David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Studies in History and Philosophy of Science Part A 34 (3):633-646 (2003)
According to a traditional view, scientific laws and theories constitute algorithmic compressions of empirical data sets collected from observations and measurements. This article defends the thesis that, to the contrary, empirical data sets are algorithmically incompressible. The reason is that individual data points are determined partly by perturbations, or causal factors that cannot be reduced to any pattern. If empirical data sets are incompressible, then they exhibit maximal algorithmic complexity, maximal entropy and zero redundancy. They are therefore maximally efficient carriers of information about the world. Since, on algorithmic information theory, a string is algorithmically random just if it is incompressible, the thesis entails that empirical data sets consist of algorithmically random strings of digits. Rather than constituting compressions of empirical data, scientific laws and theories pick out patterns that data sets exhibit with a certain noise.
|Keywords||No keywords specified (fix it)|
|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
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Evert Leeuwen Martine de Vrievans (forthcoming). Reflective Equilibrium and Empirical Data: Third Person Moral Experiences in Empirical Medical Ethics. Bioethics.
J. W. McAllister (2005). Algorithmic Compression of Empirical Data: Reply to Twardy, Gardner, and Dowe. Studies in History and Philosophy of Science Part A 36 (2):403-410.
Edwin H. -C. Hung (2005). Projective Explanation: How Theories Explain Empirical Data in Spite of Theory-Data Incommensurability. Synthese 145 (1):111 - 129.
James W. McAllister (2007). Model Selection and the Multiplicity of Patterns in Empirical Data. Philosophy of Science 74 (5):884-894.
James W. McAllister (2003). Effective Complexity as a Measure of Information Content. Philosophy of Science 70 (2):302-307.
André Nies, Frank Stephan & Sebastiaan A. Terwijn (2005). Randomness, Relativization and Turing Degrees. Journal of Symbolic Logic 70 (2):515 - 535.
James W. McAllister (2011). What Do Patterns in Empirical Data Tell Us About the Structure of the World? Synthese 182 (1):73-87.
J. W. McAllister (2003). Algorithmic Randomness in Empirical Data. Studies in History and Philosophy of Science Part A 34 (3):633-646.
Charles Twardy, Steve Gardner & David Dowe (2005). Empirical Data Sets Are Algorithmically Compressible: Reply to McAllister. Studies in the History and Philosophy of Science, Part A 36 (2):391-402.
Added to index2009-01-28
Total downloads12 ( #146,878 of 1,679,387 )
Recent downloads (6 months)1 ( #183,003 of 1,679,387 )
How can I increase my downloads?