This book is primarily about complexity science, but it is also about data structures and algorithms, intermediate programming in Python, computational modeling, and the philosophy of science:

• Complexity science: Complexity is an interdisciplinary field—at the intersection of mathematics, computer science and natural science—that focuses on discrete models of physical and social systems. In particular, it focuses on complex systems, which are systems with many interacting components.
• Data structures: A data structure is a collection of data elements organized in a way that supports particular operations. For example, a Python dictionary organizes key-value pairs in a way that provides fast mapping from keys to values, but mapping from values to keys is slower.
• Algorithms: An algorithm is a process for performing a computation. Designing efficient programs often involves the co-evolution of data structures and the algorithms that use them. For example, in the first few chapters I present graphs, data structures that implement graphs, and graph algorithms based on those data structures.
• Computational modeling: A model is a simplified description of a system used for simulation or analysis. Computational models are designed to take advantage of cheap, fast computation.
• Philosophy of science: The experiments and results in this book raise questions relevant to the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, holism and reductionism, and epistemology.