Abstract
The minority game (MG) is a simple model for understanding collective behavior of agents competing for a limited resource. In our previous work, we assumed that collective data can be generated from combination of behaviors of variant groups of agents and proposed the minority game data mining (MGDM) model. In this paper, to further explore collective behaviors, we propose a new behavior learning model called Evolutionary Mixed-games Learning (EMGL) model, based on evolutionary optimization of mixed-games, which assumes there are variant groups of agents playing majority games as well as the minority games. Genetic Algorithms then are used to optimize group parameters to approximate the decomposition of the original system and use them to predict the outcomes of the next round. In experimental studies, we apply the EMGL model to real-world time-series data analysis by testing on a few stocks from Chinese stock market and the USD-RMB exchange rate. The results suggest that the EMGL model can predict statistically better than the MGDM model for most of the cases and both models perform significantly better than a random guess.
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References
Mantegna, R., Stanley, H.: An Introduction to Econophysics: Correlations and Complexity in Finance. Cambridge University Press, Cambridge (1999)
Gode, D., Sunder, S.: Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. Journal of Political Economy 101(1), 119–137 (1993)
Johnson, N., Jefferies, P., Hui, P.: Financial Market Complexity. Oxford University Press, Oxford (2003)
Qin, Z.: Market mechanism designs with heterogeneous trading agents. In: Proceedings of Fifth International Conference on Machine Learning and Applications (ICMLA), Orlando, Florida, USA, pp. 69–74 (2006)
Challet, D., Zhang, Y.: Emergence of cooperation in an evolutionary game. Physica A: Statistical and Theoretical Physics 246(3-4), 407–418 (1997)
Li, G., Ma, Y., Dong, Y., Qin, Z.: Behavior learning in minority games. In: Guttmann, C., Dignum, F., Georgeff, M. (eds.) CARE 2009/2010. LNCS (LNAI), vol. 6066, pp. 125–136. Springer, Heidelberg (2011)
Ma, Y., Li, G., Dong, Y., Qin, Z.: Minority Game Data Mining for Stock Market Predictions. In: Cao, L., Bazzan, A.L.C., Gorodetsky, V., Mitkas, P.A., Weiss, G., Yu, P.S. (eds.) ADMI 2010. LNCS, vol. 5980, pp. 178–189. Springer, Heidelberg (2010)
Gou, C.: Dynamic Behaviors of Mix-game Models and Its Application (2005), http://arxiv.org/abs/physics/0504001
Gou, C.: Agents Play Mix-game. In: Econophysics of Stock and Other Markets. LNCS, Part II, pp. 123–132 (2006)
Savit, R., Koelle, K., Treynor, W., Gonzalez, R.: In: Tumer, Wolpert (eds.) Collectives and the Design of Complex System, pp. 199–212. Springer, Heidelberg (2004)
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Du, Y., Dong, Y., Qin, Z., Wan, T. (2011). Exploring Market Behaviors with Evolutionary Mixed-Games Learning Model. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2011. Lecture Notes in Computer Science(), vol 6922. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23935-9_24
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DOI: https://doi.org/10.1007/978-3-642-23935-9_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23934-2
Online ISBN: 978-3-642-23935-9
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