Title :
Co-evolutionary multi-agent-based modeling of artificial stock market by using the GP approach
Author_Institution :
Graduate Sch. of Econ., Kyushu Univ., Fukuoka, Japan
Abstract :
The paper deals with a multi-agent-based architecture for an artificial stock market. We attempt to add more heterogeneity to agents. Specifically, in this architecture rational agents prefer forecast equation models or simple trading rules to support their decision making, and own their own individual base or just learn from a public base. Besides these rational agents, a type of irrational agent is also defined. We focus on applying the GP approach to model cognitive behavior of adaptive agents. Time series generated from this multi-agent-based artificial stock market are demonstrated to replicate some features and compared with empirical studies.
Keywords :
cognitive systems; forecasting theory; genetic algorithms; multi-agent systems; stock markets; time series; GP approach; adaptive agents; agent heterogeneity; artificial stock market; co-evolutionary multi-agent-based modeling; cognitive behavior modelling; decision making; forecast equation models; genetic programming; irrational agents; multi-agent-based architecture; multi-agent-based artificial stock market; multi-agent-based modeling; public base; rational agents; simple trading rules; time series; Artificial intelligence; Computational modeling; Decision making; Economic forecasting; Equations; Genetic programming; Power generation economics; Power system modeling; Predictive models; Stock markets;
Conference_Titel :
Computational Intelligence for Financial Engineering, 2003. Proceedings. 2003 IEEE International Conference on
Print_ISBN :
0-7803-7654-4
DOI :
10.1109/CIFER.2003.1196256
