Analysis of micro-behavior and bounded rationality in double auction markets using co-evolutionary GP

Abstract

We investigate the dynamics of trader behaviors using a co-evolutionary genetic programming system to simulate a double-auction market. The objective of this study is twofold. First, we seek to evaluate how, if any, the difference in trader rationality/intelligence influences trading behavior. Second, besides rationality, we also analyze how, if any, the co-evolution between two learnable traders impacts their trading behaviors. We have found that traders with different degrees of rationality may exhibit different behavior depending on the type of market they are in. When the market has a profit zone to explore, the more intelligent trader demonstrate more intelligent behaviors. Also, when the market has two learnable buyers, their co-evolution produced more profitable transactions than when there was only one learnable buyer in the market. We have analyzed the learnable traders' strategies and found their behavior are very similar to humans in decision making. We will conduct human subject experiments to validate these results in the near future.

Exploring pricing rules in combinatorial sealed-bid auctions

“…13 See Gomez-Barroso et al (2011). Becker and Seshadri (2003), who described how cooperative co-evolution can be used to generate trading rules with genetic programming; more recently, Nie et al (2008), who demonstrated a co-evolutionary computation method to validate reverse multi-attribute auction settings with one buyer and many sellers and, finally, Chen et al (2009aChen et al ( , 2009b, who published two works on the dynamics of trader behaviors using a co-evolutionary genetic programming system to simulate and analyze a double auction market.…”