I. V. Arzhenovskiy, A. V. Dakhin. Cognitive Regionology: The Experience of Modeling Regional Socio-Economic Processes

UDК 316.42:314.01

DOI: https://doi.org/10.15507/2413-1407.112.028.202003.470-489

Introduction. In the current situation, the society is subject to extremely dynamic changes and strategic developments are becoming obsolete more rapidly. It is cognitive modeling of complex semi-structured systems that is one of the modern methods that presents technological solutions in response to the challenge of obsolescence of strategies. The practice of strategizing Russia’s regions makes it possible to single out the regional dimension as a self-sufficient subject of cognitive modeling. The objective of the article is to summarize many years of experience of applying the method of cognitive modeling of regional socio-economic processes and the application of the obtained models in educational, scientific and administrative activities of the region on the basis of the study conducted.

Materials and Methods. The database of Analytic software application was used as the information resource. Cognitive modeling was the main method employed and was considered in more detail; statistical methods, comparative analysis, and an expert survey were also used.

Results. Specific examples of research conducted in the Nizhny Novgorod Region, the Samara Region, and the Republic of Mordovia have shown the advantages of using cognitive modeling technology in the educational, scientific, and administrative activities in a region. The factor-digital cognitive model of a region becomes the basis for organizing trainings on the strategy of sustainable development at the regional and interregional levels. Analytic software application supports the research process and is integrated into the electronic information and educational environment of regional universities.

Discussion and Conclusion. The cognitive modeling method makes it possible to solve the problems of static and dynamic analysis of a region as a complex system in various subject areas. The factor-digital models of regions obtained using Analytic software application are of a universal nature and are relatively easily modified under the framework conditions of any other region of Russia on remote access platforms.

Keywords: strategic regional development, cognitive modeling, cognitive model of a region, sustainability, systemic analysis, interactive learning


1. Dakhin A.V., Danikova O.S., Denisov V.I., Guseva V.A. Experience of Cognitive Modeling of the Social Synergy Aftereffects for Regional Activity of Small and Medium Business. Vlast = Authority. 2011; (1):42-49. URL: https://www.isras.ru/files/File/Vlast/2011/01/Dahin.pdf (accessed 15.04.2020). (In Russ.)

2. Perevaryukha A.Yu. Cognitive Simulation in the Analysis of Structural Interactions of Environmental Processes in Caspian Sea. Prikladnaya informatika = Journal of Applied Informatics. 2014; (5):108-118. Available at: http://www.appliedinformatics.ru/general/upload/articles/p108-118-rename... (accessed 03.05.2020). (In Russ., abstract in Eng.)

3. Ginis L.A., Davydenko O.V. Development of Information and Control Systems Metamodel of Complex Objects Taking into Account Cognitive Approach. Rossiyskiy ehkonomicheskiy vestnik = Russian Economic Bulletin. 2019; 2(6):166-171. Available at: http://dgpu-journals.ru/archives/10238 (accessed 12.05.2020). (In Russ., abstract in Eng.)

4. Klimenko A., Gorelova G., Korobkin V. The Cognitive Approach to the Coverage-Directed Test Generation. Advances in Intelligent Systems and Computing. 2018; 662:372-380. (In Eng.) DOI: https://doi.org/10.1007/978-3-319-67621-0_34

5. Kuleshov V.V., Alekseev A.V., Yagol’nitser M.A. Methods of Cognitive Analysis in Devising and Substantiating Strategies of Economic Development. Studies on Russian Economic Development. 2019; 30:185-191. (In Eng.) DOI: https://doi.org/10.1134/S1075700719020096

6. Padilla L.M., Creem-Regehr S.H., Hegarty M., et al. Decision Making with Visualizations: A Cognitive Framework Across Disciplines. Cognitive Research: Principles and Implications. 2018; 3. Article 29. (In Eng.) DOI: https://doi.org/10.1186/s41235-018-0120-9

7. Vasilyev V.I., Vulfin A.M., Guzairov M.B., Kartak V.M. Chernyakhovskaya L.R. Cybersecurity Risk Assessment of Industrial Objects’ ACS of TP on the Basis of Nested Fuzzy Cognitive Maps Technology. Informatsionnye tekhnologii = Information Technologies. 2020; 26(4):213-221. (In Russ., abstract in Eng.) DOI: https://doi.org/10.17587/it.26.213-221

8. Aguilar J., Téran O., Sánchez H., Gutiérrez de Mesa J., Cordero J., Chávez D. Towards a Fuzzy Cognitive Map for Opinion Mining. Procedia Computer Science. 2017; 108:2522-2526. (In Eng.) DOI: https://doi.org/10.1016/j.procs.2017.05.287

9. Kalantari T., Khoshalhan F. Readiness Assessment of Leagility Supply Chain Based on Fuzzy Cognitive Maps and Interpretive Structural Modeling: A Case Study. Journal of Business & Industrial Marketing. 2018; 33(4):442-456. (In Eng.) DOI: https://doi.org/10.1108/JBIM-01-2017-0008

10. Felix G., Nápoles G., Falcon R., et al. A Review on Methods and Software for Fuzzy Cognitive Maps. Artificial Intelligence Review. 2019; 52:1707-1737. (In Eng.) DOI: https://doi.org/10.1007/s10462-017-9575-1

11. Li B., Wang Y., Dai G., et al. Framework and Case Study of Cognitive Maintenance in Industry 4.0. Frontiers of Information Technology & Electronic Engineering. 2019; 20:1493-1504. (In Eng.) DOI: https://doi.org/10.1631/FITEE.1900193

12. Tselykh A., Vasilev V., Tselykh L. Assessment of Influence Productivity in Cognitive Models. Artificial Intelligence Review. 2020. (In Eng.) DOI: https://doi.org/10.1007/s10462-020-09823-8

13. Gurumoorthy S., Rao B.N., Gao X.-Z. Cognitive Science and Artificial Intelligence Advances and Applications. Springer; 2018. (In Eng.) DOI: https://doi.org/10.1007/978-981-10-6698-6

14. Ross D. Empiricism, Sciences, and Engineering: Cognitive Science as a Zone of Integration. Cognitive Processing. 2019; 20:261-267. (In Eng.) DOI: https://doi.org/10.1007/s10339-019-00916-z

15. Andrade-Garda J., Anguera Á., Ares-Casal J., et al. A Metrology-Based Approach for Measuring the Social Dimension of Cognitive Trust in Collaborative Networks. Cognition, Technology & Work. 2020; 22:235-248. (In Eng.) DOI: https://doi.org/10.1007/s10111-018-0483-1

16. Dakhin A., Arzhenovskiy I. Putting Sustainability Theory Into Practice in Nizhny Novgorod, Russia. Academia and Communities: Engaging for Change. Tokyo, UNU-IAS; 2018. p. 174-183. Available at: https://www.elibrary.ru/item.asp?id=36725150 (accessed 12.05.2020). (In Eng.)

17. Gershkovich V.A., Falikman M.V. Cognitive Psychology in Search of Itself. Rossiyskiy zhurnal kognitivnoy nauki = The Russian Journal of Cognitive Science. 2018; 5(4):28-46. Available at: http://www.cogjournal.ru/5/4/index.html (accessed 25.04.2020). (In Russ., abstract in Eng.)

18. Strielkowski W., Kiseleva L.S., Popova E.N. Factors Determining the Quality of University Education: Students’ Views. Integratsiya obrazovaniya = Integration of Education. 2018; 22(2):220-236. (In Russ., abstract in Eng.) DOI: https://doi.org/10.15507/1991-9468.091.022.201802.220-236

19. Ienca M. Democratizing Cognitive Technology: A Proactive Approach. Ethics and Information Technology. 2019; 21:267-280. (In Eng.) DOI: https://doi.org/10.1007/s10676-018-9453-9

20. Dakhin A.V., Danilova O.S., Denisov V.N. Regional Policy of Modernization: Administrative Space, Factors and Scenarios (A Case of Cognitive Modeling)]. Vestnik Novosibirskogo gosudarstvennogo universiteta. Seriya: Filosofiya = Novosibirsk State University Bulletin. Series: Philosophy. 2013; (3):63-72. Available at: https://elibrary.ru/item.asp?id= 20301423 (accessed 18.04.2020). (In Russ., abstract in Eng.)

21. Arzhenovskiy I.V. Factors of Mutual Impact of Innovative Startups and the Regional Environment. Regionologiya = Regionology. 2018; 26(4):658-673. (In Russ., abstract in Eng.) DOI: https://doi.org/10.15507/2413-1407.105.026.201804.658-673

22. Dakhin A.V., Danilova O.S., Denisov V.N. Regional Modernization Policy: Administrative Space, Factors, Scenarios. Regionologiya = Regionology. 2013; (4):28-41. Available at: http://regionsar.ru/ru/node/1168 (accessed 03.05.2020). (In Russ., abstract in Eng.)

23. Arzhenovskiy I.V., Dakhin A.V. Modeling the Аctivities of Innovative Startups at the Regional Level. Finansovyy Biznes = Financial Business. 2019; (4):29-35. Available at: http://ankil.info/lib/4/267/2289/ (accessed 11.05.2020). (In Russ., abstract in Eng.)

24. Razumova I.K., Kuznetsov A.Yu. World and National Trends in University Libraries Acquisition. Integratsiya obrazovaniya = Integration of Education. 2018; 22(3):426-440. (In Russ., abstract in Eng.) DOI: https://doi.org/10.15507/1991-9468.092.022.201803.426-440

25. Montenegro de Lima C.R., Coelho Soares T., Andrade de Lima M., Oliveira Veras M., Andrade Guerra J.B.S.O.d.A. Sustainability Funding in Higher Education: A Literature-Based Review. International Journal of Sustainability in Higher Education. 2020; 21(3):441-464. (In Eng.) DOI: https://doi.org/10.1108/IJSHE-07-2019-0229

Submitted 01.06.2020; accepted for publication 30.06.2020; published online 30.09.2020.

About the authors:

Igor V. Arzhenovskiy, Professor, Department of Organization and Economics of Construction, Nizhny Novgorod State University of Architecture and Civil Engineering (65 Ilyinskaya St., Nizhny Novgorod 603950, Russia), Ph. D. (Economics), ORCID: http://orcid.org/0000-0002-4710-4902, Researcher ID: http://www.researcherid.com/rid/H-7906-2018, igor.arzhenovskiy@gmail.com

Andrey V. Dakhin, Head of the Base Department of State and Municipal Administration, Nizhny Novgorod Institute of Management Branch of the Russian Presidential Academy of National Economy and Public Administration (46 Gagarina Ave., Nizhny Novgorod 603950, Russia), Dr. Sci. (Philosophy), Full Professor, ORCID: http://orcid.org/0000-0001-5907-706X, Researcher ID: http://www.researcherid.com/rid/E-7714-2019, Scopus ID: https://www.scopus.com/authid/detail.uri?origin=resultslist&authorId=571..., nn9222@yandex.ru

Contribution of the authors:

Igor V. Arzhenovskiy – preparation of the initial version of the text; collection of data and evidence; critical analysis and revision of the text.

Andrey V. Dakhin – academic supervision; choice of research methodology; critical analysis and revision of the text.

For citation:

Arzhenovskiy I.V., Dakhin A.V. Cognitive Regionology: The Experience of Modeling Regional Socio-Economic Processes. Regionology = Russian Journal of Regional Studies. 2020; 28(3):470-489. DOI: https://doi.org/10.15507/2413-1407.112.028.202003.470-489

The authors have read and approved the final version of the manuscript.
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