SIMULATION OF TRENDS AND LEGISLATION OF LABOR ACTIVITIES IN RUSSIA: COGNITIVE APPROACH
( Pp. 94-101)

The work is devoted to modeling of regularities and tendencies of labor activity of people in the conditions of Russia. The issues of improving the well-being of society through GDP per capita growth are projected on the effectiveness of existing social, technological, economic and political macroeconomic links by the method of cognitive modeling. Increasing the efficiency of the use of human capital and increasing labor productivity are the focus of research. Traditional methods of mathematical modeling, as a rule, do not cope with tasks containing more than 20 significant parameters of real economic activity, but cognitive methods using expert cybernetic decision support systems make such complex problems really solvable [1]. Authors identified key concepts of STEP-environment, affecting the effectiveness of people's work, focusing on the formation of human capital in Russia in the current circumstances. The paper substantiates that a systemic innovation leap in all spheres of society, in accordance with strategic plans for the development of the economy, initiates the rapid growth of GDP per capita and the share of human capital in the composition of national wealth. A prerequisite for innovative development is a rapid decrease in the level of depreciation of fixed assets and activation of activities in the field of R & D. The results of the work can be applied in the strategic planning of the development of labor trends in key areas of society. Scenarios of GDP growth per capita, obtained with dynamic modeling, give grounds for making decisions in the field of increasing labor productivity and the growth of public welfare.

Melnik M.S., Viktor D.O., Olga S.P., (2018), SIMULATION OF TRENDS AND LEGISLATION OF LABOR ACTIVITIES IN RUSSIA: COGNITIVE APPROACH. Economic Problems and Legal Practice, 3 => 94-101.

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labor activity, human capital, GDP per capita, Of step-factors, productivity, depreciation of fixed assets, cognitive modeling, fuzzy cognitive matrix.