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A New Kind of Science 1st Edition

3.0 out of 5 stars 378 customer reviews
ISBN-13: 978-1579550080
ISBN-10: 1579550088
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Editorial Reviews

Amazon.com Review

Physics and computer science genius Stephen Wolfram, whose Mathematica computer language launched a multimillion-dollar company, now sets his sights on a more daunting goal: understanding the universe. Wolfram lets the world see his work in A New Kind of Science, a gorgeous, 1,280-page tome more than a decade in the making. With patience, insight, and self-confidence to spare, Wolfram outlines a fundamental new way of modeling complex systems.

On the frontier of complexity science since he was a boy, Wolfram is a champion of cellular automata--256 "programs" governed by simple nonmathematical rules. He points out that even the most complex equations fail to accurately model biological systems, but the simplest cellular automata can produce results straight out of nature--tree branches, stream eddies, and leopard spots, for instance. The graphics in A New Kind of Science show striking resemblance to the patterns we see in nature every day.

Wolfram wrote the book in a distinct style meant to make it easy to read, even for nontechies; a basic familiarity with logic is helpful but not essential. Readers will find themselves swept away by the elegant simplicity of Wolfram's ideas and the accidental artistry of the cellular automaton models. Whether or not Wolfram's revolution ultimately gives us the keys to the universe, his new science is absolutely awe-inspiring. --Therese Littleton

From Library Journal

Galileo proclaimed that nature is written in the language of mathematics, but Wolfram would argue that it is written in the language of programs and, remarkably, simple ones at that. A scientific prodigy who earned a doctorate from Caltech at age 20, Wolfram became a Nobel-caliber researcher in the emerging field of complexity shortly thereafter only to abscond from academe and establish his own software company (which published this book). In secrecy, for over ten years, he experimented with computer graphics called cellular automata, which produce shaded images on grid patterns according to programmatic rules (973 images are reproduced here). Wolfram went on to discover that the same vastly complex images could be produced by even very simple sets of rules and argues here that dynamic and complex systems throughout nature are triggered by simple programs. Mathematical science can describe and in some cases predict phenomena but cannot truly explain why what happens happens. Underscoring his point that simplicity begets complexity, Wolfram wrote this book in mostly nontechnical language. Any informed, motivated reader can, with some effort, follow from chapter to chapter, but the work as a whole and its implications are probably understood fully by the author alone. Had this been written by a lesser scientist, many academics might have dismissed it as the work of a crank. Given its source, though, it will merit discussion for years to come. Essential for all academic libraries. [This tome is a surprise best seller on Amazon. Ed.] Gregg Sapp, Science Lib., SUNY at Alban.
- Gregg Sapp, Science Lib., SUNY at Albany
Copyright 2002 Cahners Business Information, Inc.

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Product Details

  • Hardcover: 1192 pages
  • Publisher: Wolfram Media; 1 edition (May 2002)
  • Language: English
  • ISBN-10: 1579550088
  • ISBN-13: 978-1579550080
  • Product Dimensions: 8.1 x 2.5 x 9.7 inches
  • Shipping Weight: 5.6 pounds (View shipping rates and policies)
  • Average Customer Review: 3.0 out of 5 stars  See all reviews (378 customer reviews)
  • Amazon Best Sellers Rank: #113,036 in Books (See Top 100 in Books)

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Top Customer Reviews

Format: Hardcover
This review took almost one year. Unlike many previous referees (rank them by Amazon.com's "most helpful" feature) I read all 1197 pages including notes. Just to make sure I won't miss the odd novel insight hidden among a million trivial platitudes.
On page 27 Wolfram explains "probably the single most surprising discovery I have ever made:" a simple program can produce output that seems irregular and complex.
This has been known for six decades. Every computer science (CS) student knows the dovetailer, a very simple 2 line program that systematically lists and executes all possible programs for a universal computersuch as a Turing machine (TM). It computes all computable patterns, including all those in Wolfram's book, embodies the well-known limits of computability, and is basis of uncountable CS exercises.
Wolfram does know (page 1119) Minsky's very simple universal TMs from the 1960s. Using extensive simulations, he finds a slightly simpler one. New science? Small addition to old science. On page 675 we find a particularly simple cellular automaton (CA) and Matthew Cook's universality proof(?). This might be the most interesting chapter. It reflects that today's PCs are more powerful systematic searchers for simple rules than those of 40 years ago. No new paradigm though.
Was Wolfram at least first to view programs as potential explanations of everything? Nope. That was Zuse. Wolfram mentions him in exactly one line (page 1026): "Konrad Zuse suggested that [the universe] could be a continuous CA." This is totally misleading. Zuse's 1967 paper suggested the universe is DISCRETELY computable, possibly on a DISCRETE CA just like Wolfram's. Wolfram's causal networks (CA's with variable toplogy, chapter 9) will run on any universal CA a la Ulam & von Neumann & Conway & Zuse.
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Format: Hardcover
If a million scientists worked on a million experiments for three hundred years, would they learn as much about the universe as Stephen Wolfram does by sitting at his computer for twenty years?
Apparently not, according to Stephen Wolfram.
I'm annoyed with Wolfram for forcing me to poke fun at him like this. I've been waiting for this book a long time, and I genuinely wanted to give it a thumbs up. Unfortunately, Wolfram has made that impossible.
I gave the book three stars, but in fact I consider it almost un-ratable. What do you do with a 1200-page tome that contains a wealth of substantive and fascinating results, but which is insists, at every turn, to draw over-blown and under-supported conclusions from them? I split the difference and gave it a middling rating, but that does not convey the deep ambivalence I feel toward this work.
Given Wolfram's reputation, I expected a certain amount of hubris, and even looked forward to it. Most scientists work hard to suppress the egotism that drives them, but Wolfram's ego is out there in the open. While this can be refreshing, what I found here left me dumbfounded. For Wolfram, all of scientific history is either prelude or footnote to his own work on 1-D cellular automata. On pages 12-16 he breezily cites other work in chaos theory, non-linear dynamics and complexity theory. At the end of the book, there are hundreds of pages of footnotes describing previous history as essentially one damn thing after another - a testament to all the people that didn't see the promised land, as he has.
Wolfram attempts to usurp all credit for the "computational perspective." Assertions such as "the discoveries in this book showing that simple rules can lead to complex behavior" are repeated to the point of exhaustion.
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Format: Hardcover
This is a book of ruminations about cellular automata. It is chiefly concerned with the way that the state of a system evolves when deterministic rules are applied to it. The simplest system is a single point in either state 0 or state 1. The transition rule could be that the state "0" changes to state "1", and state "1" changes to state "0". That rule can be expressed as follows.
{1->0, 0->1}
If the system's initial state is 1, then the transition rule (repeatedly applied) yields the following alternating pattern of states.
For hundreds of pages the author discusses the behavior of 1-dimensional automata built from 3-cell transition rules. The 2^3=8 different states of a 3-cell cluster can be written in binary notation from 000 up to 111. The cell in the middle can transition to either of two binary states, yielding a total of 2^8=256 rules. Most rules lead to periodically repeating behaviors, with short periods like the alternating pattern shown above.
An exception is rule 30 (30 in binary is 00011110; these bits the right-hand-side values for the 8 transitions).
rule 30:
{ 111->0, 110->0, 101->0, 100->1, 011->1, 010->1, 001->1, 000->0 }
When applied to an initial state of a single 1 surrounded by 0's, rule 30 generates the following pattern (developing downward from the top row). The array can be displayed as a bitmap of black and white pixels, producing a visualization of the evolving state of the horizontal rows.
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