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
Learn more about PhilPapers
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|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
Stephen M. Downes (2009). Models, Pictures, and Unified Accounts of Representation: Lessons From Aesthetics for Philosophy of Science. Perspectives on Science 17 (4):417-428.
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.
J. Kuorikoski, A. Lehtinen & C. Marchionni (2010). Economic Modelling as Robustness Analysis. British Journal for the Philosophy of Science 61 (3):541-567.
Christopher Pincock (2012). Mathematical Models of Biological Patterns: Lessons From Hamilton's Selfish Herd. Biology and Philosophy 27 (4):481-496.
Similar books and articles
Daniel M. Merfeld (2004). Internal Models and Spatial Orientation. Behavioral and Brain Sciences 27 (3):410-410.
Jay Odenbaugh, The “Structure” of Population Ecology: Philosophical Reflections on Unstructured and Structured Models.
Jay Odenbaugh (2003). Complex Systems, Trade-Offs, and Theoretical Population Biology: Richard Levin's "Strategy of Model Building in Population Biology" Revisited. Philosophy of Science 70 (5):1496-1507.
Jay Odenbaugh (2003). Complex Systems, Trade‐Offs, and Theoretical Population Biology: Richard Levin's “Strategy of Model Building in Population Biology” Revisited. Philosophy of Science 70 (5):1496-1507.
Stephan Hartmann & Roman Frigg (2006). Models in Science. In Ed Zalta (ed.), The Stanford Encyclopedia of Philosophy. Stanford.
W. S. Parker (2006). Understanding Pluralism in Climate Modeling. Foundations of Science 11 (4):349-368.
Gregory M. Mikkelson (2001). Complexity and Verisimilitude: Realism for Ecology. [REVIEW] Biology and Philosophy 16 (4):533-546.
Jay Odenbaugh (2006). Message in the Bottle: The Constraints of Experimentation on Model Building. Philosophy of Science 73 (5):720-729.
Added to index2009-01-28
Total downloads43 ( #36,802 of 1,096,395 )
Recent downloads (6 months)4 ( #60,433 of 1,096,395 )
How can I increase my downloads?