TL;DR
This paper explores how concepts from algorithmic information theory can be applied to list-decodable codes, translating class-based properties into properties of individual codewords.
Contribution
It introduces a novel approach to analyze list-decodable codes using the framework of algorithmic information theory, bridging two areas of theoretical computer science.
Findings
Establishes a translation of class-based properties to individual codewords.
Provides new insights into the information content of individual codewords.
Suggests potential for analyzing code performance through individual properties.
Abstract
Algorithmic information theory translates statements about classes of objects into statements about individual objects; it defines individual random sequences, effective Hausdorff dimension of individual points, amount of information in individual strings, etc. We observe that a similar translation is possible for list-decodable codes.
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