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4 - Algorithmic randomness and layerwise computability

Published online by Cambridge University Press:  07 May 2020

Johanna N. Y. Franklin
Affiliation:
Hofstra University, New York
Christopher P. Porter
Affiliation:
Drake University, Iowa
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Summary

Algorithmic randomness lies at the intersection between computability theory and probability theory. In order to fully explore this interaction, one naturally needs a computable version of measurable functions. While several such notions appear in the literature, most of them do not interact well with algorithmic randomness because they are only defined up to a null set. Therefore, we need a computable notion of measurable function which is well defined on algorithmically random points, and this is what layerwise computability precisely does. This article is a survey about this notion. We give the main definitions, the most important properties, and several applications of this notion. We prioritize motivating this framework and explaining its salient features.

Type
Chapter
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Algorithmic Randomness
Progress and Prospects
, pp. 115 - 133
Publisher: Cambridge University Press
Print publication year: 2020

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