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.
No 329, Computing in Economics and Finance 2000 from Society for Computational Economics