Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-25T07:45:57.208Z Has data issue: false hasContentIssue false

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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