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Two lessons from fractals and chaos

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Date Issued:
Title: Two lessons from fractals and chaos.
Name(s): Liebovitch, Larry S., creator
Scheurle, Daniela, creator
Type of Resource: text
Genre: Article
Issuance: single unit
Date Issued: 2000
Publisher: John Wiley & Sons, Inc.
Language(s): English
Identifier: 165936 (digitool), FADT165936 (IID), fau:2615 (fedora), 10.1002/1099-0526(200003/04)5:4<34::AID-CPLX5>3.0.CO;2-3 (doi)
FAU Department/College: Department of Psychology Charles E. Schmidt College of Science
Note(s): The authors maintain that we used to think that a good measurement is characterized by its mean and variance and that a good theory is characterized by its ability to predict the values measured in an experiment. The properties of nonlinear systems called fractals and chaos have now taught us that this isn’t necessarily true. Data from fractal systems extend over many scales and so cannot be characterized by a single characteristic average number. Data from chaotic systems do not repeat the same time series of values, even if they are started very close to the same initial conditions. This means that a valid mathematical model will not be able to predict the values of the time series.
This manuscript is a version of an article published in Complexity v. 5, no. 4 (2000) p. 34-43
Subject(s): Fractals
Nonlinear systems
Mathematical models
Chaotic behavior in systems
Persistent Link to This Record:
Restrictions on Access: ©2000 John Wiley & Sons, Inc.
Host Institution: FAU

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