Abstract
In this paper, we examined the conditions under which evolutionary algorithms (EAs) are appropriate for artificial market models. We constructed three types of agents, which are different in efficiency and accuracy of learning. They were compared using acquired payoff in a minority game, a simplified model of a financial market. As a result, when the dynamics of the financial price was complex to some degree, an EA-like learning type was appropriate for the modeling of financial markets. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Izumi, K., Yamashita, T., & Kurumatani, K. (2005). Analysis of learning types in an artificial market. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3415 LNAI, pp. 145–158). Springer Verlag. https://doi.org/10.1007/978-3-540-32243-6_12
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