Chaos: Making a New Science

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Chaos: Making a New Science
Chaos Gleick OReB.jpg
20th-anniversary edition
Author James Gleick
LanguageEnglish
Genre Popular science
Publisher Viking Books
Publication date
October 29, 1987
Publication placeUnited States
Media typePrint, e-book
Pages400 pp.
ISBN 0-7493-8606-1
OCLC 59649776
LC Class Q172.5.C45 G54 1987
Followed byNature's Chaos 

Chaos: Making a New Science is a debut non-fiction book by James Gleick that initially introduced the principles and early development of the chaos theory to the public. [1] It was a finalist for the National Book Award [2] and the Pulitzer Prize [3] in 1987, and was shortlisted for the Science Book Prize in 1989. [4] The book was published on October 29, 1987 by Viking Books.

Contents

Overview

Chaos: Making a New Science was the first popular book about chaos theory. It describes the Mandelbrot set, Julia sets, and Lorenz attractors without using complicated mathematics. It portrays the efforts of dozens of scientists whose separate work contributed to the developing field. The text remains in print and is widely used as an introduction to the topic for the mathematical layperson. The book approaches the history of chaos theory chronologically, starting with Edward Norton Lorenz and the butterfly effect, through Mitchell Feigenbaum, and ending with more modern applications.

The book covers chaos theory under the lens of four themes: sensitive dependence on initial conditions, self-similarity, universality, and nonlinearity. [5]

An enhanced ebook edition was released by Open Road Media in 2011, adding embedded video and hyperlinked notes. [6]

Reception

Robert Sapolsky said, "Chaos is the first book since Baby Beluga where I've gotten to the last page and immediately started reading it over again from the front: I've found this to be the most influential book in my thinking about science since college." [7]

Freeman Dyson praised the book for its popular account but critiqued the omitting of the earlier work of Dame Mary L. Cartwright and J. E. Littlewood in forming the foundation of chaos theory. [8]

Jont Allen published a paper in an IEEE journal in which he explains the source of Chaos in some detail. Allen, Jont (2024). Chaotic convergence of Newton's method, IEEE Transactions on Signal Processing, March, (doi: 10.1109/TSP.2024.3376088) pdf

https://jontalle.web.engr.illinois.edu/Public/Allen-Newton-Apr9.24.pdf

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Chaos theory is an interdisciplinary area of scientific study and branch of mathematics. It focuses on underlying patterns and deterministic laws of dynamical systems that are highly sensitive to initial conditions. These were once thought to have completely random states of disorder and irregularities. Chaos theory states that within the apparent randomness of chaotic complex systems, there are underlying patterns, interconnection, constant feedback loops, repetition, self-similarity, fractals and self-organization. The butterfly effect, an underlying principle of chaos, describes how a small change in one state of a deterministic nonlinear system can result in large differences in a later state. A metaphor for this behavior is that a butterfly flapping its wings in Brazil can cause a tornado in Texas.

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References

  1. "Chaos Theory: A Brief Introduction". Archived from the original on August 5, 2013.
  2. "National Book Awards – 1987". Chaos: Making a New Science. National Book Foundation. Retrieved 28 May 2011.
  3. "1988 Finalists". Chaos:Making a new Science. The Pulitzer Prizes . Retrieved 28 May 2011.
  4. "Royal Society Prize for Science Books. Shortlisted Entries". Chaos. The Royal Society . Retrieved 3 June 2011.
  5. Lewis, Michael (1989). "Review of Chaos: Making a New Science". Human Development. 32 (3/4): 241–244. ISSN   0018-716X. JSTOR   26767401.
  6. Maynard, Andrew (9 April 2011). "James Gleick's Chaos – the enhanced edition". Review. 2020 Science. Retrieved 18 August 2011.
  7. Introduction to Human Behavioral Biology on YouTube
  8. Frenkel, Karen A. (1 February 2007). "Why Aren't More Women Physicists?". Scientific American . 296 (2): 90–92. Bibcode:2007SciAm.296b..90F. doi:10.1038/scientificamerican0207-90 . Retrieved 11 July 2017.

Further reading