The course explores how nature's structure reflects how nature computes. It introduces intrinsic unpredictability (deterministic chaos) and the emergence of structure (self-organization) in natural complex systems. Using statistical mechanics, information theory, and computation theory, the course will develop a systematic framework for analyzing processes in terms of their causal architecture. This is determined by answering three questions: (i) How much historical information does a process store? (ii) How is that information stored? And (iii) how is the stored information used to produce future behavior? The answers to these questions tell one how a system intrinsically computes.

The course will develop tools to describe and quantify randomness and structure. It will show how they are necessarily complementary and how they are intimately related to concepts from the theory of computation. A number of example complex systems—taken from physics, chemistry, and biology—will be used to illustrate the phenomena and methods. The course will also take time to reflect on the intellectual history of these topics, which is quite rich and touches on many basic questions in fundamental physics and the sciences and technology generally. New topics this year include complex materials and computation in quantum systems. The course will bring students to the research frontier in nonlinear physics and complex systems.