Methodology for Assessing the Risks of Regional Competitiveness Based on the Kolmogorov–Chapman Equations

Galina Chernyshova, Irina Veshneva, Anna Firsova (), Elena L. Makarova and Elena A. Makarova
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Galina Chernyshova: Faculty of Computer Science and Information Technologies, Saratov State University, 83, Astrakhanskaya Str., 410600 Saratov, Russia
Irina Veshneva: Faculty of Computer Science and Information Technologies, Saratov State University, 83, Astrakhanskaya Str., 410600 Saratov, Russia
Anna Firsova: Faculty of Economics, Saratov State University, 83, Astrakhanskaya Str., 410600 Saratov, Russia
Elena L. Makarova: Institute of Management in Economic, Environmental and Social Systems, Southern Federal University, 105/42, Bolshaya Sadovaya Str., 344006 Rostov-on-Don, Russia
Elena A. Makarova: Faculty of Psychology, Pedagogies and Defectology, Don State Technical University, 1, Gagarin Sq., 344000 Rostov-on-Don, Russia

Mathematics, 2023, vol. 11, issue 19, 1-19

Abstract: The relevance of research on competitiveness at the meso level is related to the contemporary views of a region as an essential element of the economic space. The development of forecasting and analytical methods at the regional level of the economy is a key task in the process of strategic decision making. This article proposes a method of quantitative assessment of the risks of regional competitiveness. The novelty of this approach is based on both a fixed-point risk assessment and scenario-based predictive analysis. A hierarchical structure of indicators of competitiveness of regions is offered. A method based on the Kolmogorov–Chapman equations was used for the predictive estimation of risks of regional competitiveness. The integrated risk assessment is performed using the modified fuzzy ELECTRE II method. A web application has been implemented to assess the risks of competitiveness of Russian regions. The functionality of this application provides the use of multi-criteria decision-making methods based on a fuzzy logic approach to estimate risks at a specified time, calculating the probability of risk events and their combinations in the following periods and visualizing the results. Approbation of the technique was carried out for 78 Russian regions for various scenarios. The analysis of the results obtained provides an opportunity to identify the riskiest factors of regional competitiveness and to distinguish regions with different risk levels.

Keywords: regional competitiveness; risks; mathematical modeling; multi-criteria decision-making methods; fuzzy techniques; Kolmogorov–Chapman equations; decision support system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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