David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Philosophia 34 (3):337-353 (2006)
This paper argues for two related theses. The first is that mathematical abstraction can play an important role in shaping the way we think about and hence understand certain phenomena, an enterprise that extends well beyond simply representing those phenomena for the purpose of calculating/predicting their behaviour. The second is that much of our contemporary understanding and interpretation of natural selection has resulted from the way it has been described in the context of statistics and mathematics. I argue for these claims by tracing attempts to understand the basis of natural selection from its early formulation as a statistical theory to its later development by R.A. Fisher, one of the founders of modern population genetics. Not only did these developments put natural selection of a firm theoretical foundation but its mathematization changed the way it was understood as a biological process. Instead of simply clarifying its status, mathematical techniques were responsible for redefining or reconceptualising selection. As a corollary I show how a highly idealised mathematical law that seemingly fails to describe any concrete system can nevertheless contain a great deal of accurate information that can enhance our understanding far beyond simply predictive capabilities.
|Keywords||mathematical abstraction natural selection population genetics|
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References found in this work BETA
Charles Darwin (2008). On the Origin of Species: By Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. Sterling Pub..
Nancy Cartwright (1983). How the Laws of Physics Lie. Oxford University Press.
Elliott Sober (1986). The Nature of Selection. Behaviorism 14 (1):77-88.
Denis M. Walsh, Andre Ariew & Tim Lewens (2002). The Trials of Life: Natural Selection and Random Drift. Philosophy of Science 69 (3):452-473.
Margaret Morrison (2000). Unifying Scientific Theories: Physical Concepts and Mathematical Structures. Cambridge Univ Pr.
Citations of this work BETA
D. Napoletani, M. Panza & D. Struppa (2011). Agnostic Science. Towards a Philosophy of Data Analysis. Foundations of Science 16 (1):1-20.
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