Document Type

Article

Publication Date

6-20-2010

Abstract

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.

Comments

This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Foundations of Science, volume 16, in 2011 following peer review. The final publication may differ and is available at Springer via https://doi.org/10.1007/s10699-010-9186-7.

A free-to-read copy of the final published article is available here.

Peer Reviewed

1

Copyright

Springer

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