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More "normal" than normal: scaling distributions and complex systems

Willinger, Walter and Alderson, David and Doyle, John C. and Li, Lun (2004) More "normal" than normal: scaling distributions and complex systems. In: Proceedings of the 2004 Winter Simulation Conference. Vol.1. IEEE Press , Piscataway, NJ, pp. 130-141. ISBN 0-7803-8786-4.

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One feature of many naturally occurring or engineered complex systems is tremendous variability in event sizes. To account for it, the behavior of these systems is often described using power law relationships or scaling distributions, which tend to be viewed as "exotic" because of their unusual properties (e.g., infinite moments). An alternate view is based on mathematical, statistical, and data-analytic arguments and suggests that scaling distributions should be viewed as "more normal than normal". In support of this latter view that has been advocated by Mandelbrot for the last 40 years, we review in this paper some relevant results from probability theory and illustrate a powerful statistical approach for deciding whether the variability associated with observed event sizes is consistent with an underlying Gaussian-type (finite variance) or scaling-type (infinite variance) distribution. We contrast this approach with traditional model fitting techniques and discuss its implications for future modeling of complex systems.

Item Type:Book Section
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Doyle, John C.0000-0002-1828-2486
Additional Information:© 2004 by the Winter Simulation Conference Board of Directors
Record Number:CaltechAUTHORS:WILwsc04
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Official Citation:Willinger, W., Alderson, D., Doyle, J.C., Li, L. More "normal" than normal: scaling distributions and complex systems. In Proceedings of the 2004 Winter Simulation Conference (ed. R. G. Ingalls, M. D. Rossetti, J. S. Smith, B. A. Peters)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:1045
Deposited By: Archive Administrator
Deposited On:09 Dec 2005
Last Modified:26 Nov 2019 11:15

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