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https://doi.org/10.2139/ssrn.4...
Article . 2023 . Peer-reviewed
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2023
License: CC BY
Data sources: Datacite
DBLP
Preprint . 2023
Data sources: DBLP
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On Information Content in Certain Objects

Authors: Nikolay K. Vereshchagin;

On Information Content in Certain Objects

Abstract

The fine approach to measure information dependence is based on the total conditional complexity CT(y|x), which is defined as the minimal length of a total program that outputs y on the input x. It is known that the total conditional complexity can be much larger than than the plain conditional complexity. Such strings x, y are defined by means of a diagonal argument and are not otherwise interesting. In this paper we investigate whether this happens also for some natural objects. More specifically, we consider the following objects: the number of strings of complexity less than n and the lex first string of length n and complexity at least n. It is known that they have negligible mutual conditional complexities. In this paper we prove that their mutual total conditional complexities may be large. This is the first example of natural objects whose plain conditional complexity is much less than the total one.

23 pages, 5 figures

Keywords

FOS: Computer and information sciences, Computer Science - Information Theory, Information Theory (cs.IT), 68

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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