Abstract
Using genetic programming, this paper proposes an agent-based computational modeling of double auction (DA) markets in the sense that a DA market is modeled as an evolving market of autonomous interacting traders (automated software agents). The specific DA market on which our modeling is based is the Santa Fe DA market ([12], [13]), which in structure, is a discrete-time version of the Arizona continuous-time experimental DA market ([14], [15]).
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Chen, SH. (2000). Toward an Agent-Based Computational Modeling of Bargaining Strategies in Double Auction Markets with Genetic Programming. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_76
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DOI: https://doi.org/10.1007/3-540-44491-2_76
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