the “world utility function.” Often this is achieved by allowing agents to behave selfishly according to some “personal utility function,” although this utility function is explicitly imposed by the designer so is not truly “selfish.” In this chapter we consider the problems that arise when agents are truly selfish and their personal utility functions are intrinsic to their behavior. As designers we cannot directly alter these utility functions arbitrarily; all we can do is to adjust the ways in which the agents interact with each other and the system in order to achieve our own design goals. In game theory, this is the mechanism design problem, and the design goal is denoted the “social choice function” (SCF). 1


Utility Function Nash Equilibrium Congestion Control Solution Concept Forward Problem 
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.
    A. Archer and É. Tardos. Truthful mechanisms for one-parameter agents. In Proceedings of the 42nd Annual Symposium on Foundations of Computer Science, 2001.Google Scholar
  2. 2.
    R. Axelrod. The Evolution of Cooperation. Basic Books, New York, 1984.Google Scholar
  3. 3.
    A. Demers, S. Keshav, and S. Shenker. Analysis and simulation of a fair queueing algorithm. Journal of Internetworking, 1(1):3–26, January 1990.Google Scholar
  4. 4.
    J. Feigenbaum, C. Papadimitriou, R. Sami, and S. Shenker. A bgp-based mechanism for lowest-cost routing. In Proceedings of the 2002 ACM Symposium on Principles of Distributed Computing, 2002.Google Scholar
  5. 5.
    S. Floyd, M. Handley, J. Padhye, and J. Widmer. Equation-based congestion control for unicast applications. In Proc. ACM Sigcomm 2000, 2000.Google Scholar
  6. 6.
    E. Friedman. Selfish routing on data networks isnt' too bad: Genericity, tcp and ospf. Cornell University, 2002.Google Scholar
  7. 7.
    E. J. Friedman. Strategic properties of heterogeneous serial cost sharing. Mathematical Social Sciences (forthcoming), 2000.Google Scholar
  8. 8.
    E. J. Friedman and S. Shenker. Learning and implementation in the Internet. 2002. Available from
  9. 9.
    E. J. Friedman, M. Shor, S. Shenker, and B. Sopher. Asynchronous learning with limited information: An experimental analysis. 2001. Available from
  10. 10.
    D. Fudenberg and J. Tirole. Game Theory. MIT Press, 1991.Google Scholar
  11. 11.
    A. Greenwald, E. Friedman, and S. Shenker. Learning in network contexts: Experimental results from simulations. Games and Economic Behavior, 35(1):80–123, 1999.MathSciNetCrossRefGoogle Scholar
  12. 12.
    Matthew Jackson. A crash course in implementation theory. Social Choice and Welfare, 2001.Google Scholar
  13. 13.
    K. Jain and V. Vazirani. Applications of approximation algorithms to cooperative games. In Proceedings of the 33rd Annual ACM Symposium on the Theory of Computing, pages 364–72, 2001.Google Scholar
  14. 14.
    A. Mas-Colell, M. Whinston, and J. Green. Microeconomic Theory. Oxford University Press, 1995.Google Scholar
  15. 15.
    P. Milgrom and J. Roberts. Rationalizability, learning and equilibrium in games with strategic complementarities. Econometrica, 58:1255–78, 1990.MathSciNetMATHCrossRefGoogle Scholar
  16. 16.
    N. Nisan and A. Ronen. Algorithmic mechanism design. In Proceedings of the 31st Annual ACM Symposium on the Theory of Computing, pages 129–40, 1999.Google Scholar
  17. 17.
    S. Shenker. Efficient network allocations with selfish users. In P. J. B. King, I. Mitrani, and R. J. Pooley, editors, Performance ′90, pages 279–85. North-Holland, New York, 1990.Google Scholar
  18. 18.
    S. Shenker. Making greed work in networks: A game-theoretic analysis of switch service disciplines. IEEE/ACM Transactions on Networking, 3:819–31, 1995.CrossRefGoogle Scholar
  19. 19.
    L. Valiant. A theory of the learnable. In Proceedings of the Sixteenth Annual ACM Symposium on Theory of Computing, Washington, D.C., 1984.Google Scholar
  20. 20.
    H. von Stackeiberg. Marktform und Gleichgewicht. Springer-Verlag, 1934. English translation, The Theory of the Market Economy, Oxford University Press, 1952.Google Scholar

Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Eric J. Friedman
    • 1
  1. 1.School of Operations Research and Industrial EngineeringCornell UniversityIthacaUSA

Personalised recommendations