Autonomous Agents and Multi-Agent Systems

, Volume 21, Issue 2, pp 172–203 | Cite as

What the 2007 TAC Market Design Game tells us about effective auction mechanisms

  • Jinzhong Niu
  • Kai Cai
  • Simon Parsons
  • Peter McBurney
  • Enrico H. Gerding
Article

Abstract

This paper analyzes the entrants to the 2007 tac Market Design Game. We present a classification of the entries to the competition, and use this classification to compare these entries. The paper also attempts to relate market dynamics to the auction rules adopted by these entries and their adaptive strategies via a set of post-tournament experiments. Based on this analysis, the paper speculates about the design of effective auction mechanisms, both in the setting of this competition and in the more general case.

Keywords

Double auction Mechanism design Trading agent competition 

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References

  1. 1.
    Axelrod R. (2006) The evolution of cooperation. Basic Books, Persus Books Group, New YorkGoogle Scholar
  2. 2.
    Cason, T. N., & Friedman, D. (2008). A comparison of market institutions. In C. Plott & V. Smith (Eds.), Handbook of experimental economics results (Vol. 1), Chap. 33, North Holland.Google Scholar
  3. 3.
    Clearwater, S. H. (Ed.). (1996). Market-based control: A paradigm for distributed resource allocation. River Edge, NJ: World Scientific.Google Scholar
  4. 4.
    Cliff D. (2001) Evolution of market mechanism through a continuous space of auction-types. Technical report. Hewlett-Packard Research Laboratories, BristolGoogle Scholar
  5. 5.
    Cliff D., Bruten J. (1997) Minimal-intelligence agents for bargaining behaviours in market-based environments. Technical report. Hewlett-Packard Research Laboratories, BristolGoogle Scholar
  6. 6.
    Das, R., Hanson, J. E., Kephart, J. O., & Tesauro, G. (2001). Agent-human interactions in the continuous double auction. In Proceedings of the seventeenth international joint conference on artificial intelligence. Seattle, USA.Google Scholar
  7. 7.
    Erev I., Roth A. E. (1998) Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria. The American Economic Review 88(4): 848–881Google Scholar
  8. 8.
    Friedman, D. (1993). The double auction institution: A survey. In [10], Chap. 1, (pp. 3–25).Google Scholar
  9. 9.
    Friedman, D., & Rich C. (2008). The matching market institution. In Handbook of experimental economics results (Vol. 1), Chap. 13, North Holland.Google Scholar
  10. 10.
    Friedman, D., & Rust, J. (Eds.). (1993). The double auction market: Institutions, theories and evidence. Santa Fe Institute Studies in the Sciences of Complexity. Cambridge, MA: Westview Press, Perseus Books Group.Google Scholar
  11. 11.
    Fudenberg D., Levine D. K. (1998) The theory of learning in games. MIT, Cambridge, MAMATHGoogle Scholar
  12. 12.
    Gerding, E., McBurney, P., Niu, J., Parsons, S., & Phelps, S. (2007). Overview of CAT: A market design competition, version 1.1, Technical Report ULCS-07-006. Liverpool, UK: Department of Computer Science, University of Liverpool.Google Scholar
  13. 13.
    Gjerstad S., Dickhaut J. (1998) Price formation in double auctions. Games and Economic Behavior 22: 1–29MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Gode D. K., Sunder S. (1993) Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. Journal of Political Economy 101(1): 119–137CrossRefGoogle Scholar
  15. 15.
    He M., Jennings N. R., Leung H. F. (2003) On agent-mediated electronic commerce. IEEE Transactions on Knowledge and Data Engineering 15(4): 985–1003CrossRefGoogle Scholar
  16. 16.
    Jordan, P. R., & Wellman, M. P. (2007). Best-first search for approximate equilibria in empirical games. In Proceedings of AAAI-07 workshop on trading agent design and analysis (TADA-07). Canada: Vancouver.Google Scholar
  17. 17.
    Jordan, P. R., Kiekintveld, C., & Wellman, M. P. (2007). Empirical game-theoretic analysis of the TAC supply chain game. In Proceedings of the sixth international joint conference on autonomous agents and multiagent systems (pp. 1188–1195). Hawaii: Honolulu.Google Scholar
  18. 18.
    Kaisers, M., Tuyls, K., Thuijsman, F., & Parsons, S. (2008). Auction analysis by normal form game approximation. In Proceedings of the IEEE/WIC/ACM international conference on intelligent agent technology. Sydney, Australia, short paper.Google Scholar
  19. 19.
    Klemperer P. (2002) How (not) to run auctions: The European 3G telecom auctions. European Economic Review 46(4–5): 829–845CrossRefGoogle Scholar
  20. 20.
    MacKie-Mason, J. K., & Wellman, M. P. (2006). Automated markets and trading agents. In L. Tesfatsion & K. L. Judd (Eds.), Handbook of computational economics (Vol. 2, pp. 1381–1431), Amsterdam: Elsevier, Chap. 28.Google Scholar
  21. 21.
    McCabe, K. A., Rassenti, S. J., & Smith, V. L. (1993). Designing a uniform price double auction. In [10], Chap. 11, (pp. 307–332).Google Scholar
  22. 22.
    Niu, J., Cai, K., Parsons, S., & Sklar, E. (2006). Reducing price fluctuation in continuous double auctions through pricing policy and shout improvement rule. In Proceedings of the fifth international joint conference on autonomous agents and multiagent systems (pp. 1143–1150). Japan: Hakodate.Google Scholar
  23. 23.
    Niu, J., Cai, K., Parsons, S., & Sklar, E. (2007) Some preliminary results on competition between markets for automated traders. In Proceedings of AAAI-07 workshop on trading agent design and analysis (TADA-07). Canada: Vancouver.Google Scholar
  24. 24.
    Niu J., Mmoloke A., McBurney P., Parsons S. (2007) CATP specification: A communication protocol for CAT games. Technical report. Department of Computer Science Graduate Center, City University of New York, New YorkGoogle Scholar
  25. 25.
    Niu, J., Cai, K., McBurney, P., & Parsons, S. (2008) An analysis of entries in the first TAC market design competition. In Proceedings of the IEEE/WIC/ACM international conference on intelligent agent technology. Sydney, Australia.Google Scholar
  26. 26.
    Niu, J., Cai, K., Parsons, S., Gerding, E., & McBurney, P. (2008). Characterizing effective auction mechanisms: Insights from the 2007 TAC mechanism design competition. In P. Padgham & P. Müller (Eds.). Proceedings of the seventh international conference on autonomous agents and multiagent systems (pp. 1079–1086). Portugal: Estoril.Google Scholar
  27. 27.
    Niu, J., Cai, K., Parsons, S., Gerding, E., McBurney, P., & Moyaux, T., et al. (2008). JCAT: A platform for the TAC market design competition. In P. Padgham, & P. Müller (Eds.), Proceedings of the seventh international conference on autonomous agents and multiagent systems, demo paper (pp. 1649–1650). Portugal: Estoril.Google Scholar
  28. 28.
    Pardoe D., Stone P. (2005) Developing adaptive auction mechanisms. ACM SIGecom Exchanges 5(3): 1–10CrossRefGoogle Scholar
  29. 29.
    Petric, A., Podobnik, V., Grguric, A., & Zemljic, M. (2008). Designing an effective e-market: An overview of the CAT agent. In Proceedings of AAAI-08 workshop on trading agent design and analysis (TADA-08) Chicago, IL, USA.Google Scholar
  30. 30.
    Phelps, S. (2005). JASA—Java auction simulation API. http://jasa.sourceforget.net/.
  31. 31.
    Phelps, S., McBurney, P., Parsons, S., & Sklar, E. (2002). Co-evolutionary auction mechanism design: A preliminary report. In Proceedings of the workshop on agent mediated electronic commerce IV (AMEC IV).Google Scholar
  32. 32.
    Phelps, S., Parsons, S., Sklar, E., & McBurney, P. (2003). Using genetic programming to optimise pricing rules for a double auction market. In Proceedings of the workshop on agents for electronic commerce. Pittsburgh, PA.Google Scholar
  33. 33.
    Phelps, S., Marcinkiewicz, M., Parsons, S., & McBurney, P. (2005). Using population-based search and evolutionary game theory to acquire better-response strategies for the double-auction market. In Proceedings of IJCAI-05 workshop on trading agent design and analysis (TADA-05).Google Scholar
  34. 34.
    Phelps, S., Marcinkiewicz, M., Parsons, S., & McBurney, P. (2006). A novel method for automatic strategy acquisition in n-player non-zero-sum games. In Proceedings of the fifth international joint conference on autonomous agents and multi-agent systems (AAMAS’06) (pp. 705–712). New York, NY: ACM. doi: 10.1145/1160633.1160760.
  35. 35.
    Plott C. R., Smith V. L. (1978) An experimental examination of two exchange institutions. The Review of Economic Studies 45(1): 133–153CrossRefGoogle Scholar
  36. 36.
    Rust, J., Miller, J. H., & Palmer, R. G. (1993). Behaviour of trading automata in a computerized double auction market. In [10], Chap. 6, (pp. 155–199).Google Scholar
  37. 37.
    Schwartz, R. A., Byrne, J. A., & Colaninno, A. (Eds.). (2007). The New NASDAQ Marketplace. Zicklin School of Business Financial Markets Series. New York: Springer.Google Scholar
  38. 38.
    Shah, A. (1997). Competing exchanges: The international dimension. http://www.mayin.org/ajayshah/MEDIA/1997/cmarkets.html.
  39. 39.
    Smith V. L. (1962) An experimental study of competitive market behaviour. Journal of Political Economy 70(2): 111–137CrossRefGoogle Scholar
  40. 40.
    Sodomka, E., Collins, J., & Gini, M. L. (2007). Efficient statistical methods for evaluating trading agent performance. In Proceedings of the 27th conference on artificial intelligence (pp. 770–775). Canada: Vancouver.Google Scholar
  41. 41.
    Stone P., Greenwald A. (2005) The first international trading agent competition: Autonomous bidding agents. Electronic Commerce Research 5(2): 229–265. doi: 10.1007/s10660-005-6158-z MATHCrossRefGoogle Scholar
  42. 42.
    Sutton R. S., Barto A. G. (1998) Reinforcement learning: An introduction. MIT, Cambridge, MAGoogle Scholar
  43. 43.
    Vytelingum, P., Vetsikas, I. A., Shi, B., & Jennings, N. R. (2008). IAMwildCAT: The winning strategy for the TAC market design competition. In Proceedings of 18th European conference on artificial intelligence (pp. 428–434). Patras, Greece.Google Scholar
  44. 44.
    Walsh, W., Das, R., Tesauro, G., & Kephart, J. O. (2002). Analyzing complex strategic interactions in multi-agent systems. In P. Gmytrasiewicz & S. Parsons (Eds.), Proceedings of 2002 workshop on game-theoretic and decision-theoretic agents (GTDT-02). AAAI, Edmonton, Alberta, CanadaGoogle Scholar
  45. 45.
    Walsh, W. E., Parkes, D. C., & Das R. (2003). Choosing samples to compute heuristic-strategy Nash equilibrium. In AAMAS 2003 workshop on agent mediated electronic commerce. Melbourne, Australia.Google Scholar
  46. 46.
    Wellman M. P., Cheng S. F., Reeves D. M., Lochner K. M. (2003) Trading agents competing: Performance, progress, and market effectiveness. IEEE Intelligent Systems 18(6): 48–53CrossRefGoogle Scholar
  47. 47.
    Widrow, B., & Hoff, M. E. (1960). Adaptive switching circuits. In 1960 IRE western electric show and convention record (pp. 96–104).Google Scholar
  48. 48.
    Wurman P.R., Walsh W.E., Wellman M.P. (1998) Flexible double auctions for electronic commerce: Theory and implementation. Decision Support Systems 24(1): 17–27CrossRefGoogle Scholar
  49. 49.
    Wurman P. R., Wellman M. P., Walsh W. E. (2001) A parametrization of the auction design space. Games and Economic Behavior 35: 304–338MATHCrossRefMathSciNetGoogle Scholar
  50. 50.
    Zhan W., Friedman D. (2007) Markups in double auction markets. Journal of Economic Dynamics and Control 31(9): 2984–3005MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© The Author(s) 2009

Authors and Affiliations

  • Jinzhong Niu
    • 1
  • Kai Cai
    • 1
  • Simon Parsons
    • 2
  • Peter McBurney
    • 3
  • Enrico H. Gerding
    • 4
  1. 1.Department of Computer Science, Graduate CenterCity University of New YorkNew YorkUSA
  2. 2.Department of Computer and Information Science, Brooklyn CollegeCity University of New YorkBrooklynUSA
  3. 3.Department of Computer ScienceUniversity of LiverpoolLiverpoolUK
  4. 4.Department of Electronic and Computer ScienceUniversity of SouthamptonSouthamptonUK

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