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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2014: Advances in Knowledge Discovery and Data Mining pp 284–295Cite as

A Graphical Model for Collective Behavior Learning Using Minority Games

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8444))

Abstract

The Minority Game (MG) is a simple game theory model for the collective behavior of agents in an idealized situation where they compete for some finite resource. In this paper, we assume that collective behavior is determined by the aggregation of individual actions of agents. This causal relation between collective behavior and individual actions is investigated. A graphical model is proposed to model the generative process of collective behavior using a group of agents whose actions are modeled by minority games. In this model, we can infer the individual behavior of the agents by training on the global information, and then make predictions about the future collective behavior. Experimental results on a set of stock indexes from the Chinese market and foreign exchange (FX) rates show that the new proposed model can effectively capture the rises and falls of market and be significantly better than a random predictor. This framework also provides a new data mining paradigm for analyzing collective data by modeling micro-level actions of agents using game theory models.

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Author information

Authors and Affiliations

  1. Intelligent Computing and Machine Learning Lab School of ASEE, Beihang University, Beijing, China

    Farhan Khawar & Zengchang Qin

Authors
  1. Farhan Khawar
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  2. Zengchang Qin
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Editor information

Editors and Affiliations

  1. National Cheng Kung University, Tainan, Taiwan, R.O.C.

    Vincent S. Tseng  & Hung-Yu Kao  & 

  2. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Tu Bao Ho

  3. Nanjing University, China

    Zhi-Hua Zhou

  4. National Chengchi University, Taipei, Taiwan, R.O.C.

    Arbee L. P. Chen

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© 2014 Springer International Publishing Switzerland

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Khawar, F., Qin, Z. (2014). A Graphical Model for Collective Behavior Learning Using Minority Games. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8444. Springer, Cham. https://doi.org/10.1007/978-3-319-06605-9_24

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  • DOI: https://doi.org/10.1007/978-3-319-06605-9_24

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06604-2

  • Online ISBN: 978-3-319-06605-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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