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A Dilemma for Solomonoff Prediction

Sven, Neth (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:
CreatorsEmailORCID
Sven, Nethnethsven@berkeley.edu0000-0003-4275-7581
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: 17 Jun 2022 02:30
Last Modified: 17 Jun 2022 02:30
Item ID: 20747
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/20747

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