Complexity and verisimilitude: Realism for ecology [Book Review]
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Biology and Philosophy 16 (4):533-546 (2001)
When data are limited, simple models of complex ecological systems tend to wind up closer to the truth than more complex models of the same systems. This greater proximity to the truth, or verisimilitude, leads to greater predictive success. When more data are available, the advantage of simplicity decreases, and more complex models may gain the upper hand. In ecology, holistic models are usually simpler than reductionistic models. Thus, when data are limited, holistic models have an advantage over reductionistic models, with respect to verisimilitude and predictive success. I illustrate these points with models designed to explain and predict the numbers of species on islands.
|Keywords||biodiversity biogeography ecology holism instrumentalism parsimony prediction realism reductionism verisimilitude|
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Citations of this work BETA
A. Baker (2007). Occam's Razor in Science: A Case Study From Biogeography. Biology and Philosophy 22 (2):193-215.
Gregory M. Mikkelson (2005). Niche-Based Vs. Neutral Models of Ecological Communities. Biology and Philosophy 20 (2-3):557-566.
Yoichi Ishida (2007). Patterns, Models, and Predictions: Robert Macarthur's Approach to Ecology. Philosophy of Science 74 (5):642-653.
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