Neural network forecasting algorithm as a tool for assessing human capital trends of the socio-economic system

Автор: Ketova Karolina V., Vavilova Diana D.

Журнал: Economic and Social Changes: Facts, Trends, Forecast @volnc-esc-en

Рубрика: Modeling and forecast of socio-economic processes

Статья в выпуске: 6 т.13, 2020 года.

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The article addresses the issue of neural network forecasting of human capital size, structure, and dynamics. The object of the research is the socio-economic system. The subject of the study is the practice of applying neural network models to forecasting socio-economic indicators, human capital in particular. The purpose of this work is to apply neural network modeling and adapt its algorithms to build a forecast of the studied indicator for the future. The statistical base is data on demography, the investment volume in human capital of the regional economic system, as well as the indicators of socio-economic development. The investment volume in human capital is determined by budget and private citizens’ expenditures. To forecast the human capital dynamics the authors used the values of investment volumes the forecast of which, in turn, is built using a neural network modeling. The neural network model used in this study is a multi-layer fully connected perceptron with a sigmoid logistic activation function. Neural network modeling of forecast values of investment volumes has shown its effectiveness. Human capital assessment for the period of 2000-2018 and its forecast for the period of 2019-2023 are based on the example of the regional economic system of the Udmurt Republic. Our calculations show that the highest growth rate of the studied indicator has been demonstrated since 2013, and its further increase is predicted. The results obtained correlate qualitatively with the dynamics of changes in the Russian human development index, determined by the UN experts. The proposed method of calculating and forecasting human capital can be used to assess and compare the socio-economic situation of Russia’s regions.

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Human capital, neural network modeling, algorithm forecast, investments, socio-economic system

Короткий адрес: https://readera.org/147225499

IDR: 147225499   |   DOI: 10.15838/esc.2020.6.72.7

Список литературы Neural network forecasting algorithm as a tool for assessing human capital trends of the socio-economic system

  • Mincer J. Investments in human capital and personal income distribution. Journal of Political Economy, 1958, vol. LXVI, no. 4, pp. 281–302.
  • Schulz T. Investment in human capital. American Economic Review. 1961, vol. 51, no. 1, pp. 1–17.
  • Becker G.S. Investment in human capital: A theoretical analysis. Journal of Political Economy, 1962, vol. 70, no. 5, pp. 9–49.
  • Kuznets S. Quantitative aspects of the economic growth of nations. VIII: Distribution of income by size. Economic Development and Cultural Change, 1963, vol. 11, no. 2, part 2, pp. 1–80.
  • Ehrenberg R.G., Smith R.S. Sovremennaya ekonomika truda. Teoriya i gosudarstvennaya politika [Modern Labor Economics. Theory and Public Policy]. Moscow: MSU publishing, 1996. 198 p.
  • Koritskii A.V. Vvedenie v teoriyu chelovecheskogo kapitala [Introduction in the Theory of the Human Capital]. Novosibirsk: SibUPK publishing, 2000. 112 p.
  • Akopyan A.S., Bushuev V.V., Golubev V.S. Ergodynamic model of man and human capital. Obshchestvennye nauki i sovremennost’=Social Sciences and Contemporary World, 2002, no. 6, pp. 98–106 (in Russian).
  • Malkov S.Yu., Bolokhova K.A., Davydova O.I. Evaluation and prediction model of human capital development. Ekonomika i upravlenie: problemy, resheniya=Economics and management: problems, solutions, 2016, no. 7, pp. 7–16 (in Russian).
  • Aivazian S.A., Stepanov V.S., Kozlova M.I. Measuring the synthetic categories of quality of life in a region and identification of main trends to improve the social and economic policy (Samara region and its constituent territories). Prikladnaya ekonometrika=Applied Econometrics, 2006, no. 2, pp. 18–84 (in Russian).
  • Xu Y., Li A. The relationship between innovative human capital and interprovincial economic growth based on panel data model and spatial econometrics. Journal of computational and applied mathematics, 2020. DOI: 10.1016/j.cam.2019.112381
  • Timerbulatov R.M. Investment in human capital as a factor of improving company competitiveness. Vestnik Saratovskogo gosudarstvennogo sotsial’no-ekonomicheskogo universiteta=Vestnik of Saratov State Socio-Economic University, 2016, no. 1, pp. 40–42 (in Russian).
  • Kitaeva L.V., Khaibulaev Kh.U. Investments in human capital: problems of theory and practice. Vestnik ekspertnogo soveta=Expert Council Bulletin, 2018, no. 1–2 (12–13), pp. 93–100 (in Russian).
  • Chernov G.E., Chernova E.V. Human capital as a key vector of economic development in the XXI century. Obshchestvo: politika, ekonomika, pravo=Society: Politics, Economics, Law, 2016, no. 11, pp. 54–61 (in Russian).
  • Ryabykh V.N., Ryabykh E.B. The social- economic aspect of human capital in modern globalizing economy. Vestnik Tambovskogo universiteta. Seriya: Gumanitarnye nauki=Tambov University Review. Series: Humanities, 2015, no. 9 (149), pp. 129–136 (in Russian).
  • Serebryakova N.A., Volkova S.A., Shendrikova O.O., Volkova T.A. The role of human capital in the modern economy and indicators of its evaluation. Vestnik VGUIT=Proceedings of the Voronezh State University of Engineering Technologies, 2017, vol. 79, no. 4, pp. 253–259. DOI: 10.20914/2310-1202-2017-4-253-259 (in Russian).
  • Mikhaleva O.M. The role of human capital in the innovative development of territories. Vestnik Bryanskogo gosudarstvennogo universiteta=The Bryansk State University Herald, 2019, no. 1, pp. 183–189 (in Russian).
  • Stabinskaite Yu.A. The human capital rationale behind the economic growth of the European Union countries: application of the advanced methods to enhance an efficiency of national human capital stocks. Vestnik Rossiiskogo universiteta druzhby narodov. Seriya: Ekonomika=RUDN Journal of Economics, 2019, vol. 27, no. 1, pp. 35–48. DOI: 10.22363/2313-2329-2019-27-1-35-48 (in Russian).
  • Ketova K.V. Matematicheskie modeli ekonomicheskoi dinamiki: monografiya [Mathematical Models of Economic Dynamics: Monograph]. Izhevsk: IzhGTU, 2013. 284 p.
  • Schmidhuber J. Deep learning in neural networks: An overview. Neural Networks, 2015, vol. 61, pp. 85–117. DOI: 10.1016/j.neunet.2014.09.003
  • Nguyen G., Dlugolinsky S., Bobk M. Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: A survey. Artificial Intelligence Review, 2019, vol. 52, pp. 77–124. DOI: 10.1007/s10462-018-09679-z
  • Vavilova D.D., Ketova K.V., Kasatkina E.V. Application of genetic algorithm for adjusting the structure of multilayered neural network for prediction of investment processes. In: Materialy VIII Mezhdunarodnoi konferentsii «Tekhnicheskie universitety: integratsiya s evropeiskimi i mirovymi sistemami obrazovaniya» [Proceedings of the VIIII International Conference "Technical Universities: Integration with European and World Education Systems"]. 2019, vol. 1, pp. 223–233 (in Russian).
  • Tsoy Yu.R., Spitsyn V.G. Evolutionary approach to design and training of artificial neural networks. Neiroinformatika=Neuroformatics, 2006, vol. 1, no. 1, pp. 34–61 (in Russian).
  • Yasnitskii L.N. Intellektual’nye informatsionnye tekhnologii i sistemy [Intelligent Information Technologies and Systems]. Perm: Perm State University, 2007. 271 p.
  • Effati S., Nazemi A. Neural network models and its application for solving linear and quadratic programming problems. Applied Mathematics and Computation, 2006, vol. 172, no. 1, pp. 305–331. DOI: 10.1016/j.amc.2005.02.005
  • Ghanbarzadeh M., Aminghafari M. A novel wavelet artificial neural networks method to predict nonstationary time series. Communications in Statistics-Theory and Methods, 2018, vol. 49, no. 4, pp. 864–878. DOI: 10.1080/03610926.2018.1549259
  • McCulloch W.S., Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys., 1943, vol. 5, pp. 115–133.
  • Hebb D.O. The Organization of Behavior: A Neuropsychological Theory. Wiley, 1949. 335 p.
  • Rosenblatt F. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Washington, D.C. Spartan books, 1962. 480 p.
  • Kohonen T. Self-Organizing Maps (Third Extended Edition). New York, 2001. 501 p.
  • Grossberg S. Competitive learning: From interactive activation to adaptive resonance. Cognitive Science, 1987, vol. 11, no. 1, pp. 23–63.
  • Werbos P.J. Beyond regression: New tools for prediction and analysis in the behavioral sciences. Harvard University, Cambridge, 1974.
  • Minsky M.L., Papert S. Perceptrons: An Introduction to Computational Geometry. Cambridge, Mass., 1969. 112 p.
  • Fukushima К., Miyake S., Takayuki I. Neocognitron: A neural network model for a mechanism of visual pattern recognition. IEEE Transaction on Systems, Man and Cybernetics SMC, 1983, vol. 13(5), pp. 26–34.
  • Hopfield J.J., Tank D.W. Neural computation of decisions in optimization problems. Biological Cybernetics, 1985, vol. 52, no. 3, pp. 141–152.
  • Haykin S. Neural Networks: A Comprehensive Foundation. United States, 1998. 842 p.
  • Hecht-Nielsen R. Confabulation Theory. Springer-Verlag: Berlin, Heidelberg, 2007. 116 р.
  • Yunusova L.R., Magsumova A.R. Algorithms for learning artificial neural networks. Problemy nauki=Science Problems, 2019, pp. 21–25 (in Russian).
  • Mitinskaya A.N., Matych M.A. Research of the forecasting problem using neural networks. Aktual’nye napravleniya nauchnykh issledovanii XXI veka: teoriya i praktika=Actual Directions of Scientific Researches of the XXI Century: Theory and Practice, 2015, vol. 3, no. 7-2 (18-2), pp. 30–31. DOI: 10.12737/15021 (in Russian).
  • Shagalova P.A. Implementation of the pattern recognition system for time series analysis based on the artificial neural network. Trudy Nizhegorodskogo gosudarstvennogo tekhnicheskogo universiteta im. R.E. Alekseeva=Transactions of NNSTU n.a. R.Е. Alekseev, 2015, no. 3(110), pp. 85–90 (in Russian).
  • Cavarretta F., Naldi G. Mathematical study of a nonlinear neuron model with active dendrites. Aims Mathematics, vol. 4, no. 3, pp. 831–846. DOI: 10.3934/math.2019.3.831
  • Aivazian S.A., Afanas’ev M.Yu., Kudrov A.V. Indicators of the main directions of socio-economic development in the space of characteristics of regional differentiation. Prikladnaya ekonometrika=Applied Econometrics, 2019, no. 2 (54), pp. 51–62 (in Russian).
  • Ketova K.V., Rusyak I.G., Romanovskii Yu.M. Mathematical modeling of the human capital dynamic. Komp’yuternye issledovaniya i modelirovanie=Computer Research and Modeling, 2019, vol. 11, no. 2, pp. 329–342. DOI: 10.20537/2076-7633-2019-11-2-329-342 (in Russian).
  • Rusyak I.G., Ketova K.V. Identification and forecast of generalized indicators of regional economic system development. Prikladnaya ekonometrika=Applied Econometrics, 2009, no. 3 (15), pp. 56–71 (in Russian).
  • Rutkovskaya D., Pilin’skii M., Rutkovskii L. Neironnye seti. geneticheskie algoritmy i nechetkie sistemy [Neural Networks. Genetic Algorithms and Fuzzy Systems]. Moscow: Goryachaya liniya – Telekom, 2006.
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