Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-17T18:14:32.471Z Has data issue: false hasContentIssue false
  • English
  • Français

Aspects of Theory-Ladenness in Data-Intensive Science

Published online by Cambridge University Press:  01 January 2022

Wolfgang Pietsch
Get access Rights & Permissions [Opens in a new window]

Abstract

Recent claims, mainly from computer scientists, concerning a largely automated and model-free data-intensive science have been criticized by several philosophers of science. The debate suffers from lack of detail regarding the actual methods used in data-intensive science and in which ways these presuppose theoretical assumptions. I examine two widely used algorithms, classificatory trees and nonparametric regression, and argue that they are theory laden in an external sense, regarding the framing of research questions, but not in an internal sense, concerning the causal structure of the examined phenomenon. With respect to the novelty of data-intensive science, I draw an analogy to exploratory experimentation.

Type
Confirmation Theory
Information
Philosophy of Science , Volume 82 , Issue 5 , December 2015 , pp. 905 - 916
Copyright
Copyright © The Philosophy of Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

†

I am grateful to Mathias Frisch, Sabina Leonelli, and Sylvester Tremmel for very helpful insights and discussions.

References

Anderson, Chris. 2008. “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete.” WIRED, http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory.Google Scholar
Bacon, Francis. 1620/1620. Novum Organum. Repr. Chicago: Open Court.Google Scholar
Baumgartner, Michael, and Grasshoff, Gerd. 2004. Kausalität und kausales Schließen. Norderstedt: Books on Demand.Google Scholar
Breiman, Leo. 2001. “Statistical Modeling: The Two Cultures.” Statistical Science 16 (3): 199–231.CrossRefGoogle Scholar
Burian, Richard. 1997. “Exploratory Experimentation and the Role of Histochemical Techniques in the Work of Jean Brachet, 1938–1952.” History and Philosophy of the Life Sciences 19:27–45.Google ScholarPubMed
Callebaut, Werner. 2012. “Scientific Perspectivism: A Philosopher of Science’s Response to the Challenge of Big Data Biology.” Studies in History and Philosophy of Biological and Biomedical Science 43 (1): 69–80.CrossRefGoogle Scholar
Cartwright, Nancy. 1983. How the Laws of Physics Lie. Oxford: Oxford University Press.CrossRefGoogle Scholar
Franklin, Laura R. 2005. “Exploratory Experiments.” Philosophy of Science 72:888–99.CrossRefGoogle Scholar
Gray, Jim. 2007. “Jim Gray on eScience: A Transformed Scientific Method.” In The Fourth Paradigm: Data-Intensive Scientific Discovery, ed. Hey, Tony, Tansley, Stewart, and Tolle, Kristin, xvi–xxxi. Redmond, WA: Microsoft Research.Google Scholar
Hacking, Ian. 1983. Representing and Intervening. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Halevy, Alon, Norvig, Peter, and Pereira, Fernando. 2009. “The Unreasonable Effectiveness of Data.” IEEE Intelligent Systems 24 (2): 8–12.CrossRefGoogle Scholar
Keynes, John M. 1921. A Treatise on Probability. London: Macmillan.Google Scholar
Krohs, Ulrich. 2012. “Convenience Experimentation.” Studies in History and Philosophy of Biological and Biomedical Sciences 43 (1): 52–57.CrossRefGoogle ScholarPubMed
Laney, Doug. 2001. “3D Data Management: Controlling Data Volume, Velocity, and Variety.” http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf.Google Scholar
Leonelli, Sabina. 2012. “Making Sense of Data-Driven Research in the Biological and Biomedical Sciences.” Studies in History and Philosophy of Biological and Biomedical Sciences 43:1–3.CrossRefGoogle ScholarPubMed
Mackie, John L. 1980. The Cement of the Universe. Oxford: Oxford University Press.CrossRefGoogle Scholar
Mill, John S. 1886. System of Logic. London: Longmans, Green.Google Scholar
Norvig, Peter. 2009. “All We Want Are the Facts, Ma’am.” http://norvig.com/fact-check.html.Google Scholar
Norvig, Peter 2011. “On Chomsky and the Two Cultures of Statistical Learning.” http://norvig.com/chomsky.html.Google Scholar
Pietsch, Wolfgang. 2014. “The Nature of Causal Evidence Based on Eliminative Induction.” Topoi 33 (2): 421–35.CrossRefGoogle Scholar
Russell, Stuart, and Norvig, Peter. 2009. Artificial Intelligence. Upper Saddle River, NJ: Pearson.Google Scholar
Skyrms, Brian. 2000. Choice and Chance. Belmont, CA: Wadsworth.Google Scholar
Steinle, Friedrich. 1997. “Entering New Fields: Exploratory Uses of Experimentation.” Philosophy of Science 64 (Proceedings): S65–S74.CrossRefGoogle Scholar
Steinle, Friedrich 2005. Explorative Experimente. Stuttgart: Steiner.Google Scholar
Vincenti, Walter. 1993. What Engineers Know and How They Know It. Baltimore: Johns Hopkins University Press.Google Scholar
von Wright, Georg H. 1951. A Treatise on Induction and Probability. New York: Routledge.Google Scholar
Wasserman, Larry. 2006. All of Nonparametric Statistics. New York: Springer.Google Scholar
Waters, C. Kenneth. 2007. “The Nature and Context of Exploratory Experimentation.” History and Philosophy of the Life Sciences 29 (3): 275–84.Google ScholarPubMed