IDEAS home Printed from https://ideas.repec.org/a/url/upravl/v11y2020i1p45-56.html
   My bibliography  Save this article

Models of public investment management at regional level

Author

Listed:
  • Viktoria V. Akberdina

    (Institute of Economics of the Ural Branch of the RAS, Ural Federal University named after the First President of Russia B.N. Yeltsin, Ekaterinburg, Russia)

  • Andrey I. Volodin

    (University of Regina, Regina, Canada)

  • Roman V. Gubarev

    (Plekhanov Russian University of Economics, Moscow, Russia)

  • Evgeniy I. Dzyuba

    (Division of All-Russia People’s Front in Republic of Bashkortostan, Ufa, Russia)

  • Fanil’ S. Fayzullin

    (Institute of Social and Economic Research of the Ufa Federal Research Center of the RAS, Ufa, Russia)

Abstract

Despite significant funding of current national projects and state programs designed to make a breakthrough in the socioeconomic and scientific-technological development of Russia, the problem of creating a unified methodology for the development and implementation of investment and industrial policy remains unresolved. The proportion of methods using relevant tools of economicmathematical modeling and information technologies is still quite low. This issue is particularly acute at regional level. The study aims to substantiate the regional investment model as an effective tool for strategic management of the national economy and its practical implementation using information technologies. We develop and implement a regional investment model based on agent-oriented modeling. This model will allow the executive authorities of any subject of the Russian Federation to make effective management decisions and update the provisions of the regional investment and industrial policy in conditions of limited investment resources (budget funds). The methodological platform of the research is the synthesis of strategic management, indicative planning and reproductive approach. In the study, the methods of agent-oriented modeling and the modeling based on production functions are applied. The study of investment activity in Russia is conducted according to regional statistics (using data for 2017) with the use of artificial intelligence by the method of self-organizing Kohonen maps in a special software product Deductor Studio Lite. Using data for the Republic of Bashkortostan, we establish the possibility of applying production functions to describe functional dependencies in the author’s regional investment model.

Suggested Citation

  • Viktoria V. Akberdina & Andrey I. Volodin & Roman V. Gubarev & Evgeniy I. Dzyuba & Fanil’ S. Fayzullin, 2020. "Models of public investment management at regional level," Upravlenets, Ural State University of Economics, vol. 11(1), pages 45-56, March.
    • Handle: RePEc:url:upravl:v:11:y:2020:i:1:p:45-56
    DOI: 10.29141/2218-5003-2019-11-1-5
    as

    Download full text from publisher

    File URL: http://upravlenets.usue.ru/images/83/5.pdf
    Download Restriction: no
    File URL: http://upravlenets.usue.ru/ru/-2020/603
    Download Restriction: no
    File URL: https://libkey.io/10.29141/2218-5003-2019-11-1-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Sergey Svetunkov, 2016. "The Possibility Using the Power Production Function of Complex Variable for Economic Forecasting," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(3), pages 966-976.
    2. Valery Makarov & Sergey Ayvazyan & Mikhail Afanasyev & Albert Bakhtizin & Ashkhen Nanavyan, 2016. "Modeling the Development of Regional Economy and an Innovation Space Efficiency," Foresight-Russia Форсайт, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 10(3 (eng)), pages 76-90.
    3. Vladimir Okrepilov & Valeriy Makarov & Albert Bakhtizin & Svetlana Kuzmina, 2015. "Application of Supercomputer Technologies for Simulation Of Socio-Economic Systems," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(2), pages 301-313.
    4. Valery Makarov & Sergey Ayvazyan & Mikhail Afanasyev & Albert Bakhtizin & Ashkhen Nanavyan, 2016. "Modeling the Development of Regional Economy and an Innovation Space Efficiency," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 10(3), pages 76-91.
    5. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    6. Yakov P. SILIN & Yelena B. DVORYADKINA & Ivan A. ANTIPIN, 2018. "The priorities of the strategic development of a new industrial city," Upravlenets, Ural State University of Economics, vol. 9(6), pages 2-16, December.
    7. Boush, G. D. & Kulikova, О. М. & Shelkov, I. К., 2016. "Agent modelling of cluster formation processes in regional economic systems," R-Economy, Ural Federal University, Graduate School of Economics and Management, vol. 2(1), pages 89-101.
    8. Yulia V. DUBROVSKAYA & Elena V. KOZONOGOVA & Anatoly V. MOLODCHIK, 2019. "On algorithmization and automation of regional strategizing," Upravlenets, Ural State University of Economics, vol. 10(4), pages 65-74, September.
    9. Brian Heath & Raymond Hill & Frank Ciarallo, 2009. "A Survey of Agent-Based Modeling Practices (January 1998 to July 2008)," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-9.
    10. Galina Boush & Okxana Kulikova & Ivan Shelkov, 2016. "Agent Modelling of Cluster Formation Processes in Regional Economic Systems (p. 64-77)," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(1), pages 64-77.
    11. Tatiana Ladykova & Vladimir Bersenyov, 2018. "Typology of Macroeconomic Parameters of Population Income," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(2), pages 380-394.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shamis, V. A. & Kulikova, O. M. & Neiman, S. Y. & Usacheva, E. V., 2017. "Agent modeling of advertising impact on the regional economic cluster lifecycle," R-Economy, Ural Federal University, Graduate School of Economics and Management, vol. 3(4), pages 203-212.
    2. Jutta Günther & Dirk Meissner, 2017. "Clusters as Innovative Melting Pots?—the Meaning of Cluster Management for Knowledge Diffusion in Clusters," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 8(2), pages 499-512, June.
    3. Nina I. LARIONOVA & Tatyana V. YALYALIEVA & Dmitry L. NAPOLSKIKH, 2018. "Global Competitiveness, Neoindustrialization And Innovative Clusters: International Indicators And Trends Of Russian Federation," Revista Galega de Economía, University of Santiago de Compostela. Faculty of Economics and Business., vol. 27(2), pages 125-138.
    4. Vladimir Kolmakov & Aleksandra Polyakova & Svetlana Karpova & Alla Golovina, 2019. "Cluster Development Based on Competitive Specialization of Regions," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(1), pages 270-284.
    5. Dmitriy Aleksandrovich Abashev & Valery Vitalyevich Yanovskiy, 0. "Modern Approach to the Regional Strategic Planning: Main Directions and Tools," Administrative Consulting, Russian Presidential Academy of National Economy and Public Administration. North-West Institute of Management., issue 7.
    6. Hossein Sabzian & Mohammad Ali Shafia & Ali Maleki & Seyeed Mostapha Seyeed Hashemi & Ali Baghaei & Hossein Gharib, 2019. "Theories and Practice of Agent based Modeling: Some practical Implications for Economic Planners," Papers 1901.08932, arXiv.org.
    7. Lijian Wei & Xiong Xiong & Wei Zhang & Xue-Zhong He & Yongjie Zhang, 2017. "The effect of genetic algorithm learning with a classifier system in limit order markets," Published Paper Series 2017-3, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    8. Jonas Hauke & Sebastian Achter & Matthias Meyer, 2020. "Theory Development Via Replicated Simulations and the Added Value of Standards," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(1), pages 1-12.
    9. Bao, Te & Hommes, Cars & Pei, Jiaoying, 2021. "Expectation formation in finance and macroeconomics: A review of new experimental evidence," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    10. Rixen, Martin & Weigand, Jürgen, 2014. "Agent-based simulation of policy induced diffusion of smart meters," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 153-167.
    11. Bert, Federico E. & Rovere, Santiago L. & Macal, Charles M. & North, Michael J. & Podestá, Guillermo P., 2014. "Lessons from a comprehensive validation of an agent based-model: The experience of the Pampas Model of Argentinean agricultural systems," Ecological Modelling, Elsevier, vol. 273(C), pages 284-298.
    12. Gräbner, Claudius, 2015. "Formal Approaches to Socio Economic Policy Analysis - Past and Perspectives," MPRA Paper 61348, University Library of Munich, Germany.
    13. Tesfatsion, Leigh, 2021. "Agent-Based Computational Economics: Overview and Brief History," ISU General Staff Papers 202111080800001125, Iowa State University, Department of Economics.
    14. Sinitskaya, Ekaterina & Tesfatsion, Leigh, 2015. "Macroeconomies as constructively rational games," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 152-182.
    15. Frank M. A. Klingert & Matthias Meyer, 2012. "Effectively combining experimental economics and multi-agent simulation: suggestions for a procedural integration with an example from prediction markets research," Computational and Mathematical Organization Theory, Springer, vol. 18(1), pages 63-90, March.
    16. Tara Natarajan, 2018. "Formal Methods for Integrated Socioeconomic Analysis: An Introduction to the Special Issue," Forum for Social Economics, Taylor & Francis Journals, vol. 47(1), pages 1-7, January.
    17. Wood, Aaron D. & Mason, Charles F. & Finnoff, David, 2016. "OPEC, the Seven Sisters, and oil market dominance: An evolutionary game theory and agent-based modeling approach," Journal of Economic Behavior & Organization, Elsevier, vol. 132(PB), pages 66-78.
    18. Wood, Aaron D. & Berry, Kevin, 2024. "COVID-19 transmission in a resource dependent community with heterogeneous populations: An agent-based modeling approach," Economics & Human Biology, Elsevier, vol. 52(C).
    19. Ngo-Hoang, Dai-Long, 2019. "A research paper of Hossein Sabzian (2019), Theories and Practice of Agent based Modeling: Some practical Implications for Economic Planners, ArXiv, 54p," AgriXiv xutyz, Center for Open Science.
    20. Vincze, János & Varga, Gergely, 2016. "Megtakarítási típusok - egy adaptív-evolúciós megközelítés [Types of saving - an adaptive-evolutionary approach]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 162-187.

    More about this item

    Keywords

    strategic management; policy-indicative plan; regional investment model; agent-oriented approach; assessment of production capabilities; industrial complex of the region.;
    All these keywords.

    JEL classification:

    • L52 - Industrial Organization - - Regulation and Industrial Policy - - - Industrial Policy; Sectoral Planning Methods
    • O25 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - Industrial Policy
    • R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:url:upravl:v:11:y:2020:i:1:p:45-56. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Victor Blaginin (email available below). General contact details of provider: https://edirc.repec.org/data/usueeru.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.