Toward an Agent-Based Computational Modeling of Bargaining Strategies in Double Auction Markets with Genetic Programming

Authors:
Shu-Heng Chen
Affiliations:
-
Venue:
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Year:
2000

Citing 2
Cited 2

Genetic programming for the acquisition of double auction market strategies

Advances in genetic programming
Complete Classes of Strategies for the Classical Iterated Prisoner's Dilemma

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII

Strategic software agents in continuous double auction under dynamic environments

IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
A Generic Framework for a Combined Agent-based Market and Production Model

Computational Economics

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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]).