David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Foundations of Science 16 (1):1-20 (2011)
In this paper we will offer a few examples to illustrate the orientation of contemporary research in data analysis and we will investigate the corresponding role of mathematics. We argue that the modus operandi of data analysis is implicitly based on the belief that if we have collected enough and sufficiently diverse data, we will be able to answer most relevant questions concerning the phenomenon itself. This is a methodological paradigm strongly related, but not limited to, biology, and we label it the microarray paradigm. In this new framework, mathematics provides powerful techniques and general ideas which generate new computational tools. But it is missing any explicit isomorphism between a mathematical structure and the phenomenon under consideration. This methodology used in data analysis suggests the possibility of forecasting and analyzing without a structured and general understanding. This is the perspective we propose to call agnostic science, and we argue that, rather than diminishing or flattening the role of mathematics in science, the lack of isomorphisms with phenomena liberates mathematics, paradoxically making more likely the practical use of some of its most sophisticated ideas
|Keywords||Methods of computational science Philosophy of data analysis Philosophy of science|
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References found in this work BETA
Peter Achinstein (1983). The Nature of Explanation. Oxford University Press.
Robert Batterman (2010). On the Explanatory Role of Mathematics in Empirical Science. British Journal for the Philosophy of Science 61 (1):1-25.
Robert W. Batterman (2002). The Devil in the Details: Asymptotic Reasoning in Explanation, Reduction, and Emergence. Oxford University Press.
Michael Friedman (1974). Explanation and Scientific Understanding. Journal of Philosophy 71 (1):5-19.
I. J. Good (1983). The Philosophy of Exploratory Data Analysis. Philosophy of Science 50 (2):283-295.
Citations of this work BETA
Paul Humphreys (2013). Data Analysis: Models or Techniques? [REVIEW] Foundations of Science 18 (3):579-581.
Domenico Napoletani, Marco Panza & Daniele C. Struppa (2013). Processes Rather Than Descriptions? Foundations of Science 18 (3):587-590.
Johannes Lenhard (2013). Coal to Diamonds. Foundations of Science 18 (3):583-586.
Domenico Napoletani, Marco Panza & Daniele C. Struppa (2013). Artificial Diamonds Are Still Diamonds. Foundations of Science 18 (3):591-594.
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