Read more about Think Complexity: Exploring Complexity Science with Python

Think Complexity: Exploring Complexity Science with Python

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Allen B. Downey, Franklin W. Olin College of Engineering

Copyright Year: 2012

ISBN 13: 9781449314637

Publisher: Green Tea Press

Language: English

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Reviewed by Stephen Davies, Associate Professor, University of Mary Washington on 5/13/19

It's difficult to be truly "comprehensive" in the field of Complexity, since it is so broad and so (relatively) new. An introductory text would do best to give the flavor of the field and enough interesting applications to pique the reader's... read more

Table of Contents

  • Preface
  • 1 Complexity Science
  • 2 Graphs
  • 3 Analysis of algorithms
  • 4 Small world graphs
  • 5 Scale-free networks
  • 6 Cellular Automata
  • 7 Game of Life
  • 8 Fractals
  • 9 Self-organized criticality
  • 10 Agent-based models
  • 11 Case study: Sugarscape
  • 12 Case study: Ant trails
  • 13 Case study: Directed graphs and knots
  • 14 Case study: The Volunteer's Dilemma

About the Book

This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science:

  • Data structures and algorithms: A data structure is a collection that contains data elements organized in a way that supports particular operations. For example, a dictionary organizes key-value pairs in a way that provides fast mapping from keys to values, but mapping from values to keys is generally slower.
  • An algorithm is a mechanical process for performing a computation. Designing efficient programs often involves the co-evolution of data structures and the algorithms that use them. For example, the first few chapters are about graphs, a data structure that is a good implementation of a graph---nested dictionaries---and several graph algorithms that use this data structure.
  • Python programming: This book picks up where Think Python leaves off. I assume that you have read that book or have equivalent knowledge of Python. As always, I will try to emphasize fundmental ideas that apply to programming in many languages, but along the way you will learn some useful features that are specific to Python.
  • Computational modeling: A model is a simplified description of a system that is useful for simulation or analysis. Computational models are designed to take advantage of cheap, fast computation.
  • Philosophy of science: The models and results in this book raise a number of questions relevant to the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, holism and reductionism, and Bayesian epistemology.

This book focuses on discrete models, which include graphs, cellular automata, and agent-based models. They are often characterized by structure, rules and transitions rather than by equations. They tend to be more abstract than continuous models; in some cases there is no direct correspondence between the model and a physical system.

Complexity science is an interdisciplinary field---at the intersection of mathematics, computer science and physics---that focuses on these kinds of models. That's what this book is about.

About the Contributors


Allen B. Downey is an American computer scientist, Professor of Computer Science at the Franklin W. Olin College of Engineering and writer of free textbooks.

Downey received in 1989 his BS and in 1990 his MA, both in Civil Engineering from the Massachusetts Institute of Technology, and his PhD in Computer Science from the University of California at Berkeley in 1997.

He started his career as Research Fellow in the San Diego Supercomputer Center in 1995. In 1997 he became Assistant Professor of Computer Science at Colby College, and in 2000 at Wellesley College. He was Research Fellow at Boston University in 2002 and Professor of Computer Science at the Franklin W. Olin College of Engineering since 2003. In 2009-2010 he was also Visiting Scientist at Google Inc.