Complexity and verisimilitude: Realism for ecology [Book Review]

Biology and Philosophy 16 (4):533-546 (2001)

Authors
Greg Mikkelson
McGill University
Abstract
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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Reprint years 2004
DOI 10.1023/A:1011905829922
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References found in this work BETA

A Confutation of Convergent Realism.Larry Laudan - 1981 - Philosophy of Science 48 (1):19-49.
A Confutation of Convergent Realism.Larry Laudan - 1980 - In Yuri Balashov & Alexander Rosenberg (eds.), Philosophy of Science: Contemporary Readings. Routledge. pp. 211.

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Citations of this work BETA

Occam’s Razor in Science: A Case Study From Biogeography.A. Baker - 2007 - Biology and Philosophy 22 (2):193-215.
The Galilean Turn in Population Ecology.Mark Colyvan & Lev R. Ginzburg - 2003 - Biology and Philosophy 18 (3):401-414.

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