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Think Complexity: Complexity Science and Computational Modeling 1st Edition

4.3 out of 5 stars 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: #127,102 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

Top Customer Reviews

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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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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Format: Paperback Verified Purchase
As most people these days, I spend my whole day in front of a computer, and I do most of my reading on a screen.
So when I concede myself the luxury of buying a "real" book, I expect it to be something that I can enjoy sitting on a sofa or in bed, as a stand alone item.
This book is certainly an interesting read for the topics it examines, however it completely fails on my requirements. There is not a single page in which the author is not asking the reader to go check a wikipedia page, download a scientific paper or go examine a piece of code available on the book's companion website.
This leaves the reader two choices: either do what the author is asking, sacrificing what should have been a reading session for yet another go of clicks and scrolls or (what i did) just ignore the suggestions. This will obviously make it more difficult to follow the line of thought, especially because the author many times is posing questions which have no answer in the book itself. So if you don't do the homework you never get the answer!
Overall the continuous referencing to external resources has left the feeling in me that this piece of work could have been a stimulating and interesting one if only the author had put in it the extra effort to make it a self standing reading. He could still have provided links to external resources, but only as optional.
In the end I don't recommend it unless you are really committed to following the author's path, which may be more doable for a college course type of reader than for a casual one like myself.
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