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Published in:
2007 | OriginalPaper | Chapter
Reverse Engineering an Agent-Based Hidden Markov Model for Complex Social Systems
Authors : Hung-Ching Chen, Mark Goldberg, Malik Magdon-Ismail, William A. Wallace
Published in: Intelligent Data Engineering and Automated Learning - IDEAL 2007
Publisher: Springer Berlin Heidelberg
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The power of social values that helps to shape or formulate our behavior patterns is not only inevitable, but also how we have surreptitiously responded to the hidden curriculum that derives from such social values in our decision making can be just as significant. Through a machine learning approach, we are able to discover the agent dynamics that drives the evolution of the social groups in a community. By doing so, we set up the problem by introducing an agent-based hidden Markov model, in which the acts of an agent are determined by
micro-laws
with unknown parameters. To solve the problem, we develop a multistage learning process for determining the
micro-laws
of a community based on observed set of communications between actors without the semantic contents. We present the results of extensive experiments on synthetic data as well as some results on real communities,
e.g.
, Enron email and movie newsgroups.