Emergence: from chaos to order
Emergence: from chaos to order
An introduction to econophysics: correlations and complexity in finance
An introduction to econophysics: correlations and complexity in finance
Naive Bayes Classification Given Probability Estimation Trees
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Market Mechanism Designs with Heterogeneous Trading Agents
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Decision tree learning with fuzzy labels
Information Sciences: an International Journal
Behavior learning in minority games
CARE@AI'09/CARE@IAT'10 Proceedings of the CARE@AI 2009 and CARE@IAT 2010 international conference on Collaborative agents - research and development
Exploring market behaviors with evolutionary mixed-games learning model
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
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TheMinority Game (MG) is a simple model for understanding collective behavior of agents in an idealized situation for a finite resource. It has been regarded as an interesting complex dynamical disordered system from a statistical mechanics point of view. In previous work, we have investigated the problem of learning the agent behaviors in the minority game by assuming the existence of one "intelligent agent" who can learn from other agent behaviors. In this paper, we propose a framework called Minority Game Data Mining (MGDM), that assumes the collective data are generated from combining the behaviors of variant groups of agents following the minority games. We then apply this framework to time-series data analysis in the real-world. We test on a few stocks from the Chinese market and the US Dollar-RMB exchange rate. The experimental results suggest that the winning rate of the new model is statistically better than a random walk.