David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
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|
|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
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.
Similar books and articles
D. C. Struppa (2011). Agnostic Science. Towards a Philosophy of Data Analysis. Foundations of Science 16 (1):1-20.
Douglas S. Robertson (2003). Phase Change: The Computer Revolution in Science and Mathematics. Oxford University Press.
Sabina Leonelli (2012). Classificatory Theory in Data-Intensive Science: The Case of Open Biomedical Ontologies. International Studies in the Philosophy of Science 26 (1):47 - 65.
Emma Smith (2008). Pitfalls and Promises: The Use of Secondary Data Analysis in Educational Research. British Journal of Educational Studies 56 (3):323 - 339.
Gualtiero Piccinini (2009). First-Person Data, Publicity and Self-Measurement. Philosophers' Imprint 9 (9):1-16.
William F. Brewer & Clark A. Chinn (1994). Scientists' Responses to Anomalous Data: Evidence From Psychology, History, and Philosophy of Science. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1994:304 - 313.
Todd Harris (2003). Data Models and the Acquisition and Manipulation of Data. Philosophy of Science 70 (5):1508-1517.
Clark A. Chinn & William F. Brewer (1996). Mental Models in Data Interpretation. Philosophy of Science 63 (3):219.
Uljana Feest (2011). What Exactly is Stabilized When Phenomena Are Stabilized? Synthese 182 (1):57-71.
Leo A. Goodman, Statistical Magic and/or Statistical Serendipity: An Age of Progress in the Analysis of Categorical Data.
Robert Thomas (2002). Idea Analysis of Algebraic Groups: A Critical Comment on George Lakoff and Rafael Núñez's Where Mathematics Comes From. Philosophical Psychology 15 (2):185 – 195.
Kent Johnson (2011). A Lot of Data. Philosophy of Science 78 (5):788-799.
Steven Cook (2001). Observations on the Practice of Data-Mining: Comments on the JEM Symposium. Journal of Economic Methodology 8 (3):415-419.
William C. Wimsatt (1990). Taming the Dimensions-Visualizations in Science. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1990:111 - 135.
Added to index2010-11-18
Total downloads18 ( #100,586 of 1,140,533 )
Recent downloads (6 months)5 ( #37,960 of 1,140,533 )
How can I increase my downloads?