Izvestiya of Saratov University.

Mathematics. Mechanics. Informatics

ISSN 1816-9791 (Print)
ISSN 2541-9005 (Online)

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Firsova A. A., Makarova E. L. Cognitive analysis of the structural stability for the knowledge-intensive sectors of the regional economy. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, 2022, vol. 22, iss. 3, pp. 401-412. DOI: 10.18500/1816-9791-2022-22-3-401-412, EDN: BCKVXN

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
31.08.2022
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Language: 
English
Heading: 
Computer Sciences
Article type: 
Article
UDC: 
338
DOI: 
10.18500/1816-9791-2022-22-3-401-412
EDN: 
BCKVXN

Cognitive analysis of the structural stability for the knowledge-intensive sectors of the regional economy

Autors: 
Firsova Anna A., Saratov State University
Makarova Elena L., Southern Federal University
Abstract: 

The paper demonstrates the application of cognitive modeling methods to study the functioning problems of semi-structured complex economic systems. The aim of the research is to study the structural stability of the relationship between knowledge-intensive sectors and regional economy using cognitive analysis. The concepts and factors influencing the studied systems are determined, cognitive maps of the relationship between knowledge-intensive sectors and regional economy are created, a qualitative analysis of the properties and cycles has been carried out. Impulse and scenario modeling made it possible to draw a conclusion about structural stability of the developed cognitive model. Various scenarios of the system's behavior under the influence of impulse modeling are visualized. The research results can be used to support decision-making process due to developing strategies and policies for the balanced development of knowledge-intensive markets and socio-economic systems.

Key words: 
cognitive modeling
cognitive map
scenario analysis
impulse modeling
knowledge-intensive sectors
Acknowledgments: 
This work was supported by Russian Science Foundation (project No. 19-18-00199).
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Received: 
04.03.2021
Accepted: 
29.04.2022
Published: 
31.08.2022
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