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.
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References
Izumi, K., Ueda, K.: Phase transition in a foreign exchange market: Analysis based on an artificial market approach. IEEE Transactions on Evolutionary Computation 5, 456–470 (2001)
Chen, S.-H., Yeh, C.-H.: Evolving traders and the buisiness school with genetic programming: a new architecture of the agent-based artificial stock market. Journal of Economic Dynamics and Control 25, 363–393 (2001)
Izumi, K., Nakamura, S., Ueda, K.: Development of an artificial market model based on a field study. Information Science (in press)
Arthur, W.B.: Inductive reasoning and bounded rationality (the el farol problem). American Economic Review 84, 406 (1994)
Minority Game’s web page, http://www.unifr.ch/econophysics/
Zhang, Y.-C.: Modeling market mechanism with evolutionary games. Europhys. News 29, 51–54 (1998)
Challet, D., Zhang, Y.-C.: Emergence of cooperation and organization in an evolutionary game. Physica A 246, 407–418 (1997)
Marsili, M.: Market mechanism and expectations in minority and majority games. Physica A 299, 93–103 (2001)
Cavagna, A.: Irrelevance of memory in the minority game. Physical Review E 59, 3783–3786 (1999)
Lux, T., Marchesi, M.: Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397, 493–500 (1999)
Joshi, S., Parket, J., Bedau, M.A.: Technical trading creates a prisoner’s dilemma: Results from an agent-based model. In: Abu-Mostafa, Y.S., LeBaron, B., Lo, A.W., Weigend, A.S. (eds.) Computational Finance 1999, pp. 465–479. MIT Press, Cambridge (2000)
Izumi, K.: Complexity of agents and complexity of markets. In: Terano, T., Nishida, T., Namatame, A., Tsumoto, S., Osawa, Y., Washio, T. (eds.) New Frontiers in Artificial Intelligence, pp. 110–120. Springer, Heidelberg (2001)
LeBaron, B.: Evolution and time horizons in an agent based stock market. Macroeconomic Dynamics 5, 225–254 (2001)
Chen, S.H., Liao, C.C.: Agent-based computational modeling of the stock price-volume relation. Information Sciences (in press)
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Izumi, K., Yamashita, T., Kurumatani, K. (2005). Analysis of Learning Types in an Artificial Market. In: Davidsson, P., Logan, B., Takadama, K. (eds) Multi-Agent and Multi-Agent-Based Simulation. MABS 2004. Lecture Notes in Computer Science(), vol 3415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32243-6_12
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DOI: https://doi.org/10.1007/978-3-540-32243-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25262-7
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