Think Complexity: Complexity Science and Computational Modeling

Dive into Python’s advanced possibilities, including algorithm analysis, graphs, scale-free networks, and cellular automata with this in-depth, hands-on guide. Whether you’re an intermediate-level Python programmer, or a student of computational modeling, you’ll examine data structures, complexity science, and other fascinating topics through a series of exercises, easy-to-understand explanations, and case studies.

Think Complexity presents features that make Python such a simple and powerful language. Author Allen Downey provides code to help you get started, along with a solution for each exercise. With this book, you will:

• Work with graphs and graph algorithms, NumPy arrays and SciPy methods, basic signal processing and Fast Fourier Transform, and hash tables.
• Discover complexity science, the field that studies abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines.
• Explore the philosophy of science through the models and results in this book about the nature of scientific laws, theory choice, and realism and instrumentalism, and more.

Table of Contents

Chapter 1. Complexity Science
Chapter 2. Graphs
Chapter 3. Analysis of Algorithms
Chapter 4. Small World Graphs
Chapter 5. Scale-Free Networks
Chapter 6. Cellular Automata
Chapter 7. Game of Life
Chapter 8. Fractals
Chapter 9. Self-Organized Criticality
Chapter 10. Agent-Based Models
Chapter 11. Case Study: Sugarscape
Chapter 12. Case Study: Ant Trails
Chapter 13. Case Study: Directed Graphs and Knots
Chapter 14. Case Study: The Volunteer’s Dilemma

Appendix A. Call for Submissions
Appendix B. Reading List