Complexity Explorer

Video Zip Files:

Lecture Slides:

NetLogo:

Unit 1 Models:

Unit 2 Models:

Unit 3 Models:

Unit 4 Models:

Unit 5 Models:

RobbyGA.nlogo
CommonSelectionMethods.pdf (for Advanced-Level Homework)

Unit 6 Models:

Mini-Life.nlogo (note that this version does not show the "green" updates shown in the vidoes)
GameOfLife.nlogo
ElementaryCAs.nlogo
Link to Edge of Chaos Applet

Unit 7 Models:

Note that the Flocking, Fireflies, and Ants NetLogo models are in the Models Library: go to the Biology section.
FirefliesWithEntropy.nlogo
FlockingWithEntropy.nlogo

Unit 8 Models:

PD-Two-Person-Iterated.nlogo

Unit 9 Models:

SmallWorldNetworks.nlogo
SmallWorldNetworksMacOS10.8.nlogo (the version above doesn't work on OS 10.8; instead use this one)
preferential-attachment.nlogo (from the NetLogo Models Library)
NetworkBuilder.nlogo
RandomNetworks.nlogo
NetworkAttachmentModels.nlogo
ZipfsLaw.nlogo

Unit 10

PowerLawCalculator.nlogo

Optional Readings:

General:

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

L. Kadanoff, Chaos: A view of complexity in the physical sciences. In From Order to Chaos: Essays: Critical, Chaotic, and Otherwise, 1993. A relatively non-technical discussion of the logistic map, and other issues in the study of chaos.
M. Feigenbaum, Universal behavior in nonlinear systems. Los Alamos Science, 1, 1980, pp. 4-27. A more thorough and technical, though very readable, discussion of the logistic map and related systems by the scientist who made many of the discoveries described here.
J. Garland and L. Bradley, On the importance of nonlinear modeling in computer performance prediction

Unit 3: Fractals

Fractal Explorer. An on-line book and other resources for learning about fractals.
B. Mandelbrot, The Fractal Geometry of Nature, W.H. Freeman and Co. New York (1983).
N. Lesmoir-Gordon and R. Edney, et al. Introducing Fractal Geometry, Icon Books Ltd. (2000).
M. Schroeder, Fractals, Chaos, Power Laws. W. H. Freeman and Co. (1991)
C. Brown and L. Liebovitch, Fractal Analysis, Series: Quantitative Applications in the Social Sciences, Sage Publications Inc. (2010).

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, 1st Edition. 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

J. Conway, "What is Life?" Chapter 25 in Berlekamp, E. R.; Conway, J. H.; and Guy, R. K., Winning Ways for Your Mathematical Plays, Vol. 2: Games in Particular
W. Poundstone, The Recursive Universe: Cosmic Complexity and the Limits of Scientific Knowledge. An entertaining and enlightening popular science book that tackles big questions about the universe by looking at Conway's Game of Life.
Elementary Cellular Automaton from mathworld.wolfram.com
Website for A New Kind of Science

M. Mitchell, Review of A New Kind of Science
M. Mitchell, Computation in Cellular Automata: A Selected Review
Mitchell, M., Crutchfield, J. P., and Das, R. Evolving cellular automata to perform computations: A review of recent work

Unit 7: Models of Self-Organization

S. Camazine et al., Self-Organization in Biological Systems. Princeton University Press, 2001.
S. Strogatz, Sync: How Order Emerges From Chaos In the Universe, Nature, and Daily Life. Hyperion, 2003.
E. Yong, How the science of swarms can help us fight cancer and predict the future. Wired, 03.19.13.
C. W. Reynolds, Flocks, herds, and schools: A distributed behavioral model. ACM SIGGRAPH Computer Graphics, 1987.
D. M. Gordon, The regulation of foraging activity in Red Harvester ant colonies.
D. M. Gordon, Interaction patterns and task allocation in ant colonies.

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

R. Axlerod, The Evolution of Cooperation. New York: Basic Books, 1984.
R. Axelrod, The Complexity of Cooperation. Princeton University Press, 1997.
M. Nowak, Five rules for the evolution of cooperation.
B. Hayes, New dilemmas for the prisoner. American Scientist
T. C. Schelling, Dynamic models of segregation.
B. Hayes, The math of segregation.
W. B. Arthur, Inductive reasoning and bounded rationality (the El Farol problem).
W. B. Arthur, Complexity economics: A different framework for economic thought.
J. D. Farmer, Economics needs to treat the economy as a complex system.

Unit 9: Networks

D. J. Watts and S. H. Strogatz, Collective dynamics of 'small-world' networks.
A. L. Barabasi and R. Albert, Emergence of scaling in random networks.
M. E. J. Newman, Networks: An Introduction.
M. E. J. Newman, Structure and function of complex networks.
D. Easley and J. Kleinberg, Networks, Crowds, and Markets.
D. J. Watts, Six degrees: The science of a connected age.
A. L Barabasi, Linked: The new science of networks.
D. J. Watts, "Too complex to exist", Boston Globe, June 14, 2009.

Unit 10: Scaling in Biology and Society

M. E. J. Newman, Power laws, Pareto distributions, and Zipf's law.
G. B. West and J. H. Brown, The origin of allometric scaling laws in biology from genomes to ecosystems: Towards a quantitative unifying theory of biological structure and organization.
G. B. West et al., The fourth dimension of life: Fractal geometry and allometric scaling of organisms.
P. S. Agutter and D. N. Wheatley, Metabolic scaling: Consensus or controversy?
Mathbench, The 3/4 Law (Very nice, relatively nontechnical overview)
L. M. A. Bettencourt et al., Growth, innovation, scaling, and the pace of life in cities.
L. M. A. Bettencourt, The origins of scaling in cities.

Complexity Explorer Santa Few Institute

Introduction to Complexity (Fall, 2014)