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Think Complexity: Complexity Science and Computational Modeling 1st Edition

18 customer reviews
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ISBN-13: 978-1449314637
ISBN-10: 1449314635
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Editorial Reviews

Book Description

Exploring Complexity Science with Python

About the Author

Allen Downey is a Professor of Computer Science at the Olin College of Engineering and a former Visiting Scientist at Google, Inc. He has taught computer science at Wellesley College, Colby College and U.C. Berkeley. He has a Ph.D. in Computer Science from U.C. Berkeley and Master's and Bachelor's degrees from MIT.

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Product Details

  • Paperback: 160 pages
  • Publisher: O'Reilly Media; 1 edition (March 12, 2012)
  • Language: English
  • ISBN-10: 1449314635
  • ISBN-13: 978-1449314637
  • Product Dimensions: 7 x 0.4 x 9.2 inches
  • Shipping Weight: 12.3 ounces (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (18 customer reviews)
  • Amazon Best Sellers Rank: #523,111 in Books (See Top 100 in Books)

More About the Author

Allen Downey is a Professor of Computer Science at Olin College and author of Think Python, Think Stats, Think Bayes, Think Complexity, and several other computer science books. The idea behind these books is that if you know how to program, you can use that skill to learn other things.

Allen is an avid runner, gardener and cook. He ran the Boston Marathon for the first time in 2011, finishing in 3:45. Allen lives in Needham, MA with his wife, two daughters, and two cats.

Customer Reviews

Most Helpful Customer Reviews

27 of 29 people found the following review helpful By Si Dunn on March 28, 2012
Format: Paperback
Are you a reasonably competent Python programmer yearning for new challenges? "Think Complexity" definitely delivers some.

Allen B. Downey's well-written new book can help you dive into complexity science and improve your Python skills along the way. It's not just another hello-world, learn-to-program-in-Python text.

"This book," Downey states, "is about data structures and algorithms, intermediate programming in Python, computational modeling, and the philosophy of science." Hello, NEW world.

His new work, he adds, sprang out of a blending of "boredom and fascination: boredom with the usual presentation of data structures and algorithms and fascination with complex systems. The problem with data structures is that they are often taught without a motivating context; the problem with complexity science is that it usually is not taught at all."

Complexity science is the scientific study of complex systems - which can be anything from computer networks to the human brain, global markets, ecosystems, metropolitan areas, space shuttles, ant trails, and so forth. Complexity science is practiced "at the intersection of mathematics, computer science, and natural science," Downey says.

How does "the philosophy of science" fit into Downey's book? "Think Complexity" offers "experiments and results [that] raise questions relevant to the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, holism and reductionism, and epistemology."

Downey's new work "picks up where Think Python left off" and is intended to appeal to the "broad intellectual curiosity" of software engineers and their "drive to expand their knowledge and skills.
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11 of 12 people found the following review helpful By Eric Chou on June 30, 2012
Format: Paperback
I really like this book, but I feel I could get a lot more out of this book if I had a more solid understanding that was introduced in the author's "Think Python" book. This is obviously by no fault of the book itself, just a fair warning to people whom may be in the same boat. I plan to go thru "Think Python" and re-read this book again. Readers need some intermediate Python chops and some understanding in scientific methodology prior to this book in order to maximize the benefits. And yes, as other review mentioned, plan to spend a fair amount of time to read up on all the references. I read the book digitally via Kindle app, so it was easy to link to the Wiki pages, but I can see some frustration if one was using a printed version. Also plan on doing a fair amount of coding in the exercises.

It was interesting how the author organized the idea shift in scientific thinking of the complexity science. If one is familiar with the works like Malcolm Gladwell in "Blink", "Outliers" or similarly in "Freakonomics" one can clearly related to the method of using simulation-based computational model to solve problems that are non-linear with large composite, many-to-many elements. Many of the TED talks I have seen also employed this line of method in arriving at their respective conclusions.

The middle section of the book introduced various models and approaches into solving complex problems. I absolutely love the fact that the theories were broken down into small pieces of problems that can be illustrated by small Python programs. Of the examples, the sections on Dijkstra algorithm and scale-free networks were the most interesting to me. As network engineer whom have dealt with OSPF and IS-IS on regular basis, I never thought it was possible to simulate the algorithm via Python.
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10 of 11 people found the following review helpful By Ira Laefsky VINE VOICE on March 13, 2012
Format: Paperback
This short but extremely exciting book is simultaneously an invitation to actively participate in what Stephen Wolfram has called "A New Kind of Science", and an introduction to "Data Structures" (what traditionally has been the second course in Computer Science with an exciting new motivation. Complexity Science has been a part of the public attention since the 1992 publications of Stephen Levy's "Artificial Life" and M. Mitchell Waldrop's "Complexity"; it attempts to motivate and explain aspects of Life's Biological Processes, Economics, and Chaos Phenomena (such as weather, and fractal displays. While Wolfram's massive and influential book of 2002 vastly popularized this important new field, there hasn't been a simple way (up until now) for DIY experimenters to see on their own computers the results where a small number of simple rules leads to the most complex results and phenomena.

This book gives relatively straightforward programs in the Python Language which explain and illustrate phenomena such as Conway's "Game of Life", Wolfram's Cellular Automata experiments, and fractal graphics which can be run on a experimenter's own PC. Moreover, this book invites the reader to design their own experiments which may be published in a subsequent edition of the book and which give the real possibility of participating in new science to a moderately skilled home experimenter.

The book also importantly provides new motivation to one of the most basic skills of computer science by providing a way through which relatively simple data structures can yield important and surprising results in a variety of new science.

--Ira Laefsky, MSE/MBA
Home Experimenter formerly on the Senior Consulting Staff of Arthur D. Little, Inc. and Digital Equipment Corporation
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