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

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Shu-Heng Chen and Ren-Jie Zeng and Tina Yu",
  title =        "Analysis of micro-behavior and bounded rationality in
                 double auction markets using co-evolutionary GP",
  booktitle =    "GEC '09: Proceedings of the first ACM/SIGEVO Summit on
                 Genetic and Evolutionary Computation",
  year =         "2009",
  editor =       "Lihong Xu and Erik D. Goodman and Guoliang Chen and 
                 Darrell Whitley and Yongsheng Ding",
  bibsource =    "DBLP,",
  pages =        "807--810",
  address =      "Shanghai, China",
  organisation = "SigEvo",
  URL =          "",
  DOI =          "doi:10.1145/1543834.1543948",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        jun # " 12-14",
  isbn13 =       "978-1-60558-326-6",
  keywords =     "genetic algorithms, genetic programming, Poster",
  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 two-fold. 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.",
  notes =        "Also known as \cite{DBLP:conf/gecco/ChenZY09} part of

Genetic Programming entries for Shu-Heng Chen Ren-Jie Zeng Tina Yu