In this paper, we propose a Bayesian probabilistic model to describe collective behavior generated by a finite number of agents competing for limited resources. In this model, the strategy for each agent is a binary choice in the Minority Game and it can be modeled by a Binomial distribution with a Beta prior. The strategy of an agent can be learned given a sequence of historical choices by using Bayesian inference. Aggregated micro-level choices constitute the observable time series data in macro-level, therefore, this can be regarded as a machine learning model for time series prediction. To verify the effectiveness of the new model, we conduct a series of experiments on artificial data and real-world stock price data. Experimental results demonstrate the new proposed model has a better performance comparing to a genetic algorithm based decomposition model.
CITATION STYLE
Zhao, H., Qin, Z., Liu, W., & Wan, T. (2017). A Bayesian model of game decomposition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10350 LNCS, pp. 82–91). Springer Verlag. https://doi.org/10.1007/978-3-319-60042-0_9
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