@inproceedings{bcbcef666f224c18b0222ca09ffdbe6c,
title = "A Bayesian model of game decomposition",
abstract = "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.",
keywords = "Bayesian inference, Collective behaviour, Stock prediction",
author = "Hanqing Zhao and Zengchang Qin and Weijia Liu and Tao Wan",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 30th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, IEA/AIE 2017 ; Conference date: 27-06-2017 Through 30-06-2017",
year = "2017",
doi = "10.1007/978-3-319-60042-0\_9",
language = "英语",
isbn = "9783319600413",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "82--91",
editor = "Moonis Ali and Salem Benferhat and Karim Tabia",
booktitle = "Advances in Artificial Intelligence",
address = "德国",
}