Chaos: Making a New Science

Chaos: Making a New Science

by James Gleick

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The blockbuster modern science classic that introduced the butterfly effect to the world—even more relevant two decades after it became an international sensation 
For centuries, scientific thought was focused on bringing order to the natural world. But even as relativity and quantum mechanics undermined that rigid certainty in the first half of the twentieth century, the scientific community clung to the idea that any system, no matter how complex, could be reduced to a simple pattern. In the 1960s, a small group of radical thinkers began to take that notion apart, placing new importance on the tiny experimental irregularities that scientists had long learned to ignore. Miniscule differences in data, they said, would eventually produce massive ones—and complex systems like the weather, economics, and human behavior suddenly became clearer and more beautiful than they had ever been before. In this seminal work of scientific writing, James Gleick lays out a cutting edge field of science with enough grace and precision that any reader will be able to grasp the science behind the beautiful complexity of the world around us.

Product Details

ISBN-13: 9781453210475
Publisher: Open Road Media
Publication date: 03/22/2011
Sold by: Barnes & Noble
Format: NOOK Book
Pages: 360
Sales rank: 99,075
File size: 8 MB

About the Author

Born in New York City in 1954, James Gleick is one of the nation’s preeminent science writers. Upon graduating from Harvard in 1976, he founded Metropolis, a weekly Minneapolis newspaper, and spent the next decade working at the New York Times. Gleick’s prominent works include Genius: The Life and Science of Richard Feynman, Isaac Newton, and Chaos: Making a New Science, all of which were shortlisted for the Pulitzer Prize. His latest book, The Information: A History, a Theory, a Flood,was published in March 2011. He lives and works in New York.

Read an Excerpt



By James Gleick


Copyright © 2008 James Gleick
All rights reserved.
ISBN: 978-1-4532-1047-5



Physicists like to think that all you have to do is say, these are the conditions, now what happens next?


The sun beat down through a sky that had never seen clouds. The winds swept across an earth as smooth as glass. Night never came, and autumn never gave way to winter. It never rained. The simulated weather in Edward Lorenz's new electronic computer changed slowly but certainly, drifting through a permanent dry midday midseason, as if the world had turned into Camelot, or some particularly bland version of southern California.

Outside his window Lorenz could watch real weather, the early-morning fog creeping along the Massachusetts Institute of Technology campus or the low clouds slipping over the rooftops from the Atlantic. Fog and clouds never arose in the model running on his computer. The machine, a Royal McBee, was a thicket of wiring and vacuum tubes that occupied an ungainly portion of Lorenz's office, made a surprising and irritating noise, and broke down every week or so. It had neither the speed nor the memory to manage a realistic simulation of the earth's atmosphere and oceans. Yet Lorenz created a toy weather in 1960 that succeeded in mesmerizing his colleagues. Every minute the machine marked the passing of a day by printing a row of numbers across a page. If you knew how to read the printouts, you would see a prevailing westerly wind swing now to the north, now to the south, now back to the north. Digitized cyclones spun slowly around an idealized globe. As word spread through the department, the other meteorologists would gather around with the graduate students, making bets on what Lorenz's weather would do next. Somehow, nothing ever happened the same way twice.

Lorenz enjoyed weather—by no means a prerequisite for a research meteorologist. He savored its changeability. He appreciated the patterns that come and go in the atmosphere, families of eddies and cyclones, always obeying mathematical rules, yet never repeating themselves. When he looked at clouds, he thought he saw a kind of structure in them. Once he had feared that studying the science of weather would be like prying a jack-in-the-box apart with a screwdriver. Now he wondered whether science would be able to penetrate the magic at all. Weather had a flavor that could not be expressed by talking about averages. The daily high temperature in Cambridge, Massachusetts, averages 75 degrees in June. The number of rainy days in Riyadh, Saudi Arabia, averages ten a year. Those were statistics. The essence was the way patterns in the atmosphere changed over time, and that was what Lorenz captured on the Royal McBee.

He was the god of this machine universe, free to choose the laws of nature as he pleased. After a certain amount of undivine trial and error, he chose twelve. They were numerical rules—equations that expressed the relationships between temperature and pressure, between pressure and wind speed. Lorenz understood that he was putting into practice the laws of Newton, appropriate tools for a clockmaker deity who could create a world and set it running for eternity. Thanks to the determinism of physical law, further intervention would then be unnecessary. Those who made such models took for granted that, from present to future, the laws of motion provide a bridge of mathematical certainty. Understand the laws and you understand the universe. That was the philosophy behind modeling weather on a computer.

Indeed, if the eighteenth-century philosophers imagined their creator as a benevolent noninterventionist, content to remain behind the scenes, they might have imagined someone like Lorenz. He was an odd sort of meteorologist. He had the worn face of a Yankee farmer, with surprising bright eyes that made him seem to be laughing whether he was or not. He seldom spoke about himself or his work, but he listened. He often lost himself in a realm of calculation or dreaming that his colleagues found inaccessible. His closest friends felt that Lorenz spent a good deal of his time off in a remote outer space.

As a boy he had been a weather bug, at least to the extent of keeping close tabs on the max-min thermometer recording the days' highs and lows outside his parents' house in West Hartford, Connecticut. But he spent more time inside playing with mathematical puzzle books than watching the thermometer. Sometimes he and his father would work out puzzles together. Once they came upon a particularly difficult problem that turned out to be insoluble. That was acceptable, his father told him: you can always try to solve a problem by proving that no solution exists. Lorenz liked that, as he always liked the purity of mathematics, and when he graduated from Dartmouth College, in 1938, he thought that mathematics was his calling. Circumstance interfered, however, in the form of World War II, which put him to work as a weather forecaster for the Army Air Corps. After the war Lorenz decided to stay with meteorology, investigating the theory of it, pushing the mathematics a little further forward. He made a name for himself by publishing work on orthodox problems, such as the general circulation of the atmosphere. And in the meantime he continued to think about forecasting.

To most serious meteorologists, forecasting was less than science. It was a seat-of-the-pants business performed by technicians who needed some intuitive ability to read the next day's weather in the instruments and the clouds. It was guesswork. At centers like M.I.T., meteorology favored problems that had solutions. Lorenz understood the messiness of weather prediction as well as anyone, having tried it firsthand for the benefit of military pilots, but he harbored an interest in the problem—a mathematical interest.

Not only did meteorologists scorn forecasting, but in the 1960s virtually all serious scientists mistrusted computers. These souped-up calculators hardly seemed like tools for theoretical science. So numerical weather modeling was something of a bastard problem. Yet the time was right for it. Weather forecasting had been waiting two centuries for a machine that could repeat thousands of calculations over and over again by brute force. Only a computer could cash in the Newtonian promise that the world unfolded along a deterministic path, rule-bound like the planets, predictable like eclipses and tides. In theory a computer could let meteorologists do what astronomers had been able to do with pencil and slide rule: reckon the future of their universe from its initial conditions and the physical laws that guide its evolution. The equations describing the motion of air and water were as well known as those describing the motion of planets. Astronomers did not achieve perfection and never would, not in a solar system tugged by the gravities of nine planets, scores of moons and thousands of asteroids, but calculations of planetary motion were so accurate that people forgot they were forecasts. When an astronomer said, "Comet Halley will be back this way in seventy-six years," it seemed like fact, not prophecy. Deterministic numerical forecasting figured accurate courses for spacecraft and missiles. Why not winds and clouds?

Weather was vastly more complicated, but it was governed by the same laws. Perhaps a powerful enough computer could be the supreme intelligence imagined by Laplace, the eighteenth-century philosopher-mathematician who caught the Newtonian fever like no one else: "Such an intelligence," Laplace wrote, "would embrace in the same formula the movements of the greatest bodies of the universe and those of the lightest atom; for it, nothing would be uncertain and the future, as the past, would be present to its eyes." In these days of Einstein's relativity and Heisenberg's uncertainty, Laplace seems almost buffoon-like in his optimism, but much of modern science has pursued his dream. Implicitly, the mission of many twentieth-century scientists—biologists, neurologists, economists—has been to break their universes down into the simplest atoms that will obey scientific rules. In all these sciences, a kind of Newtonian determinism has been brought to bear. The fathers of modern computing always had Laplace in mind, and the history of computing and the history of forecasting were intermingled ever since John von Neumann designed his first machines at the Institute for Advanced Study in Princeton, New Jersey, in the 1950s. Von Neumann recognized that weather modeling could be an ideal task for a computer.

There was always one small compromise, so small that working scientists usually forgot it was there, lurking in a corner of their philosophies like an unpaid bill. Measurements could never be perfect. Scientists marching under Newton's banner actually waved another flag that said something like this: Given an approximate knowledge of a system's initial conditions and an understanding of natural law, one can calculate the approximate behavior of the system. This assumption lay at the philosophical heart of science. As one theoretician liked to tell his students: "The basic idea of Western science is that you don't have to take into account the falling of a leaf on some planet in another galaxy when you're trying to account for the motion of a billiard ball on a pool table on earth. Very small influences can be neglected. There's a convergence in the way things work, and arbitrarily small influences don't blow up to have arbitrarily large effects." Classically, the belief in approximation and convergence was well justified. It worked. A tiny error in fixing the position of Comet Halley in 1910 would only cause a tiny error in predicting its arrival in 1986, and the error would stay small for millions of years to come. Computers rely on the same assumption in guiding spacecraft: approximately accurate input gives approximately accurate output. Economic forecasters rely on this assumption, though their success is less apparent. So did the pioneers in global weather forecasting.

With his primitive computer, Lorenz had boiled weather down to the barest skeleton. Yet, line by line, the winds and temperatures in Lorenz's printouts seemed to behave in a recognizable earthly way. They matched his cherished intuition about the weather, his sense that it repeated itself, displaying familiar patterns over time, pressure rising and falling, the airstream swinging north and south. He discovered that when a line went from high to low without a bump, a double bump would come next, and he said, "That's the kind of rule a forecaster could use." But the repetitions were never quite exact. There was pattern, with disturbances. An orderly disorder.

To make the patterns plain to see, Lorenz created a primitive kind of graphics. Instead of just printing out the usual lines of digits, he would have the machine print a certain number of blank spaces followed by the letter a. He would pick one variable—perhaps the direction of the airstream. Gradually the a's marched down the roll of paper, swinging back and forth in a wavy line, making a long series of hills and valleys that represented the way the west wind would swing north and south across the continent. The orderliness of it, the recognizable cycles coming around again and again but never twice the same way, had a hypnotic fascination. The system seemed slowly to be revealing its secrets to the forecaster's eye.

One day in the winter of 1961, wanting to examine one sequence at greater length, Lorenz took a shortcut. Instead of starting the whole run over, he started midway through. To give the machine its initial conditions, he typed the numbers straight from the earlier printout. Then he walked down the hall to get away from the noise and drink a cup of coffee. When he returned an hour later, he saw something unexpected, something that planted a seed for a new science.

This new run should have exactly duplicated the old. Lorenz had copied the numbers into the machine himself. The program had not changed. Yet as he stared at the new printout, Lorenz saw his weather diverging so rapidly from the pattern of the last run that, within just a few months, all resemblance had disappeared. He looked at one set of numbers, then back at the other. He might as well have chosen two random weathers out of a hat. His first thought was that another vacuum tube had gone bad.

Suddenly he realized the truth. There had been no malfunction. The problem lay in the numbers he had typed. In the computer's memory, six decimal places were stored: .506127. On the printout, to save space, just three appeared: .506. Lorenz had entered the shorter, rounded-off numbers, assuming that the difference—one part in a thousand—was inconsequential.

It was a reasonable assumption. If a weather satellite can read ocean-surface temperature to within one part in a thousand, its operators consider themselves lucky. Lorenz's Royal McBee was implementing the classical program. It used a purely deterministic system of equations. Given a particular starting point, the weather would unfold exactly the same way each time. Given a slightly different starting point, the weather should unfold in a slightly different way. A small numerical error was like a small puff of wind—surely the small puffs faded or canceled each other out before they could change important, large-scale features of the weather. Yet in Lorenz's particular system of equations, small errors proved catastrophic.

He decided to look more closely at the way two nearly identical runs of weather flowed apart. He copied one of the wavy lines of output onto a transparency and laid it over the other, to inspect the way it diverged. First, two humps matched detail for detail. Then one line began to lag a hairsbreadth behind. By the time the two runs reached the next hump, they were distinctly out of phase. By the third or fourth hump, all similarity had vanished.

It was only a wobble from a clumsy computer. Lorenz could have assumed something was wrong with his particular machine or his particular model—probably should have assumed. It was not as though he had mixed sodium and chlorine and got gold. But for reasons of mathematical intuition that his colleagues would begin to understand only later, Lorenz felt a jolt: something was philosophically out of joint. The practical import could be staggering. Although his equations were gross parodies of the earth's weather, he had a faith that they captured the essence of the real atmosphere. That first day, he decided that long-range weather forecasting must be doomed.

"We certainly hadn't been successful in doing that anyway and now we had an excuse," he said. "I think one of the reasons people thought it would be possible to forecast so far ahead is that there are real physical phenomena for which one can do an excellent job of forecasting, such as eclipses, where the dynamics of the sun, moon, and earth are fairly complicated, and such as oceanic tides. I never used to think of tide forecasts as prediction at all—I used to think of them as statements of fact—but of course, you are predicting. Tides are actually just as complicated as the atmosphere. Both have periodic components—you can predict that next summer will be warmer than this winter. But with weather we take the attitude that we knew that already. With tides, it's the predictable part that we're interested in, and the unpredictable part is small, unless there's a storm.

"The average person, seeing that we can predict tides pretty well a few months ahead would say, why can't we do the same thing with the atmosphere, it's just a different fluid system, the laws are about as complicated. But I realized that any physical system that behaved nonperiodically would be unpredictable."

The fifties and sixties were years of unreal optimism about weather forecasting. Newspapers and magazines were filled with hope for weather science, not just for prediction but for modification and control. Two technologies were maturing together, the digital computer and the space satellite. An international program was being prepared to take advantage of them, the Global Atmosphere Research Program. There was an idea that human society would free itself from weather's turmoil and become its master instead of its victim. Geodesic domes would cover cornfields. Airplanes would seed the clouds. Scientists would learn how to make rain and how to stop it.

The intellectual father of this popular notion was Von Neumann, who built his first computer with the precise intention, among other things, of controlling the weather. He surrounded himself with meteorologists and gave breathtaking talks about his plans to the general physics community. He had a specific mathematical reason for his optimism. He recognized that a complicated dynamical system could have points of instability—critical points where a small push can have large consequences, as with a ball balanced at the top of a hill. With the computer up and running, Von Neumann imagined that scientists would calculate the equations of fluid motion for the next few days. Then a central committee of meteorologists would send up airplanes to lay down smoke screens or seed clouds to push the weather into the desired mode. But Von Neumann had overlooked the possibility of chaos, with instability at every point.


Excerpted from CHAOS by James Gleick. Copyright © 2008 James Gleick. Excerpted by permission of OPEN ROAD INTEGRATED MEDIA.
All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.

Table of Contents


The Butterfly Effect,
Life's Ups and Downs,
A Geometry of Nature,
Strange Attractors,
The Experimenter,
Images of Chaos,
The Dynamical Systems Collective,
Inner Rhythms,
Chaos and Beyond,
Notes on Sources and Further Reading,

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Chaos 4.1 out of 5 based on 0 ratings. 35 reviews.
Guest More than 1 year ago
Unlike the reviewers above, I found the book to be a bit tough going at times. In addition, the focus on the personalities involved I found to be distracting. The math/science is good, the story somewhat less so.
Guest More than 1 year ago
A very good history of fractal dynamics, its origins and applications for the layman from its origins in skewed data during meteorological printouts at MIT to modern day applications and how seemingly irrational phenomena can be explained..
Guest More than 1 year ago
The story of chaos is unwoven in this book in an interesting manner with few equations so the average reader can understand and comprehend the emergence of this science.
duanewilliams on LibraryThing More than 1 year ago
Chaos: Making a New Science is about a variety of topics: the sensitivity of some systems to their initial conditions, the weather being a prime example, which makes detailed long-term forecasting impossible; nonlinear systems; fractals; strange attractors; dynamical systems; etc. It is also about the people who discovered and studied these phenomena. It describes their difficulties in introducing these ideas into the scientific community. That's not an unusual situation in science. Einstein's special theory of relativity, for example, despite it's mathematical simplicity and fit with evidence, was not readily accepted.Gleick sometimes strays a bit from his topic, as when he briefly talks about Darwinian thinking in biology. He writes, "In biology, however, Darwin firmly established teleology as the central mode of thinking about cause. [...] Natural selection operates not on genes or embryos, but on the final product. [...] Final cause survives in science wherever Darwinian thinking has become habitual." (se p. 201 in the original hardback edition) I don't know where he got his information, but he got it wrong. Darwinian evolution through natural selection is not teleological. In What Evolution Is, Ernst Mayr writes, "... those who adopt teleological thinking will argue that progress is due to a built-in drive or striving toward perfection. Darwin rejected such a causation and so do modern Darwinians ..." In Darwin's Dangerous Idea, Daniel Dennett writes, "The theory of natural selection shows how ever feature of the natural world can be the product of a blind, unforesightful, nonteleological, ultimately mechanical process of differential reproduction over long periods of time." The nonteleological nature of Darwinian evolution is one of the principle themes of Dennett's book.Chaos is a long book about somewhat difficult ideas, mostly of a mathematical nature, but the mathematics is largely suppressed. One important point that I think he makes very clear is that very simple equations when iterated in real space can exhibit surprising behavior.The topics of this book are mostly outside my areas of even limited expertise, but I was wondering as I read it how many of the phenomena it describes depend on the use of real numbers, i.e., numbers that in general require infinite precision, e.g. ¿. If physical theories were to be developed on the basis of discrete mathematics, would some of these problems of chaos disappear? Consider the very first topic in the book: the sensitivity of weather models to initial conditions. With limited precision measuring instruments there are infinitely many states of the weather, if described by real numbers, that cannot be distinguished. So, if small differences, below the precision of measurement, can make a big difference as the weather develops, we have a problem that limits predictability. But, if the physics of weather were described by a mathematics with finite precision, then we might be able to make completely accurate measurements of initial conditions¿in principle.I found Chaos interesting to read, but I am always skeptical about reading explanations of science written by journalists, just as I am skeptical of explanations of science written by philosophers.
theportal2002 on LibraryThing More than 1 year ago
Although the book slowed down in spots it still was very exciting. I loved reading about how non linear systems or Chaos slowly emerged to change the face of many disciplines today. Being a consultant it makes me take a fresh look at the way I view and interpret data looking for strange attractors and other things. It was very interesting to see how many of the pioneers of this new science had no mentors or support within their communities. As a matter of fact many of them were warned that studying a new discipline would not bode well for their careers.
br77rino on LibraryThing More than 1 year ago
As I recall (it's been decades since I read this, and I'm planning on reading "The Information"), it's a pretty good book. Lots of fractal stuff in it of course.
P_S_Patrick on LibraryThing More than 1 year ago
I found this a fairly good introduction to chaos. It was well written and easy to understand, like a popular science book should be. A non scientist would have easily understood most of it, and would probably find most of it interesting too. The book might have benefited by having sections which had more detail, a bit more maths, as there is little technical information at all in this book. The book goes on about all the scientists and their stories, which played a part in the developments in the field of chaos. The author made sure it was kept interesting, and I imagine most readers would find the book genuinely interesting, as well as finishing with a better understanding of the topic. Those involved in the field of science should appreciate the book, as chaos can be found in many of the serious disciplines such as biology, physics, maths, and chemistry. It was also interesting to learn how chaos forms amazingly complex patterns from seemingly random things, if you know how to analyze them.
raindiva1 on LibraryThing More than 1 year ago
My favorite chaos book. Interesting and informative. Perfectly readable for a layperson or a scientist.
TheBentley on LibraryThing More than 1 year ago
Very interesting, but not as well-written or as accessible as "Faster." Personally, I could have done with more concept and less profiling of the individual personalities involved.
pjane on LibraryThing More than 1 year ago
I'm not a scientist, but I found this book both readable and fascinating. Gleick delves deep into some pretty abstruse subjects, but he keeps his finger on the human stories behind them and drives his history forward. Recommended.
co_coyote on LibraryThing More than 1 year ago
I recommended this book to my son, the incipient neurobiologist, this fall and he had the same reaction to it I did: is it too late to become a mathematician? In my case, alas, the answer is obviously yes, but there is still hope for him. Anyone who can write this well and compellingly about a subject I don't even think I am interested in all that much is to be enormously commended.
yapete on LibraryThing More than 1 year ago
By now a classic. This book got me into Chaos theory big time. I was even on the way to making it my thesis topic, but got seduced by the dark side... (experimental physics). Great read; what a science book should be.
FlyByPC on LibraryThing More than 1 year ago
One of my all-time favorite books -- and the one that got me interested in dynamical systems. This would make the short list of books I would want along if stranded on the proverbial desert island. Gleick not only does an outstanding job of describing chaos (in the sense of sensitivity to initial conditions), but inspires readers to experiment. Reading this book while playing around with the ideas it inspires using your favorite programming language is a fun way to pass the time. (Try programming a model of the waterwheel and graphing its velocity!) Very, very highly recommended if you've ever had the slightest interest in science, math, or computers.
PointedPundit on LibraryThing More than 1 year ago
Well-Written; could have Employed a Little More MathFew writers write clearly and concisely about science and Mathematics. James Gleick, a former science writer for the New York Times, writes about the first years of the study of chaos. Focusing on scientists rather than science, Gleick explains the thought processes and investigative techniques researchers applied to chaos problems. Rather than attempt to explain Julia sets, Lorenz attractors, and the Mandelbrot Set with complicated equations, Chaos employs sketches, photographs, and descriptive prose.There are not many writers who have the ability to write on two planes. One is understandable by the general public. The other is appreciated by experts who grasp the subject matter and appreciate the author¿s depth of understanding. I am not one of the latter. While reading the book, I found myself long for math that would connect the prose to the science.Nevertheless, this book is a history of a new science. Limited as it is, it inspired me to further study. It is probably asking too much to expect more from a book about science¿s frontiers.
boeflak on LibraryThing More than 1 year ago
I won't pretend I understood all of it. But it's written in a way so that anyone can understand much of it -- and every step on the journey is fascinating.
marinam713 on LibraryThing More than 1 year ago
An exhilarating read. Understanding variation, adaptability, and the overall beauty of our physical systems has never been this much fun to read about. Literally walked me through my own thesis. Absolutely brilliant!
Scott_Morris on LibraryThing More than 1 year ago
Great Book. I read it to get a base on the 'new science' and how it was affecting thought and organization development. It's written in an easy yet engaging way, and I felt like I was reading a novel. Push my thinking through worm holes I didn't know existed and continues to challenge me. Is smeinal in my understanding of God, Church, and relationships.
justine on LibraryThing More than 1 year ago
The fundamental book on chaos science for the popular audience.
zezethex on LibraryThing More than 1 year ago
James Gleick's early history of the science of chaos is a thorough and personal account compiled from hours of interviews, articles and lectures. Chaos is perhaps a somewhat controversial term in science and perhaps is better described as complexity forming out of simplicity or self-organizion emerging from apparent randomness. The simple, mechanistic way of viewing the world as deterministic, static and linear no longer holds water. In other words, systems are not clocklike machines destined to run down into a lifeless eternity, but rather evolve through time into more beautiful and complex patterns. At what point does a chemical feedback loop cease to become "mere chemicals" and become alive? Time can be viewed as a process, rather than a series of intervals. Gleick, for the most part, stays away from couching philosophical questions and rather lets the reader ask for themselves. This book is a fantastic introduction for those with the patience for scientific terms and interest in scientific history. For the less scientifically inclined a more general, great introduction on the subject is a book called the Turbulent Mirror, by John Briggs and F. David Peat.
lunaverse on LibraryThing More than 1 year ago
This book on complexity theory explains chaos concepts through the history of the discoveries. You not only learn about Strange Attractors and Bifrication, but also about the men who first coined these terms, and the conditions under which the discoveries were made. Fewer pictures than Turbulent Mirror, but if you're going to read two or more books on Chaos Theory, this should be one of them.
daschaich on LibraryThing More than 1 year ago
Achieves its goal - even after 18 years: When I first picked up Gleick's "Chaos" I was a little skeptical - could a book written in 1987 still work as an introduction to chaos and nonlinear dynamics, a field that has been evolving rapidly for the past eighteen years? Well, in a certain sense, it turns out it can.The truth is that the focus of Gleick's book is not so much chaos itself as it is the people who first explored chaos theory and eventually managed to make it respectable and bring it into the mainstream. As the book's subtitle hints, Gleick is concerned mainly with how a 'new science' is 'made', not necessarily with the actual science or math involved. This was not quite what I was expecting from "Chaos", but it is actually an advantage for the book, since its age becomes somewhat irrelevant: although chaos theory itself has been growing and evolving dramatically in recent decades, "Chaos" deals only with its roots in the '60s, '70s and early '80s. On the other hand, I was hoping for more discussion of the science itself, rather than the personalities involved in its early development.I was also not that taken with the style of Gleick's writing. His narrative tends to jump around rapidly, often spending only a few pages on some person or event before moving on to another, commonly with little in the way of connection or logical transition. This is fine for short articles in newspapers and magazines, but it doesn't work so well in a 300+ page book. The vast cast of characters (meteorologists, physicists, mathematicians, computer scientists, biologists, ecologists and many others) spins in and out of view, and it can be very difficult to get more than a general impression how the little pieces all fit together in the big picture.However, even though I'm complaining about the content and presentation, I'm still giving "Chaos" four stars. This is because "Chaos" managed to get me interested in and excited about nonlinear dynamics. Gleick was able to convey the sense of wonder and excitement that comes from looking at nature in a new way, through the lens of nonlinearity. He successfully presented the making of this new science as the greatest and most exciting scientific revolution since the development of quantum mechanics - with the difference that chaos is more accessible, more understandable, and applicable in a far wider range of fields.In short, "Chaos" still achieves its goal 18 years after it was written. It gets the reader (this reader, at least) interested in and excited about nonlinear dynamics and eager to explore the topic in greater depth. Reading Gleick's book inspired me to pick up a copy of Robert Hilborn's "Chaos and Nonlinear Dynamics" from the library and take a more serious look at the science itself. "Chaos" should make a good read for anyone who knows little or nothing about chaos or nonlinear dynamics but is curious about the topic and interested in learning a bit about its early development.
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TonyAY More than 1 year ago
This book is typical of those authors that provide a lot of fact and detail, but do not tie anything together. Other than saying that I read a book about "Chaos theory", it was of little value or entertainment.