CSSS 2008 Santa Fe-Readings
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CSSS Santa Fe 2008 |
Contents
Week One: Modeling/Nonlinear Dynamics
Liz Bradley: Introduction to Nonlinear Dynamics
Nonlinear Dynamics
- Syllabus
- Numerical Solution of Differential Equations: Notes for CSCI3656
- Time Series Analysis
- Slides for Lecture 1
- Slides for Lecture 2
- Slides for Lecture 3
- Slides for Lecture 4
Owen Densmore & Steve Guerin: Modeling
Before the modeling class (afternoon the first day!) you should:
- Download the most recent versions of both NetLogo and NetLogo 3D from http://ccl.northwestern.edu/netlogo/
- Run some of the Model Library examples for both NetLogo and NetLogo 3D:
- Start the application
- Click Model Library in the File menu, try these:
- NetLogo: Art > Diffusion Graphics
- NetLogo 3D: 3D > Sample Models > Raindrops 3D
Note: move the 3D raindrop world by click & drag - To run most of the models, click "Setup" then "Go"
- To see the code, click on the Procedures tab
- Start the application
- Finally, under the "Help" menu, click "User Manual" and explore!
Note: the url discussed in class is: http://backspaces.net/csss08 which contains the models.zip file, which in turn contains the commandcenter.rtf file we used to build our model. CSSS08Day1.pdf is the slide set used for the first half of the class.
Josh Epstein: Modeling in the Social Sciences
- Modeling Civil Violence: An Agent-based Computational Approach. Epstein. PNAS 99:3, 7243-7250 (2002).
- Controlling Pandemic Flu: The Value of International Air Restrictions. Epstein, Goedecke, Yu, Wagener, and Bobashev. PLOS One 5, 1-11 (2007).
- Population Growth and Collapse in a Multiagent Model of the Kayenta Anasazi in Long House Valley. Axtell, Epstein, Dean, Gumerman, Swedlund, Harburger, Chakravarty, Hammond, Parker, and Parker. PNAS 99:3, 7275-7279 (2002).
- Coupled Contagion Dynamics of Fear and Disease: Mathematical and Computational Explorations. Epstein, Parker, Cummings, and Hammond. SFI Working Paper 07-12-048 (2007).
Slide Presentations:
- Social Sciences Modeling I. Epstein. 06-03-08.
- Social Sciences Modeling II. Epstein. 06-04-08.
- Social Sciences Modeling III. Epstein. 06-05-08.
David Krakauer
Here is a link to the pdf of David's Lecture on Evolutionary Dynamics
Mark Newman
Here is a link to the pdf of Mark's 4 lectures on Networks
Tom Carter
Here is a link to a page with various background readings -- I'll be talking about some of this material, watch the wiki for days/times
Week Two: Ecology/Evolution/Molecular Biology/Disordered Systems
Aaron Clauset: MCMC for Simulation and Inference
Here is a pdf copy of Aaron's lecture notes
These three references cover a wide variety of details related to Markov chain Monte Carlo (MCMC) methods. The second and third are general references, written for physics and machine learning audiences. (The Newman and Barkema book should be available through the SFI Library.) The first shows an application of MCMC methods in the context of learning the large-scale structure of networks.
- "Hierarchical structure and the prediction of missing links in networks." Clauset, Moore and Newman. Nature 453, 98-101 (2008).
- Monte Carlo Methods in Statistical Physics. Newman and Barkema. Oxford University Press (1999).
- "An Introduction to MCMC for Machine Learning." Andrieu, de Freitas, Doucet and Jordan. Machine Learning 50, 5-43 (2003).
Jennifer Dunne: Foodwebs
Jennifer Dunne is a Research Fellow at the Santa Fe Institute, and is Co-Director of the Pacific Ecoinformatics and Computational Ecology Lab in Berkeley, CA. Dr. Dunne will lecture on ecological network structure and recommends the following article (Williams and Martinez, 2000) as a starting point for learning about such research. Her lectures will use this Nature paper as a jumping off point for discussing recent advances in research on food-web topology and robustness.
Williams and Martinez, 2000 Nature [1]
For more info about ecological networks, go to http://www.foodwebs.org
08-SFI CSSS Dunne 1 & 2 PowerPoint
08-SFI CSSS Martinez 1 PowerPoint
08-SFI CSSS Martinez 2 PowerPoint
FoodWeb3D Movie
Dan Stein: Quenched Disorder, Spin Glasses, and Complexity
This course is designed to introduce the participant to the study of systems with quenched disorder, which are fascinating systems in their own right but which also helped introduce many of the ideas and concepts that have become central to complexity studies. These ideas have found applications to problems from fields as diverse as biology, computer science, and economics, and we will explore some of these as well.
The course presupposes no prior knowledge of physics or statistical mechanics, and math will be kept to a minimum. If you'd like a flavor of some of the things we'll be discussing, you can take a look at D.L. Stein, ``Spin Glasses, Scientific American v. 261, pp. 52--59 (1989). Despite the passage of time, many of the issues and questions discussed in that article remain open!
For those who would like to access the subject on a more technical level (which is unnecessary for this course), here are some references:
K. Binder and A.P. Young, ``Spin Glasses, Rev. Mod. Phys. v. 58, p. 801 (1986).
M. Mezard, G. Parisi, and M. Virasoro, ``Spin Glass Theory and Beyond (World Scientific, 1986).
Spin Glasses and Biology, edited by D.L. Stein (World Scientific Publishing Co., Singapore, 1992)
J.A. Hertz and K.H. Fischer, ``Spin Glasses (Cambridge, 1989).
C.M. Newman and D.L. Stein, ``Topical Review: Ordering and Broken Symmetry in Short-Ranged Spin Glasses, Journal of Physics: Condensed Matter 15, R1319--R1364 (2003).
The last of these can be accessed from my Web page
Jon Wilkins: Adaptationism and the Adaptive Landscape
Here is a ppt copy of Jon's lecture notes - related to genomic imprinting, notions of optimality in evolution: [2]
Week Three: Econ/Finance/"AI"
Melanie Mitchell: Evolutionary Computing
Mitchell, M. (2001). Life and evolution in computers. History and Philosophy of the Life Sciences, 23, 361-383. [3]
Mitchell, M (2006). Coevolutionary learning with spatially distributed populations. In G. Y. Yen and D. B. Fogel (editors), Computational Intelligence: Principles and Practice . New York: IEEE Computational Intelligence Society. [4]