Выполнено математическое моделирование величины и структуры человеческого капитала РФ. Построен прогноз его динамики до 2025 г. с использованием двумерного уравнения переноса, в котором учтены время и возраст демографических элементов, а также прогнозные объемы бюджетных и частных инвестиций в человеческий капитал, полученные по многослойной нейросетевой модели. Построенные прогнозы удовлетворяют заданной точности.
This paper presents a programming and computing suite which comprises regional socio-economic parameters database (gross regional product, physical capital, human capital and their investment volume), an analytical subsystem which renders mathematical models of parameters’ analysis and forecasting, as well as visual representation unit of modeling and forecasting results. In view of the results, a system research report is built, providing information on socio-economic condition of the region. The calculations are performed by the example of the Udmurt Republic.
Purpose of the research. Human capital in the modern world is the primary factor in ensuring the progressive development of society. The most important component of human capital is the component of health, which affects the quality of labor resources and labor productivity in the region. The purpose of this research is to study the development trends of the health component of human capital, using the example of the socio-economic system of one of the regions of the Russian Federation – the Udmurt Republic, to study the structure and dynamics of the specified object of research.Materials and methods. Statistical methods and techniques for studying the development of regional socio-economic processes as an information-supported element in making managerial decisions in strategic planning were used instrumental in this research. The information resource is based on official statistical data of institutions and departments of the Russian Federation and the data from periodicals, as well as using expert assessments in their information and analytical materials. The research base is the socio-economic processes of the last two decades in the Udmurt Republic. A numerical analysis of the structure and dynamics of the state of health of the population is carried out on the example of the Udmurt Republic, using primary modern data reflected in the system of state statistical accounting for the period 2000-2018.Results. Calculations have shown that the quality of the health component of human capital in the Udmurt Republic has been declining in recent decades. The share of healthy people in the region in the working age group of the population 15-72 years old decreased from 59.8% in 2000 to 42.1% in 2018. At the same time, the proportion of people with chronic diseases increased: 33.2% in 2000 and 48.5% in 2018. In general, the share of people with disabilities in the total population of the age group 15-72 years old increased from 7.0% to 9.4%. The general level of health of the population is declining at an average annual rate of 0.4 percentage points. This situation is due to both the aging factor of the population and the general trend of deteriorating health of the population in middle ages.Conclusion. The revealed and analyzed trends in the work of the structure and dynamics of the human capital health component of the population in socio-economic system indicate a decrease in the rate of positive influence of human capital on economic dynamics and the labor market. The analysis showed the emergence of the need to create additional conditions to reduce the level of general morbidity and disability. The results obtained indicate the need to increase the volume of funding for the health care system in order to expand the scale of involvement of the population in a healthy lifestyle, develop a preventive health care system, improve the availability and quality of medical care.
The paper is presented an effective adaptive forecasting system based on combining mathematical modeling tools, including neural networks and genetic algorithm. The construction of the neural network structure that is best relative to the selected criterion makes it possible to improve the procedure for finding a solution to the problem in terms of a number of parameters. Each individual in the genetic algorithm is encoded as a vector with data on the number of neurons on the intermediate layers of the neural network. The evolution of the population occurs in the genetic algorithm, information in the chromosomes changes as a result of the probabilistic application of genetic operators. As a result, such a structure of the neural network is formed, at which the convergence to a given level of error of 1.0% is the fastest. Applied calculations were carried out on the monthly statistical data of investments in human capital (education, healthcare and culture) of the Udmurt Republic. The proposed adaptive system, applied to the construction of forecasts of socio-economic indicators, can be used in the construction of development strategies both at the regional level and at the country level.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.