Neth, Sven (2022) A Dilemma for Solomonoff Prediction. [Preprint]

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Abstract

The framework of Solomonoff prediction assigns prior probability to hypotheses inversely proportional to their Kolmogorov complexity. There are two well-known problems. First, the Solomonoff prior is relative to a choice of Universal Turing machine. Second, the Solomonoff prior is not computable. However, there are responses to both problems. Different Solomonoff priors converge with more and more data. Further, there are computable approximations to the Solomonoff prior. I argue that there is a tension between these two responses. This is because computable approximations to Solomonoff prediction do not always converge.

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Item Type:	Preprint
Creators:	Creators Email ORCID Neth, Sven nethsven@berkeley.edu 0000-0003-4275-7581
Keywords:	Solomonoff prediction, Bayesian epistemology, prior probability, computability, convergence, Kolmogorov complexity
Subjects:	Specific Sciences > Computation/Information Specific Sciences > Computer Science Specific Sciences > Artificial Intelligence General Issues > Confirmation/Induction General Issues > Models and Idealization Specific Sciences > Probability/Statistics
Depositing User:	Sven Neth
Date Deposited:	21 Dec 2022 15:38
Last Modified:	21 Dec 2022 15:38
Item ID:	21576
Official URL:	https://www.cambridge.org/core/journals/philosophy...
DOI or Unique Handle:	https://doi.org/10.1017/psa.2022.72
Subjects:	Specific Sciences > Computation/Information Specific Sciences > Computer Science Specific Sciences > Artificial Intelligence General Issues > Confirmation/Induction General Issues > Models and Idealization Specific Sciences > Probability/Statistics
Date:	13 June 2022
URI:	https://philsci-archive.pitt.edu/id/eprint/21576

Available Versions of this Item

• A Dilemma for Solomonoff Prediction. (deposited 17 Jun 2022 02:30)
  • A Dilemma for Solomonoff Prediction. (deposited 21 Dec 2022 15:38) [Currently Displayed]

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