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Aggregated Simulation Model of a Region: Problems of Krasnoyarsk Region
 
PIIS042473880005769-4-1
DOI10.31857/S042473880005769-4
Publication type Article
Status Published
Authors
Gayane Beklaryan 
Occupation: Senior Research Scholar
Affiliation:
Central Economics and Mathematics Institute, Russian Academy of Sciences
Russia
Address: Moscow, Russian Federation
Journal nameEkonomika i matematicheskie metody
Edition
Pages47-61
Abstract

The article presents an approach to the rational management of the region on the example of the Krasnoyarsk region using the methods of system dynamics. A simulation model of the region is developed, and the possibility of optimizing the key characteristics of such a system through the rational management of multiple parameters, such as the rate of construction of new housing, the rate of growth of the housing services’ costs, the rate of construction of social infrastructure (gardens, schools, hospitals), etc. The suggested model allows forming the forecast dynamics of the most important macroeconomic characteristics of the region, taking into account the internal direct and backward linkages between the various elements of such a system and the existing restrictions. The computer implementation of this model is performed in the simulation system Powersim, which supports the methods of system dynamics, as well as the possibility of finding suboptimal solutions using genetic optimization algorithms. An important optimization problem of the region to maximize the integral index — the Gross Regional Product under multiple constraints is formulated. To solve this optimization problem, a genetic optimization algorithm (GA) was chosen, the feature of which is the aggregation of the target functional with a simulation model of the region (implemented in Powersim). Numerical studies have been carried out to predict the GRP of the Krasnoyarsk Region under various scenario conditions, in particular, for the basic scenario, in which the current values of the control parameters of the system are stored and for the best scenario, in which the values of the corresponding control parameters are calculated as a result of solving the problem of the formulated optimization problem using the created optimization module (genetic algorithm). With the help of a simulation model on real data we demonstrated the possibility of improving the economic situation in the Krasnoyarsk Region, mainly due to increased investment in human capital, affecting the dynamics of scientific and technological progress and GRP, respectively.

Keywordssimulation modeling, regional economy, system dynamics, Krasnoyarsk Region.
Received13.08.2019
Publication date22.08.2019
Number of characters27863
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1. Aivazian S.A., Afanasiev M.Y, Kudrov A.V. (2016). Models of Productive Capacity and Technological Efficiency Evaluations of Regions of the Russian Federation Concerning the Output Structure. Economics and Mathematics Methods, 1, 28–44 (in Russian).
2. Akopov A.S. (2010). On the Issue of Developing of Intelligent Control Systems of Complex Organizational Structures (part I): Mathematical Support for Control System of the Vertically Integrated Oil Company Investment Activities. Problemy upravleniya, 6, 12–18 (in Russian).
3. Akopov A.S. (2012). System Dynamics Modeling of Banking Group Strategy. Business-Informatics, 2, 10–19 (in Russian).
4. Akopov A.S. (2011). On the Issue of Developing of Intelligent Control Systems of Complex Organizational Structures (part II): Software Support for Control System of the Vertically Integrated Oil Company Investment Activities. Problemy upravleniya, 1, 47–54 (in Russian).
5. Akopov A.S., Beklaryan G.L. (2014). Modelling the Dynamics of the “Smarter Region”. In: “Proceedings of 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics”. L.: IEEE, 203–209.
6. Akopov A.S., Beklaryan G.L., Beklaryan L.A. (2017). Agent-Based Modelling for Ecological-Economics System of a City (on Example of Yerevan, Republic of Armenia). Artificial Societies, 12 (3–4), 1–10 (in Russian).
7. Akopov A.S., Beklaryan L.A., Saghatelyan A.K. (2017). Agent-Based Modelling for Ecological Economics: A Case Study of the Republic of Armenia. Ecological Modelling, 346, 99–118.
8. Akopov A.S., Beklaryan L.A., Saghatelyan A.K. (2019). Agent-Based Modelling of Interactions between Air Pollutants and Greenery Using a Case Study of Yerevan, Armenia. Environmental Modelling and Software, 116, 7–25.
9. Arrow K.Z. (1962). The Economic Implications of Learning by Doing. Review of Economic studies, 29, 3, 155–173.
10. Bakhtizin A.R., Bukhwald E.M., Kolchugina A.V. (2017). Economic Differentiation of Regions of Russia: New Estimates and Patterns. ETAP: Economic Theory, Analysis, and Practice,  1, 41–56 (in Russian).
11. Beklaryan G.L. (2018). Aggregated Simulation Model of Foreign Economic Activity of the Russian Federation. Economics of Contemporary Russia, 4, 50–65 (in Russian).
12. Beklaryan L.A., Borisova S.V., Khachatryan N.K. (2012).  One-Product Dynamic Model of Replacement of Production Assets. Trunk Properties. Journal of Ñomputational Mathematics and Mathematical Physics, 52 (5), 801–817 (in Russian).
13. Cobb C.W., Douglas P.H. (1928). A Theory of Production. American Economic Review, 18, 139–165.
14. Forrester J.W. (1959). Industrial Dynamics — a Major Breakthrough for Decision Makers. Harvard Business Review, 36 (4), 37–66.
15. Forrester J.W. (1969). Urban Dynamics. Cambridge: MIT Press.
16. Ivanov Y.N. (ed.) (2002). Economic Statistics. [Ehkonomicheskaya statistika]. Ìoscow: INFRA-M (in Russian).
17. Kleiner G.B., Piontkovsky D.I. (2000). Multi-Factor Production Functions with Constant Marginalfactor Substitution Elasticity. Economics and Mathematics Methods, 36 (1), 90–114  (in Russian).
18. Komninos N. (2008). Intelligent Cities and Globalisation of Innovation Networks. London, New York: Routledge, 308.
19. Makarov V., Ayvazyan S., Afanasyev M., Bakhtizin A., Nanavyan A.M. (2016). Modeling the Development of Regional Economy and an Innovation Space Efficiency Foresight and STI Governance. Foresight, 10 (3), 76–90 (in Russian).
20. Makarov V.L., Bakhtizin A.R., Beklaryan G.L., Akopov A.S., Rovenskaya E.A., Strelkovskii N.V. (2019). Aggregated Agent-Based Simulation Model of Migration Flows of the European Union Countries. Economics and Mathematics Methods, 55 (1), 3–15 (in Russian).
21. Meadows D.H. (1972). Limits to Growth: A Report for the Club of Rome's Project on the Predicament of Mankind. New York: Universe Books.
22. Meadows D.H., Randers J. Meadows D.L. (2004). Limits to Growth — the 30 Year Update. Update. White River Jct. (VT): Chelsea Green Publ. Co.
23. Nordhaus W.D.? (2008).? A? Question? of? Balance:? Weighing? the? Options? on? Global? Warming? Policies. New Haven & London: Yale University Press.
24. Romer P.M. (1990). Endogenous Technological Change. Journal of Political Economy, 98, 5, 71–102.
25. Sidorenko V.N. (1998). System Dynamics. [Sistemnaya dinamika.] Ìoscow: Faculty of Economics, MSU, TEIS, 200 (in Russian).
26. Stewart Q. J. (1950). The Development of Social Physics. American Journal of Physics, 18, 239–253.
27. Whelpton P.K. (1928). Population of the United States, 1925 to 1970. The American Journal of Sociology, 34, 2, 253–270.
28. Yap Y.L. (1977). The Attraction of Cities: A Review of the Migration Literature. Journal of Development Economics, 4 (3), 239–264.

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