Think Complexity: Complexity Science and Computational Modeling by Allen B. Downey delves into the fascinating world of complexity science through practical Python programming. This second edition, published on August 28, 2018, by O’Reilly Media, invites readers to explore complex physical, biological, and economic systems using computational models and simulations.

The book is designed for intermediate-level Python programmers or those studying computational modeling, providing a series of worked examples, exercises, case studies, and clear explanations. Key topics include the use of NumPy arrays and SciPy methods for basic signal processing and Fast Fourier Transform techniques, as well as abstract models such as power laws, fractals, and pink noise. Additionally, readers will engage with Jupyter notebooks filled with starter code and solutions to re-implement and extend original experiments in complexity science.

Further, the book explores philosophical questions in science, including discussions on the nature of scientific laws, theory choice, realism versus instrumentalism, and models of computation like Turmites, Turing machines, and cellular automata. With 198 pages of content, this edition offers a comprehensive guide to understanding complexity through computational modeling.