Bounded Rationality and Market Micro-Behaviors: Case Studies Based on Agent-Based Double Auction Markets

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

  author =       "Shu-Heng Chen and Ren-Jie Zeng and Tina Yu and 
                 Shu G. Wang",
  title =        "Bounded Rationality and Market Micro-Behaviors: Case
                 Studies Based on Agent-Based Double Auction Markets",
  booktitle =    "Multi-Agent Applications with Evolutionary Computation
                 and Biologically Inspired Technologies: Intelligent
                 Techniques for Ubiquity and Optimization",
  publisher =    "IGI Global",
  year =         "2010",
  editor =       "Shu-Heng Chen and Yasushi Kambayashi and 
                 Hiroshi Sato",
  chapter =      "5",
  pages =        "78--94",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "9781605668987",
  DOI =          "DOI:10.4018/978-1-60566-898-7.ch005",
  size =         "17 pages",
  abstract =     "We investigate the dynamics of trader behaviours using
                 an agent-based genetic programming system to simulate
                 double-auction markets. The objective of this study is
                 two-fold. First, we seek to evaluate how, if any, the
                 difference in trader rationality/intelligence
                 influences trading behaviour. Second, besides
                 rationality, we also analyse 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 demonstrates 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
                 trading strategies and found the learning behaviors are
                 very similar to humans in decision-making. We plan to
                 conduct human subject experiments to validate these
                 results in the near future.",
  notes =        "Shu-Heng Chen (National Chengchi University, Taiwan),
                 Ren-Jie Zeng (Taiwan Institute of Economic Research,
                 Taiwan), Tina Yu (Memorial University of Newfoundland,
                 Canada) and Shu G. Wang (National Chengchi University,

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