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Understanding the Emergence of Population Behavior in Individual-Based Models

Published online by Cambridge University Press:  01 January 2022

Abstract

Proponents of individual-based modeling in ecology claim that their models explain the emergence of population-level behavior. This article argues that individual-based models have not, as yet, provided such explanations. Instead, individual-based models can and do demonstrate and explain the emergence of population-level behaviors from individual behaviors and interactions.

Type
Biological Sciences
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

Many thanks to Matt Bateman, Brett Calcott, Josh Epstein, Peter Godfrey-Smith, Steve Kimbrough, Arnon Levy, Ian Lustick, Emily Parke, Joan Roughgarden, Dmitri Tymoczko, and Bill Wimsatt for helpful discussions. This research was supported, in part, by National Science Foundation grant SES-0957189.

References

Craver, C. F., and Bechtel, W.. 2007. “Top-Down Causation without Top-Down Causes.” Biology and Philosophy 22 (4):547–63.10.1007/s10539-006-9028-8CrossRefGoogle Scholar
Dewar, R. C., and Porté, A.. 2008. “Statistical Mechanics Unifies Different Ecological Patterns.” Journal of Theoretical Biology 251 (3): 389403.10.1016/j.jtbi.2007.12.007CrossRefGoogle ScholarPubMed
Epstein, J. M. 2006. Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton, NJ: Princeton University Press.Google Scholar
Grimm, V., and Railsback, S. F.. 2005. Individual-Based Modeling and Ecology. Princeton, NJ: Princeton University Press.10.1515/9781400850624CrossRefGoogle Scholar
Humphreys, P. 1997. “Emergence, Not Supervenience.” Philosophy of Science 64 (Proceedings): S337S345.10.1086/392612CrossRefGoogle Scholar
McQuarrie, D. D. A., and Simon, J. J. D.. 1997. Physical Chemistry: A Molecular Approach. Sausalito, CA: University Science.Google Scholar
Nagel, E. 1961. The Structure of Science: Problems in the Logic of Scientific Explanation. New York: Harcourt, Brace & World.10.1119/1.1937571CrossRefGoogle Scholar
Pearl, J. 2000. Causality: Models, Reasoning, and Inference. Cambridge: Cambridge University Press.Google Scholar
Rademacher, C., Neuert, C., Grundmann, V., Wissel, C., and Grimm, V.. 2004. “Reconstructing Spatiotemporal Dynamics of Central European Natural Beech Forests: The Rule-Based Forest Model Before.” Forest Ecology and Management 194 (1): 349–68.10.1016/j.foreco.2004.02.022CrossRefGoogle Scholar
Railsback, S. F., and Grimm, V.. 2011. Agent-Based and Individual–Based Modeling: A Practical Introduction. Princeton, NJ: Princeton University Press.Google Scholar
Reynolds, C. W. 1987. “Flocks, Herds, and Schools: A Distributed Behavioral Model.” In SIGGRAPH ’87: Conference Proceedings, July 27–31, 1987, Anaheim, California, ed. Stone, Maureen C., 2534. New York: Association for Computing Machinery.10.1145/37401.37406CrossRefGoogle Scholar
Sklar, L. 1993. Physics and Chance: Philosophical Issues in the Foundations of Statistical Mechanics. Cambridge: Cambridge University Press.10.1017/CBO9780511624933CrossRefGoogle Scholar
Spirtes, P., Glymour, C. N., and Scheines, R.. 2000. Causation, Prediction, and Search. 2nd ed. Vol. 81. Cambridge, MA: MIT Press.Google Scholar
Wilensky, U. 1998. NetLogo Flocking Model. Center for Connected Learning and Computer-Based Modeling, Northwestern University. http://ccl.northwestern.edu/netlogo/hubnet.html.Google Scholar
Wimsatt, W. C. 1997. “Aggregativity: Reductive Heuristics for Finding Emergence.” Philosophy of Science 64 (4): 372–84.10.1086/392615CrossRefGoogle Scholar