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Complexity, Entropy and the Physics of Information
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TLDR
In this article, the authors discuss the connections between quantum and classical physics, information and its transfer, computation, and their significance for the formulation of physical theories, but also consider the origins and evolution of the information-processing entities, their complexity, and the manner in which they analyze their perceptions to form models of the Universe.Abstract:
This book has emerged from a meeting held during the week of May 29 to June 2, 1989, at St. John’s College in Santa Fe under the auspices of the Santa Fe Institute. The (approximately 40) official participants as well as equally numerous “groupies” were enticed to Santa Fe by the above “manifesto.” The book—like the “Complexity, Entropy and the Physics of Information” meeting explores not only the connections between quantum and classical physics, information and its transfer, computation, and their significance for the formulation of physical theories, but it also considers the origins and evolution of the information-processing entities, their complexity, and the manner in which they analyze their perceptions to form models of the Universe. As a result, the contributions can be divided into distinct sections only with some difficulty. Indeed, I regard this degree of overlapping as a measure of the success of the meeting. It signifies consensus about the important questions and on the anticipated answers: they presumably lie somewhere in the “border territory,” where information, physics, complexity, quantum, and computation all meet.read more
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Entropy, complexity, predictability, and data analysis of time series and letter sequences
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Uncertainty Relation at Finite Temperature
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Emergent biological principles and the computational properties of the universe: explaining it or explaining it away
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Journal ArticleDOI
Entropy Fit Indices: New Fit Measures for Assessing the Structure and Dimensionality of Multiple Latent Variables.
Hudson Golino,Robert Moulder,Dingjing Shi,Alexander P. Christensen,Luis Eduardo Garrido,Maria Dolores Nieto,John R. Nesselroade,Ritu Sadana,Jotheeswaran Amuthavalli Thiyagarajan,Steven M. Boker +9 more
TL;DR: Three new fit measures (termed entropy fit indices) that combines information theory, quantum information theory and structural analysis are proposed that can be estimated in complete datasets using Shannon entropy, while EFI and TEFI.vn and EFI are as accurate or more accurate than traditional fit measures when identifying the number of simulated latent factors.