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

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

Shu-Heng Chen, Ren-Jie Zeng, Tina Yu, Shu G. Wang
ISBN13: 9781605668987|ISBN10: 1605668982|EISBN13: 9781605668994
DOI: 10.4018/978-1-60566-898-7.ch005
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MLA

Chen, Shu-Heng, et al. "Bounded Rationality and Market Micro-Behaviors: Case Studies Based on Agent-Based Double Auction Markets." Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization, edited by Shu-Heng Chen, et al., IGI Global, 2011, pp. 78-94. https://doi.org/10.4018/978-1-60566-898-7.ch005

APA

Chen, S., Zeng, R., Yu, T., & Wang, S. G. (2011). Bounded Rationality and Market Micro-Behaviors: Case Studies Based on Agent-Based Double Auction Markets. In S. Chen, Y. Kambayashi, & H. Sato (Eds.), Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization (pp. 78-94). IGI Global. https://doi.org/10.4018/978-1-60566-898-7.ch005

Chicago

Chen, Shu-Heng, et al. "Bounded Rationality and Market Micro-Behaviors: Case Studies Based on Agent-Based Double Auction Markets." In Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization, edited by Shu-Heng Chen, Yasushi Kambayashi, and Hiroshi Sato, 78-94. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60566-898-7.ch005

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Abstract

We investigate the dynamics of trader behaviors 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 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 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.

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