References

Prepared by Steve Railsback, LRA@Northcoast.com

Complex Adaptive Systems

Waldrop, M. M. (1992). Complexity: The Emerging Science at the Edge of Order and Chaos. New York, Simon & Schuster. A popular book introducing the complexity approach and portraying the scientists that founded the Santa Fe Institute.
Holland, J. H. (1995). Hidden Order: How Adaptation Builds Complexity. Reading, Massachusetts, Perseus Books.
Holland, J. H. (1998). Emergence: From Chaos to Order. Reading, Massachusetts, Helix Books.
Two books providing useful examples of what makes complex systems intelligent and life-like, based mainly on computer simulation models.
Kauffman, S. (1995). At Home in the Universe: The Search for the Laws of Self-Organization and Complexity. New York, Oxford University Press. Discussion of the potential importance of complexity phenomenon in the development of life and intelligence; an evolutionary and biological perspective.
Auyang, S. Y., 1998. Foundations of Complex-system Theories in Economics, Evolutionary Biology, and Statistical Physics. Cambridge University Press. A dense, thorough presentation of methods for understanding complex adaptive systems, including approaches for understanding links between traits of individuals and the emergent system dynamics. Essential for individual-based modelers.

The on-line home of artificial life is: http://alife.org/. This site includes a comprehensive set of links, software, and literature. The ALife 7 conference was just held at Reed College, Portland, OR. http://alife7.alife.org/.
Adami, C. 1998. Introduction to Artificial Life (book and CD). Springer-Verlag, New York. Developed for the author's class at Cal Tech. Discussion of computer simulation to investigate general principles of living systems. Described at: http://www.telospub.com/catalog/PHYSICS/ALife.html.

Clark, C. W. and M. Mangel (2000). Dynamic state variable models in ecology. New York, Oxford University Press. Does not address individual-based modeling but presents an approach to simulating habitat selection that is a useful foundation for individual-based decision making methods.
Bull, C. D., N. B. Metcalfe and M. Mangel (1996). Seasonal matching of foraging to anticipated energy requirements in anorexic juvenile salmon. Proceedings of the Royal Society of London B 263: 13-18.
Thorpe, J. E., M. Mangel, N. B. Metcalfe and F. A. Huntingford (1998). Modelling the proximate basis of salmonid life-history variation, with application to Atlantic salmon, Salmo salar L. Evolutionary Ecology 12: 581-599.
Grand, T. C. (1999). Risk-taking behavior and the timing of life history events: consequences of body size and season. Oikos 85(3): 467-480.
Papers providing support for state-based decision-making approaches that can be adapted for IBMs.

Huston, M., D. DeAngelis and W. Post (1988). New computer models unify ecological theory. BioScience 38(10): 682-691. A classic reference on the potential value of individual-based modeling.
Grimm, V. (1999). Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future? Ecological Modelling 115: 129-148.
Grimm, V., T. Wyszomirski, D. Aikman and J. Uchmanski (1999). Individual-based modelling and ecological theory: synthesis of a workshop. Ecological Modelling 115: 275-282.

Railsback, S. F., R. H. Lamberson, B. C. Harvey and W. E. Duffy (1999). Movement rules for spatially explicit individual-based models of stream fish. Ecological Modelling 123(2-3): 73-89.
Railsback, S. F. and B. C. Harvey (In prep.). Comparison of salmonid habitat selection objectives in an individual-based model.
- The state-based, predictive method for simulating habitat selection in fish is described in the first paper and tested in comparison to conventional approaches in the second paper. The second paper also provides an example of pattern-oriented analysis of an IBM to test specific model components and to understand links between individual traits and population behavior.
Railsback, S. F. (In prep.). Concepts from Complex Adaptive Systems as a framework for individual-based modeling. In review at Ecological Modelling. Six concepts from complexity research presented as a conceptual basis for ecological IBMs.
Railsback, S. F. (In press). Getting "results": the pattern-oriented approach to analyzing natural systems with individual-based models. Natural Resource Modeling. A method for testing and understanding IBMs, with comparison to conventional alternatives.

Lorek, H. and M. Sonnenschein (1999). Modelling and simulation software to support individual-based ecological modelling. Ecological Modelling. 115: 199-216. Discussion of the limitations of "from scratch" software, and three alternative general approaches. (The Wesp simulation library described in this paper is no longer under development.)
Minar, N., R. Burkhart, C. Langton and M. Askenazi (1996). The Swarm simulation system: A toolkit for building multi-agent simulations. Working Paper 96-06-042, Santa Fe, Santa Fe Institute. Available at: www.santafe.edu/projects/swarm/swarmdoc/overview.html. An overview of Swarm and discussion of potential pitfalls in software development for agent-based models.
The Swarm simulation system is at: www.swarm.org. This site allows downloading of documentation, tutorials, and software; and provides links to many projects using Swarm. (There is also a small but growing number of alternative packages for agent-based simulation; search the web.)
Software for fish IBMs developed by the Humboldt State University team is described at: http://www.humboldt.edu/~ecomodel . The site also contains information on conceptual issues and a number of model applications.