Complexity Explorer Santa Fe Institute

This course is no longer in session.

Introduction to Complexity (Spring 2018 - Spring 2019)

Lead instructor:

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Unit 2 Models:

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Unit 10

Optional Readings:


  • M. Mitchell, Complexity:  A Guided Tour.  Parts of the course will roughly follow this book.  The book is a useful companion to the course, but is not required for taking this course.

Unit 1: What is Complexity?

  • W. Weaver, Science and complexity. American Scientist, 36: 536-544, 1948.   A classic article by an influential 20th century scientists/mathematician, on why science should focus on complex systems. 
  • S. Lloyd, Measures of complexity: A non-exhaustive list. IEEE Control Systems Magazine, 7-8, August, 2001.   A list of some mathematical measures of complexity, though without much explanation of what they mean.

Unit 2: Dynamics and Chaos

Unit 3: Fractals

Unit 4: Information, Order, and Randomness

  • Shannon, C.E. (1948), A Mathematical Theory of Communication, Bell System Technical Journal, 27, pp. 379–423 & 623–656, July & October, 1948.  Shannon's original article is tough going in places but is available online here.
  • R.V.L. Hartley, Transmission of Information, Bell System Technical Journal, July 1928
  • J. L. Lebowitz, Boltzmann's entropy and time's arrow. A semi-popular article for people who want to read further about the issues in Unit 4.
  • S. Carroll, From Eternity to Here: The Quest for the Ultimate Theory of Time. A fascinating (if long) book that will bring you up-to-date on current views in physics about the nature of time.
  • T. D. Schneider, Information Theory Primer. A nice, brief primer on Shannon information; readable if you are comfortable with exponents, logarithms, summation signs, and such. Geared towards biologists, so uses genetics as an example.
  • James Gleick, The Information: A History, a Theory, a Flood, New York: Pantheon, 2011.  For general audiences.
  • Thomas M. Cover, Joy A. Thomas. Elements of information theory. New York: Wiley-Interscience, 1991.  A more advanced textbook on information theory.

Unit 5: Genetic Algorithms

  • There are many excellent online tutorials on genetic algorithms, and several good free software packages.
  • Chapter 9 in Complexity:  A Guided Tour, covers some of the same material covered in the lectures for this unit.
  • Robby the Robot code in C.
  • Link to Karl Sims' papers on evolving computer graphics and virtual creatures.
  • J. H. Holland.  Adaptation in Natural and Artificial Systems.   MIT Press, 1992.  John Holland's classic book, originally published in 1975, that sets out the theoretical basis for genetic algorithms. 
  • K. De Jong, Evolutionary Computation.  MIT Press, 2002.  Somewhat technical textbook on genetic algorithms and other evolutionary computation techniques.
  • M. Mitchell, An Introduction to Genetic Algorithms.  MIT Press, 1996.  Older, fairly short textbook on genetic algorithms

Unit 6: Cellular Automata

Unit 7: Models of Self-Organization

Unit 8: Models of Self-Organization and Cooperation in Social Systems

Unit 9: Networks

Unit 10: Scaling in Biology and Society