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Open AccessArticle

Information Entropy-Based Metrics for Measuring Emergences in Artificial Societies

College of Computer, National University of Defense Technology, Changsha 410073, Hunan, China
Author to whom correspondence should be addressed.
Entropy 2014, 16(8), 4583-4602;
Received: 21 May 2014 / Revised: 30 June 2014 / Accepted: 12 August 2014 / Published: 15 August 2014
(This article belongs to the Special Issue Complex Systems and Nonlinear Dynamics)
Emergence is a common phenomenon, and it is also a general and important concept in complex dynamic systems like artificial societies. Usually, artificial societies are used for assisting in resolving several complex social issues (e.g., emergency management, intelligent transportation system) with the aid of computer science. The levels of an emergence may have an effect on decisions making, and the occurrence and degree of an emergence are generally perceived by human observers. However, due to the ambiguity and inaccuracy of human observers, to propose a quantitative method to measure emergences in artificial societies is a meaningful and challenging task. This article mainly concentrates upon three kinds of emergences in artificial societies, including emergence of attribution, emergence of behavior, and emergence of structure. Based on information entropy, three metrics have been proposed to measure emergences in a quantitative way. Meanwhile, the correctness of these metrics has been verified through three case studies (the spread of an infectious influenza, a dynamic microblog network, and a flock of birds) with several experimental simulations on the Netlogo platform. These experimental results confirm that these metrics increase with the rising degree of emergences. In addition, this article also has discussed the limitations and extended applications of these metrics. View Full-Text
Keywords: emergence; entropy; dynamic system; artificial society; information theory emergence; entropy; dynamic system; artificial society; information theory
MDPI and ACS Style

Tang, M.; Mao, X. Information Entropy-Based Metrics for Measuring Emergences in Artificial Societies. Entropy 2014, 16, 4583-4602.

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