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Allen Downey
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Think Complexity: Complexity Science and Computational Modeling 1st Edition

by Allen B. Downey (Author)
4.2 4.2 out of 5 stars 37 ratings
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Think Complexity: Complexity Science and Computational Modeling
Think Complexity: Complexity Science and Computational Modeling
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Expand your Python skills by working with data structures and algorithms in a refreshing context―through an eye-opening exploration of complexity science. Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of exercises, case studies, and easy-to-understand explanations.

You’ll work with graphs, algorithm analysis, scale-free networks, and cellular automata, using advanced features that make Python such a powerful language. Ideal as a text for courses on Python programming and algorithms, Think Complexity will also help self-learners gain valuable experience with topics and ideas they might not encounter otherwise.

  • Work with NumPy arrays and SciPy methods, basic signal processing and Fast Fourier Transform, and hash tables
  • Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines
  • Get starter code and solutions to help you re-implement and extend original experiments in complexity
  • Explore the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, and other topics
  • Examine case studies of complex systems submitted by students and readers

Previous page
  1. ISBN-10
    1449314635
  2. ISBN-13
    978-1449314637
  3. Edition
    1st
  4. Publisher
    O'Reilly Media
  5. Publication date
    April 10, 2012
  6. Language
    English
  7. Dimensions
    7 x 0.37 x 9.19 inches
  8. Print length
    156 pages
  9. See all details
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Editorial Reviews

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.

Product details

  • Publisher ‏ : ‎ O'Reilly Media; 1st edition (April 10, 2012)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 156 pages
  • ISBN-10 ‏ : ‎ 1449314635
  • ISBN-13 ‏ : ‎ 978-1449314637
  • Item Weight ‏ : ‎ 9.6 ounces
  • Dimensions ‏ : ‎ 7 x 0.37 x 9.19 inches
  • Customer Reviews:
    4.2 4.2 out of 5 stars 37 ratings

About the author

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Allen Downey

Allen Downey

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

Allen has a Ph.D. from U.C. Berkeley and M.S. and B.S. degrees from MIT. He has taught at Colby College and Wellesley College.

Customer reviews

4.2 out of 5 stars
4.2 out of 5
37 global ratings

Top reviews from the United States

M.S.
5.0 out of 5 stars Excellent starting point for learning Computational modelling
Reviewed in the United States on April 25, 2012
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I bought this book to basically understand how Google and Facebook model their internal data and have algorithms make sense of it, to understand how any real life observation could be modeled correctly on a computer. I was amazed at the way this book is organized. It starts with a philosophy that complexity science is a way to model systems "as-is" , i.e when systems are simplified , they tend to lose a lot of information which could have been critical the evolution of the system. It then gives a solid introduction to basics of data-structures being used to model data in the most efficient way. Every chapter has links to a trove of articles and documents and exercises. Be sure to take time to solve those exercises. It will help you build a strong foundation towards computer science and computational modelling. The case studies will further reinforce your knowledge . This is not just a book its a Guide which will show you paths to learn deeper about the truth of things. I have bought the kindle version of the book it helps to navigate through to the links in Wikipedia and public websites while practicing the problems at the end. My advice to anyone who buys this book, Read through every chapter , solve every problem in the book and try to read all the reference articles, even if it takes a long time. I promise you , this book will transform you Completely.
11 people found this helpful
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alessandro averchi
3.0 out of 5 stars Don't buy unless you are seriously committed to do your homework.
Reviewed in the United States on February 8, 2013
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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.
39 people found this helpful
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Eric Chou
4.0 out of 5 stars Where Rubber meets the Road
Reviewed in the United States on June 30, 2012
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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. That was a treat. I also have some ideas inspired by the scale-free networks section that I feel I can apply to work.

Bottom line, if Professor Downey ever opens an online class for "Think Complexity" either synchronized or on UDemy I would sign up in a heartbeat. Cheers.
19 people found this helpful
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Marc
5.0 out of 5 stars Fantastic Python text
Reviewed in the United States on March 2, 2017
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Fantastic Python text. Author writes in a very approachable style which is refreshing to read. He does not make it a chore to learn, and as an experienced programmer it was fairly easy to skim to find the more personally relevant sections.

I have been tutoring a friend whose course is supplemented by the author's previous book,  Think Python: How to Think Like a Computer Scientist , and I will be trying to convince the dept. staff to use this book for their Algorithm course now that they've switched their core curriculum to Python since this book does such a thorough job explaining many concepts in an easy to follow way.
3 people found this helpful
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Matthew B. Doar
5.0 out of 5 stars An excellent book, nice and meaty with a range of flavors
Reviewed in the United States on March 20, 2012
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The author covers many of the emerging ideas that are grouped under the term "Complexity Science".
The Python code is solid and helps both explain and implement the ideas in the book.
Yet it's thin enough to read all of the book!

I personally enjoyed it as a good summary of areas that I've dug into over the past twenty years, but with lots of encouragement to keep exploring.
4 people found this helpful
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Matthew Michael Funcheon
5.0 out of 5 stars Fast delivery. Item as described
Reviewed in the United States on June 6, 2019
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Fast delivery. Item as described
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Rajib
5.0 out of 5 stars Five Stars
Reviewed in India on March 16, 2015
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Great book on python and it's use to understand the topic of complexity
One person found this helpful
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Theodor Fabel
5.0 out of 5 stars Regt zum mitarbeiten an
Reviewed in Germany on November 9, 2014
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Endlich ein Buch, das nicht mit Codebeispielen angefüllt ist sondern mit Quellenangaben und guter Beschreibung den Leser zum Lösen von technischen / mathematischen / ökonomischen Aufgabenstellungen einlädt. Nebenbei werden noch Prinzipen von Python erklärt und falls man Schwierigkeiten beim Lösen hat, gibt es Links zu Lösungsvarianten. Ebenso werden praktische Libraries für Python verwendet, so kann man diese auf in seine eigenen Projekte einbinden. Ein sehr gelungenes Buch, allerdings nichts für Programmieranfänger, Grundkenntnisse in Python sind ebenfalls notwendig!
3 people found this helpful
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Amazon Customer
5.0 out of 5 stars Critical Mass : Applied
Reviewed in the United Kingdom on April 28, 2013
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In an airport waiting for another long-haul flight, I bought "Critical Mass" by Philip Ball to get some intellectual stimulation. It gave a me a completely new view of the world: A world of complexity, evolution & emergence appeared out of the blue in the 8 hour flight. I landed with a sharp contrast in mind from the reductionist engineering background I left another continent with.
I caught myself standing in the border control queue, waving the hot air from my face with my passport, wondering: How do I apply these concepts & ideas?

Two and a half years later I know how, thanks to Allan Downey's Think Complexity. A book that takes your through a supremely structured self-learning program of getting the concepts of complex systems under your skin.

Be warned, when you start with his other books (learning python, think stats, etc.) there is no indication of the density of knowledge in this book. It is a complete course in engineering in pocket-format. It is brilliantly assembled, but don't be deceived by it's number of pages: Completing the exercises in the book will be time-consuming and it will change the way you solve problems.

I would proudly recommend it any-time to an enthusiastic Python'eer, but also warn that it is not for the faint-hearted casual reader.

Brillant assembly Allan. Thank you..!
6 people found this helpful
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R. Sampathkumar
2.0 out of 5 stars A little disappointed.
Reviewed in India on January 8, 2017
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Expected more in depth information on the topics discussed. A little disappointed.
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Susan Stepney
3.0 out of 5 stars and found David Beazley’s excellent tutorials, one on Python generators
Reviewed in the United Kingdom on August 9, 2014
Verified Purchase
I am teaching myself Python. Not from scratch, because I can already program in other languages. But that’s part of the problem: because I know how to program, I am learning Python from the on-line documentation (which could be better) and Stack Overflow (which in invaluable). This means I can find the constructs I look for; but what about the ones I don’t know exist?

So I’ve been thinking about getting a book, to help fill the gaps. I came across Think Complexity, a slim book (130pp) that claims to be targeted at an intermediate level, with the bonus of using examples from Complexity Science, a subject I also study.

It starts off well, with a mention of Python generators (which I had come across as a concept) and their “yield” statement (which was new to me). Yet the discussion is very brief: less than a page. I wanted to know more, as it sounded interesting, and made me wonder if the approach would allow coroutines. So I googled, and found David Beazley’s excellent tutorials, one on Python generators, using them in a functional manner to implement processing pipelines, and one, indeed, on coroutines. There is a lot more than even hinted at in Think Python.

Next comes a chapter on algorithmic complexity and “big O” notation that introduces Python list comprehensions. Now, it’s virtually impossible to have visited Stack Overflow more than a few times without having come across list comprehensions: they are marvellous beasts. However, their introduction here crystallised my apprehension with the book: they are explained with just a few examples only. Examples can be great for showing what is possible, and the examples here are good in that they start trivial and get more complicated. But you also need a description of the underlying syntax, so that you know that you have inferred the structure correctly from the examples, and to cover usages not illustrated by the examples.

The chapter on Cellular Automata uses NumPy arrays, but doesn’t talk about them much. NumPy is excellent for doing anything with arrays, and if you have come to Python via Matlab, like I have, you will feel right at home with them. One interesting snippet made here is an efficient way to implement Conway’s Game of Life using convolution from SciPy (although Bill Gosper’s HashLife, underlying Golly, is faster, and more interesting algorithmically).

Then there are brief chapters on fractals, self-organised criticality, and agent-based models. But not a lot more Python. The book finishes up with several case studies prepared by students following up some of the concepts in the book; these are probably the most interesting parts. However, they are interesting mainly from a complexity viewpoint, not really from a Python viewpoint.

In summary, although this is advertised as “intermediate level” Python, it doesn’t go very far beyond what you can pick up readily from Stack Overflow. However, the idea of teaching a programming course using fun examples from Complexity Science is a good one: so many texts use relatively boring examples with little motivation. It is clear here from the chapters contributed by the students that they really engaged with the material.
3 people found this helpful
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