Think Complexity: Complexity Science and Computational Modeling

Think Complexity: Complexity Science and Computational Modeling

<div><p>Complexity science uses computation to explore the physical and social sciences. In <i>Think Complexity</i>, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.</p><p>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.</p><p>In this updated second edition, you will:</p><ul><li>Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform</li><li>Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines</li><li>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</li><li>Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism</li></ul><p>Ideal as a text for a course on computational modeling in Python, <i>Think Complexity</i> also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.</p></div>