ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

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Language: 
Russian
Heading: 
Science for Education. Methodical Papers. History.
Article type: 
Article
UDC: 
519.722
DOI: 
10.18500/0869-6632-003112
EDN: 
EZYDZC

Definition of information in computer science

Autors: 
Kuzenkov Oleg Anatolevich, Lobachevsky State University of Nizhny Novgorod
Abstract: 

Purpose of this study is to formulate a working definition of information to meet the needs of computer science. There is currently no strict definition of this term. There is a methodological contradiction: the development and application of information technologies requires accuracy and rigor, but at the same time the development is based on a vague, intuitive concept.

Materials and methods. The materials for the study are existing classical approaches to understanding information, and the main method is the analysis of these approaches. The proposed definition is constructed taking into account two mathematical transformations: the selection of a certain subset and the mapping between sets. To formalize the allocation procedure, it is used apparatus of fuzzy sets.

Results. A definition of information is proposed as the result of a mapping in which the selection of a subset from a set of prototypes leads to the selection of a corresponding subset from a set of images. The selected subset can be understood as fuzzy, then an equivalent definition of information is acceptable as a result of mapping, in which an increase in the heterogeneity of the distribution of the presence indicator on the set of prototypes leads to an increase in the heterogeneity of the distribution of the corresponding indicator on the set of images. The essence of the new definition is demonstrated using models of population dynamics in discrete time. The significance of the proposed approach for information technology is revealed using the example of the numerical method of multi-extremal optimization. It is shown that the proposed definition makes it possible to formulate effective stopping conditions for the numerical method of stochastic optimization, which guarantees the receipt of a given amount of information.

Conclusion. The proposed understanding of information allows us to overcome the shortcomings of previous approaches to understanding the essence of information, retains all the advantages of the classical approach and is consistent with other well-known approaches in the field of computer science. This definition can be used to improve numerical optimization methods, as well as other information technology tools.

Key words: 
information
Entropy
fuzzy set
membership indicator
distribution
mapping
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Received: 
03.12.2023
Accepted: 
11.03.2024
Available online: 
27.05.2024
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