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On Bargaining Strategies In The Sfi Double Auction Tournaments: Is Genetic Programming The Answer?


  • Shu-Heng Chen

    (National Chengchi University)


While early computational studies of bargaining strategies, such as Rust, Miller and Palmer (1993, 1994) and Andrew and Prager (1996) all indicates the significance of agent-based modeling in the follow-up research, a real agent-based model of bargaining strategies in DA markets has never been taken. This paper attempts to take the fisrt step toward it.In this paper, genetic programming is employed to evolve bargaining strategies within the context of SFI double auction tournaments. We are interested in knowing that given a set of traders, each with a fixed trading strategies, can the automated trader driven by genetic programming eventually develop bargaining strategies which can outperform its competitors' strategies? To see how GP trader can survive in various environments, different sets of traders characterized by different compositions of bargaining strategies are chosen to compete with the single GP trader. To give a measure of the difficult level of the DA auction markets facing the GP trader, the program length is used to define the intelligence of chosen traders. In one experiment, the chosen traders are all naive; in another experiment, the traders are all sophisticated. Other experiments are placed in the middle of these two extremes.

Suggested Citation

  • Shu-Heng Chen, 2000. "On Bargaining Strategies In The Sfi Double Auction Tournaments: Is Genetic Programming The Answer?," Computing in Economics and Finance 2000 329, Society for Computational Economics.
  • Handle: RePEc:sce:scecf0:329

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    References listed on IDEAS

    1. Rubinstein, Ariel, 1982. "Perfect Equilibrium in a Bargaining Model," Econometrica, Econometric Society, vol. 50(1), pages 97-109, January.
    2. van Bragt, David & van Kemenade, Cees & la Poutre, Han, 2001. "The Influence of Evolutionary Selection Schemes on the Iterated Prisoner's Dilemma," Computational Economics, Springer;Society for Computational Economics, vol. 17(2-3), pages 253-263, June.
    3. Ken Binmore & Nir Vulkan, 1999. "Applying game theory to automated negotiation," Netnomics, Springer, vol. 1(1), pages 1-9, October.
    4. Martin J. Osborne & Ariel Rubinstein, 2005. "Bargaining and Markets," Levine's Bibliography 666156000000000515, UCLA Department of Economics.
    5. Roth, Alvin E & Murnighan, J Keith & Schoumaker, Francoise, 1988. "The Deadline Effect in Bargaining: Some Experimental Evidence," American Economic Review, American Economic Association, vol. 78(4), pages 806-823, September.
    6. Gale, John & Binmore, Kenneth G. & Samuelson, Larry, 1995. "Learning to be imperfect: The ultimatum game," Games and Economic Behavior, Elsevier, vol. 8(1), pages 56-90.
    7. Binmore, K. & Samuelson, L. & Gale, J., 1993. "Learning to be Imperfect: The Ultimatum Game," Working papers 9325, Wisconsin Madison - Social Systems.
    8. Enrico Gerding & David van Bragt & Han La Poutré, 2003. "Multi-Issue Negotiation Processes by Evolutionary Simulation, Validation and Social Extensions," Computational Economics, Springer;Society for Computational Economics, vol. 22(1), pages 39-63, August.
    9. Binmore, K. & Piccione, M. & Samuelson, L., 1996. "Evolutionary Stability in Alternating-Offers Bargaining Games," Working papers 9603r, Wisconsin Madison - Social Systems.
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