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Chaos : The organization of intrinsic computation: Complexity-entropy diagrams and the diversity of natural information processing

By David P. Feldman, Carl S. McTague, and James P. Crutchfield

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Book Id: WPLBN0002169288
Format Type: PDF eBook :
File Size: Serial Publication
Reproduction Date: 14 October 2008

Title: Chaos : The organization of intrinsic computation: Complexity-entropy diagrams and the diversity of natural information processing  
Author: David P. Feldman, Carl S. McTague, and James P. Crutchfield
Volume: Issue : October 2008
Language: English
Subject: Science, Physics, Natural Science
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Chaos Collection
Historic
Publication Date:
Publisher: American Institute of Physics

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Feldman, Carl S. Mctague, And James P. Crutchfiel, D. P. (n.d.). Chaos : The organization of intrinsic computation: Complexity-entropy diagrams and the diversity of natural information processing. Retrieved from http://www.gejl.org/


Description
Description: Intrinsic computation refers to how dynamical systems store, structure, and transform historical and spatial information. By graphing a measure of structural complexity against a measure of randomness, complexity-entropy diagrams display the different kinds of intrinsic computation across an entire class of systems. Here, we use complexity-entropy diagrams to analyze intrinsic computation in a broad array of deterministic nonlinear and linear stochastic processes, including maps of the interval, cellular automata, and Ising spin systems in one and two dimensions, Markov chains, and probabilistic minimal finite-state machines. Since complexity-entropy diagrams are a function only of observed configurations, they can be used to compare systems without reference to system coordinates or parameters. It has been known for some time that in special cases complexity-entropy diagrams reveal that high degrees of information processing are associated with phase transitions in the underlying process space, the so-called “edge of chaos.” Generally, though, complexity-entropy diagrams differ substantially in character, demonstrating a genuine diversity of distinct kinds of intrinsic computation.

 
 



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