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Solomonoff Prediction and Occam's Razor

Sterkenburg, Tom F. (2016) Solomonoff Prediction and Occam's Razor. Philosophy of Science, 83 (4). pp. 459-479.

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Abstract

Algorithmic information theory gives an idealized notion of compressibility, that is often presented as an objective measure of simplicity. It is suggested at times that Solomonoff prediction, or algorithmic information theory in a predictive setting, can deliver an argument to justify Occam's razor. This paper explicates the relevant argument, and, by converting it into a Bayesian framework, reveals why it has no such justificatory force. The supposed simplicity concept is better perceived as a specific inductive assumption, the assumption of effectiveness. It is this assumption that is the characterizing element of Solomonoff prediction, and wherein its philosophical interest lies.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Sterkenburg, Tom F.t.f.sterkenburg@rug.nl
Keywords: Algorithmic information theory, Occam's razor, Bayesian confirmation, universal prediction
Subjects: Specific Sciences > Computer Science
General Issues > Confirmation/Induction
Specific Sciences > Probability/Statistics
Depositing User: Mr Tom Sterkenburg
Date Deposited: 14 Sep 2016 12:45
Last Modified: 14 Sep 2016 12:45
Item ID: 12429
Journal or Publication Title: Philosophy of Science
Publisher: The University of Chicago Press
Official URL: http://www.journals.uchicago.edu/doi/pdfplus/10.10...
DOI or Unique Handle: 10.1086/687257
Subjects: Specific Sciences > Computer Science
General Issues > Confirmation/Induction
Specific Sciences > Probability/Statistics
Date: October 2016
Page Range: pp. 459-479
Volume: 83
Number: 4
URI: https://philsci-archive.pitt.edu/id/eprint/12429

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