Co-evolutionary Auction Mechanism Design: A Preliminary Report

  • Steve Phelps
  • Peter McBurney
  • Simon Parsons
  • Elizabeth Sklar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2531)


Auctions can be thought of as a method for resource allocation. The economic theory behind such systems is mechanism design. Traditionally, economists have approached design problems by studying the analytic or experimental properties of different mechanisms. An alternative is to view a mechanism as the outcome of some evolutionary process involving buyers, sellers and an auctioneer, and so automatically generate not just strategies for trading, but also strategies for auctioneering. As a first step in this alternative direction, we have applied genetic programming to the development of an auction pricing rule for double auctions in a wholesale electricity marketplace.


Genetic Programming Market Power Trading Strategy Electricity Market Price Rule 
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.


  1. 1.
    P. J. Angeline and J. B. Pollack. Competitive environments evolve better solutions for complex tasks. In S. Forrest, editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 264–270, 1993.Google Scholar
  2. 2.
    A. D. Blair, E. Sklar, and P. Funes. Co-evolution, determinism and robustness. In Proceedings of the Second Asia-Pacific Conference on Simulated Evolution and Learning, pages 389–396. Springer-Verlag, 1999.Google Scholar
  3. 3.
    D. Cliff. Minimal-intelligence agents for bargaining behaviours in market environments. Technical Report HPL-97-91, HP Labs, 1997.Google Scholar
  4. 4.
    D. Cliff. Evolution of market mechanism through a continuous space of auction-types. Technical Report HPL-2001-326, HP Labs, 2001.Google Scholar
  5. 5.
    D. Cliff. Evolutionary optimization of parameter sets for adaptive software-agent traders in continuous double auction markets. Technical Report HPL-2001-99, HP Labs, 2001.Google Scholar
  6. 6.
    D. Cliff and Geoffrey F. Miller. Tracking the red queen: Measurements of adaptive progress in co-evolutionary simulations. In European Conference on Artificial Life, pages 200–218, 1995.Google Scholar
  7. 7.
    P. Cramton and J. Schwartz. Collusive bidding: Lessons from the FCC spectrum auctions. Journal of Regulatory Economics, 17:229–252, 2000.CrossRefGoogle Scholar
  8. 8.
    S. S. Fatima and M. Wooldridge. Adaptive task and resource allocation in multi-agent systems. In Proceedings of the 5th International Conference on Autonomous Agents, pages 537–544. ACM Press, 2001.Google Scholar
  9. 9.
    S. G. Ficici and J. B. Pollack. Challenges in coevolutionary learning: Arms-race dynamics, open-endedness, and mediocre stable states. In Proceedings of the 6th International Conference on Artificial Life”, pages 238–247, 1998.Google Scholar
  10. 10.
    D. Friedman and J. Rust, editors. The Double Auction Market: Institutions, Theories and Evidence. Santa Fe Institute Studies in the Sciences of Complexity. Perseus Publishing, Cambridge, MA, USA, 1993.Google Scholar
  11. 11.
    D. E. Goldberg and K. Deb. A comparative analysis of selection schemes used in genetic algorithms. In G. J. E. Rawlins, editor, Foundations of Genetic Algorithms, pages 69–93. Morgan Kaufmann, San Mateo, CA, 1991.Google Scholar
  12. 12.
    W. D. Hillis. Co-evolving parasites improve simulated evolution as an optimization procedure. In Proceedings of the Second International Conference on Artificial Life, pages 313–324, 1992.Google Scholar
  13. 13.
    J. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975.Google Scholar
  14. 14.
    M. O. Jackson. Mechanism theory. In The Encyclopedia of Life Support Systems. EOLSS Publishers, 2000.Google Scholar
  15. 15.
    N. R. Jennings, P. Faratin, A. R. Lomuscio, S. Parsons, M. Wooldridge, and C. Sierra. Automated negotiation: prospects, methods and challenges. Group Decision and Negotiation 10(2):199–215, 2001.CrossRefGoogle Scholar
  16. 16.
    P. Klemperer. How (not) to run auctions: the European 3G telecom auctions. European Economic Review (forthcoming), 2002.Google Scholar
  17. 17.
    J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992.Google Scholar
  18. 18.
    R. P. McAfee and J. McMillan. Analyzing the airwaves auction. Journal of Economic Perspectives, 10:159–176, 1996.Google Scholar
  19. 19.
    P. Milgrom. Auctions and bidding: A primer. Journal of Economic Perspectives, 3:3–22, 1989.Google Scholar
  20. 20.
    G. F. Miller and D. Cliff. Protean behavior in dynamic games: Arguments for the co-evolution of pursuit-evasion tactics. In Proceedings of the Third International Conference on Simulation of Adaptive Behavior, pages 411–420, 1994.Google Scholar
  21. 21.
    David J. Montana. Strongly typed genetic programming. Technical Report #7866, Bolt Beranek and Newman, 10 Moulton Street, Cambridge, MA 02138, USA, July 1993.Google Scholar
  22. 22.
    J. Nicolaisen, V. Petrov, and L. Tesfatsion. Market power and efficiency in a computational electricity market with discriminatory double-auction pricing. IEEE Transactions on Evolutionary Computation, 5(5):504–523, October 2001.CrossRefGoogle Scholar
  23. 23.
    J. B. Pollack and A. D. Blair. Co-evolution in the successful learning of backgammon strategy. Machine Learning, 32:225–240, 1998.zbMATHCrossRefGoogle Scholar
  24. 24.
    T. C. Price. Using co-evolutionary programming to simulate strategic behaviour in markets. Journal of Evolutionary Economics, 7:219–254, 1997.CrossRefGoogle Scholar
  25. 25.
    A. E. Roth. The economist as engineer: Game theory, experimentation, and computation as tools for design economics. Econometrica, 70:1341–1378, 2002.zbMATHCrossRefGoogle Scholar
  26. 26.
    A. E. Roth and I. Erev. Learning in extensive form games: experimental data and simple dynamic models in the intermediate term. Games and Economic Behavior, 8:164–212, 1995.zbMATHCrossRefMathSciNetGoogle Scholar
  27. 27.
    J. Rust, J. Miller, and R. Palmer. Behavior of trading automata in a computerized double auction market. In D. Friedman and J. Rust, editors, The Double Auction Market: Institutions, Theories and Evidence, Santa Fe Institute Studies in the Sciences of Complexity, pages 155–198. Perseus Publishing, Cambridge, MA., 1993.Google Scholar
  28. 28.
    T. W. Sandholm. Distributed rational decision making. In G. Weiss, editor, Multi-agent Systems: A Modern Introduction to Distributed Artificial Intelligence, pages 201–258. MIT Press, Cambridge, MA, USA, 1999.Google Scholar
  29. 29.
    M. A. Satterthwaite and S. R. Williams. The bayesian theory of the k-double auction. In D. Friedman and J. Rust, editors, The Double Auction Market: Institutions, Theories and Evidence, Santa Fe Institute Studies in the Sciences of Complexity, pages 99–123. Perseus Publishing, Cambridge, MA., 1993.Google Scholar
  30. 30.
    G. Tesauro and J. O. Kephart. Pricing in agent economies using multi-agent Qlearning. Autonomous Agents and Multi-Agent Systems, 5(3):289–304, 2002.CrossRefGoogle Scholar
  31. 31.
    R. A. Watson and J. B. Pollack. Symbiotic combination as an alternative to sexual recombination in genetic algorithms. In 6th International Conference on Parallel Problem Solving from Nature. Springer Verlag, 16–20 2000.Google Scholar
  32. 32.
    P. R. Wurman, W. E. Walsh, and M.P. Wellman. Flexible double auctions for electronic commerce: theory and implementation. Decision Support Systems, 24:17–27, 1998.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Steve Phelps
    • 1
  • Peter McBurney
    • 1
  • Simon Parsons
    • 1
    • 2
    • 3
  • Elizabeth Sklar
    • 4
  1. 1.Department of Computer ScienceUniversity of LiverpoolLiverpoolUK
  2. 2.Center for Coordination Science, Sloan School of ManagementM.I.T.CambridgeUSA
  3. 3.Department of Computer and Information ScienceBrooklyn CollegeNew YorkUSA
  4. 4.Department of Computer ScienceColumbia UniversityNew YorkUSA

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