Kemerovo, Kemerovo, Russian Federation
Kemerovo, Russian Federation
The article illustrates the method of cognitive modeling applied to the case of the Kemerovo Region, Russia. The authors developed various scenarios for the local economy in the context of a controlled compression of the socio- economic environment. The Kemerovo Region, or Kuzbass, is a mining region, and the current economic sanctions affect its monopoly dependence in the coal industry. Although cognitive modeling is often applied to the development of resource-dependent regions, the novelty of this study lies in the choice of factors. The target factors involved economic growth, incomes of new industries, and a new, non-coal economy. The factors depended on the type of socio-economic compression, i.e., economic, demographic, physical, communicative, etc. The cognitive model was represented as an oriented graph with 23 vertices and 130 arcs. The graph made it possible to compile an adjacency matrix with the intensity coefficients determined by expert analysis. The matrix revealed a mutual influence on the factors. All factors in the cognitive model for local economic system were combined into the following aggregated groups: socio-economic compression; industry complexes; investments; market, government, plan; target factors. The modelling yielded an inertial and a target scenario. The impulse effect on the compression factors made it possible to predict the development in line with these two scenarios. The growth of the non-coal economy and related investments led to an increase in target factors.
cognitive model, factors of cognitive model, economic diversification, controlled compression, structural shifts, impulse effects of factors, scenarios of economic development
1. Mekush G. E., Kurnosov A. V. Criteria and indicators of the compression of regional economic environment. Prospects for the development of modern socio-economic processes: Proc. XLV Intern. Sci.-Prac. Conf., Anapa, 21 Dec 2023. Anapa: SRC ESP, 2023, 27–34. (In Russ.) https://elibrary.ru/newjrr
2. Kurnosov A. V. Assessment of the types and scales of "compression" of the socio-economic space of Kuzbass municipalities. Regionalnye problemy preobrazovaniya ekonomiki, 2025, (3): 59–71. (In Russ.) https://doi.org/10.26726/rppe2025v3aotas
3. Mekush G. E., Panov A. A., Kurnosov A. V. Structural changes and cluster formation as the basis of the competitiveness of the region’s economy. Regional Economics: Theory and Practice, 2024, 22(12): 2213–2233. (In Russ.) https://doi.org/10.24891/re.22.12.2213
4. Axelrod R. Structure of decision, the cognitive maps of political elites. Princeton: Princeton University, 1976, 404.
5. Casti J. Connectivity, complexity, and catastrophe in large-scale systems. Chichester–NY–Brisbane–Toronto: John Wiley & Sons, 1979, 203.
6. Atkin R. H. Combinatorial connectivies in social systems. An application of simplicial complex structures to the study of large organisations. Birkhäuser Basel, 1977, 245. https://doi.org/10.1007/978-3-0348-5750-5
7. Roberts F. S. Graph theory and its applications to problems of society. Philadelphia: SIAM, 1978, 122.
8. Felix G., Nápoles G., Falcon R., Froelich W., Vanhoof K., Bello R. A review on methods and software for fuzzy cognitive maps. Artificial Intelligence Review, 2019, 52: 1707–1737. https://doi.org/10.1007/s10462-017-9575-1
9. Saak A. A., Gorelova G. V., Kaurova O. V. Imitation cognitive modeling of the youth labor market. Nauchno-teoreticheskij zhurnal, 2020, (3): 164–176. (In Russ.) https://elibrary.ru/ksoody
10. Dolmatova L. G., Mamadiev A. Kh. Application of cognitive modeling in the sphere environmental and economic efficiency of use land resources. Bulletin of the South-Russian State Technical University (NPI). Series: Socio-Economic Sciences, 2022, 15(6): 234–241. (In Russ.) https://doi.org/10.17213/2075-2067-2022-6-234-241
11. Rogachev A. F., Shokhnekh A. V., Medvedeva L. N. Economic-mathematical modeling of management development of medium and single-industry towns with the use of cognitive maps. Audit i finansovyj analiz, 2017, (2): 122–124. (In Russ.) https://elibrary.ru/ylktys
12. Yagolnitser M. A., Ovsiannikova M. A., Kostin A. V. Synergy of investment projects: A cognitive approach. World of Economics and Management, 2022, 22(3): 51–65. (In Russ.) https://doi.org/10.25205/2542-0429-2022-22-3-51-65
13. Arzhenovskiy I. V., Dakhin A. V. Cognitive regionology: The experience of modeling regional socio-economic processes. Russian Journal of Regional Studies, 2020, 28(3): 470–489. (In Russ.) https://doi.org/10.15507/2413-1407.112.028.202003.470-489
14. Sidorov A. A., Sapron D. V. Conceptual basis for cognitive modeling of social and economic development of municipal formations. Proceedings of TUSUR University, 2015, (2): 130–135. (In Russ.) https://elibrary.ru/ubprcj
15. Sergienko O. V. Cognitive modeling of strategic-sustainable socio-economic development of the agricultural sector in the regional economy. Russian Journal of Social Sciences and Humanities, 2023, 17(3): 242–250. (In Russ.) https://elibrary.ru/cmttom
16. Zakharova E. N., Ivanova M. V. Cognitive modeling of the process of ensuring regional competitiveness. Economics: Yesterday, Today and Tomorrow, 2022, 12(7-1): 55–64. (In Russ.) https://elibrary.ru/nkpwit
17. Dabiev D. F. Development of a model for choosing the most effective directions of regional development based on cognitive modeling. Fundamental research, 2022, (1): 16–21. (In Russ.) https://doi.org/10.17513/fr.43186
18. Mityakov E. S., Karpukhina N. N., Mityakov S. N., Ladynin A. I. Cognitive modelling of economic development of industrial ecosystems. Russian Journal of Industrial Economics, 2025, (18): 63–77. (In Russ.) https://doi.org/10.17073/2072-1633-2025-1-1383
19. Makarenya T. A., Mannaa A. S., Kalinichenko A. I., Petrenko S. V. Cognitive modeling of socio-economic systems: A retrospective analysis of tools and information systems. Proceedings of Voronezh State University. Series: Systems Analysis and Information Technologies, 2023, (3): 84–94. (In Russ.) https://doi.org/10.17308/sait/1995-5499/2023/3/84-94
20. Belan A. K., Shmat V. V. Analysis of resource and non-resource factors’ influence on economic growth of Tomsk Region using cognitive approach. Vestnik Novosibirskogo gosudarstvennogo universiteta. Seriya: Socialno-ekonomicheskie nauki, 2015, 15(1): 78–93. (In Russ.) https://elibrary.ru/tvrsuj
21. The natural resources sector in the Russian Far East: A curse or a driver of development? ed. Galtseva N. V. Khabarovsk: ERI FEB RAS, 2022, 336. (In Russ.) https://elibrary.ru/hdwxxb
22. Morozova M. E., Shmat V. V. How to cognize the mechanisms of resource dependence? Application of cognitive modeling in the study of resource-dependent economy. EKO, 2015, (6): 146–159. (In Russ.) https://elibrary.ru/tsoarb
23. Kryukov V. A., Shmat V. V. Asian Russia – conditions for and obstacles to progressive diversification of macroregional economy. Prostranstvennaya ekonomika, 2022, 18(1): 34–72. (In Russ.) https://dx.doi.org/10.14530/se.2022.1.034-072
24. Roberts F. S. Discrete mathematical models, with applications to social, biological, and environmental problems. Moscow: Nauka, 1986, 496. (In Russ.)
25. Zakharova A. A., Podvesovskii A. G., Isaev R. A. Fuzzy cognitive models in management of semi-structured socio-economic systems. Information and mathematical technologies in science and management, 2020, (4): 5–23. (In Russ.) https://doi.org/10.38028/ESI.2020.20.4.001
