Idealized, inaccurate but successful: A pragmatic approach to evaluating models in theoretical ecology [Book Review]
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Biology and Philosophy 20 (2-3):231-255 (2005)
Ecologists attempt to understand the diversity of life with mathematical models. Often, mathematical models contain simplifying idealizations designed to cope with the blooming, buzzing confusion of the natural world. This strategy frequently issues in models whose predictions are inaccurate. Critics of theoretical ecology argue that only predictively accurate models are successful and contribute to the applied work of conservation biologists. Hence, they think that much of the mathematical work of ecologists is poor science. Against this view, I argue that model building is successful even when models are predictively inaccurate for at least three reasons: models allow scientists to explore the possible behaviors of ecological systems; models give scientists simplified means by which they can investigate more complex systems by determining how the more complex system deviates from the simpler model; and models give scientists conceptual frameworks through which they can conduct experiments and fieldwork. Critics often mistake the purposes of model building, and once we recognize this, we can see their complaints are unjustified. Even though models in ecology are not always accurate in their assumptions and predictions, they still contribute to successful science.
|Keywords||Accuracy Ecology Heuristic Idealization Mathematics Model Pragmatism Prediction Theory|
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References found in this work BETA
Ronald N. Giere (1999). Science Without Laws. University of Chicago Press.
Peter Godfrey-Smith (2003). Theory and Reality: An Introduction to the Philosophy of Science. University of Chicago Press.
K. S. Shrader-Frechette (1993). Method in Ecology: Strategies for Conservation. Cambridge University Press.
J. Dupre (1996). Concepts and Methods in Evolutionary Biology. Monograph Collection (Matt - Pseudo).
Citations of this work BETA
Collin Rice (2015). Moving Beyond Causes: Optimality Models and Scientific Explanation. Noûs 49 (3):589-615.
J. Kuorikoski, A. Lehtinen & C. Marchionni (2010). Economic Modelling as Robustness Analysis. British Journal for the Philosophy of Science 61 (3):541-567.
Jay Odenbaugh & Anna Alexandrova (2011). Buyer Beware: Robustness Analyses in Economics and Biology. Biology and Philosophy 26 (5):757-771.
Jay Odenbaugh (2008). Ecology and the Inescapability of Values. Science and Engineering Ethics 14 (4):593-596.
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