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Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you\'ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.
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 worked examples, exercises, case studies, and easy-to-understand explanations.
Work with
NumPy arrays and SciPy methods, including
basic signal processing and Fast Fourier Transform; Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines; Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata; Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism.
Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.
