Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/107661
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Type: Conference paper
Title: Understanding complex systems: using interaction as a measure of emergence
Author: Szabo, C.
Teo, Y.
Chengleput, G.
Citation: Proceedings of the Winter Simulation Conference, 2015 / Tolk, A., Diallo, S., Ryzhov, I., Yilmaz, L., Buckley, S., Miller, J. (ed./s), vol.2015-January, pp.207-218
Publisher: IEEE
Publisher Place: online
Issue Date: 2015
Series/Report no.: Winter Simulation Conference Proceedings
ISBN: 9781479974863
ISSN: 0891-7736
Conference Name: Winter Simulation Conference (WSC) (07 Dec 2014 - 10 Dec 2014 : Savanah, Georgia)
Statement of
Responsibility: 
Claudia Szabo, Yong Meng Teo, Gautam K. Chengleput
Abstract: Understanding the behavior of complex systems is becoming a crucial issue as systems grow in size, and the interconnection and geographical distribution of their components diversifies. The interaction over time of many components often leads to emergent behavior, which can be harmful to the system. Despite this, very few practical approaches for the identification of emergent behavior exist, and many are unfeasible to implement. Approaches using interaction as a measure of emergence have the potential to alleviate this problem. In this paper, we analyse absolute and relative methods that use interaction as a measure of emergence. Absolute methods compute a degree of interaction that characterizes a system state as being emergent. Relative methods compare interaction graphs of the system state with interaction graphs of systems that have been shown previously to exhibit emergence. We present these approaches and discuss their advantages and limitations using theoretical and experimental analysis.
Rights: © 2014 IEEE
RMID: 0030039015
DOI: 10.1109/WSC.2014.7019889
Appears in Collections:Computer Science publications

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