Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/103348
Title: Development of multifactor dynamic model of forecasting of scientific migration
Authors: Sudakova, A. E.
Tarasyev, A. A.
Issue Date: 2020
Publisher: American Institute of Physics Inc.
Citation: Sudakova A. E. Development of multifactor dynamic model of forecasting of scientific migration / A. E. Sudakova, A. A. Tarasyev. — DOI 10.1063/5.0027280 // AIP Conference Proceedings. — 2020. — Vol. 2293. — 120009.
Abstract: The quality of human capital plays the key role in the technological and economic development of modern societies. On the current stage, many countries require highly skilled specialists due to a razing intensity of knowledge in technology. This is explained by the fact that highly skilled specialists contribute to the acceleration of knowledge accumulation, innovation implementation, and lead to the development of socio-economic development due to a higher level of economic activity. Therefore, to provide the country's development and competitiveness at the global level, it is important to maintain the academic exchange and to attract highly skilled specialists. Russia has a number of peculiarities in the migration process of highly skilled specialists. On the one hand, the country is experiencing the consequences of mass emigration of highly skilled specialists in the 20th century; on the other hand, it has a low ability to attract qualified specialists from other countries due to the specifics of the development of the socio-economic system. Features of socioeconomic development of the country lead to the fact that currently in Russia obtain a unidirectional nature of the migration process of highly qualified personnel. Migration on a permanent basis and the mobility of the scientist has a different impact on the domestic science, on the increment of its intellectual capital. To solve effectively the problem of scientific emigration, it is necessary to assess the potential of scientific emigration and to be able to assess and predict the scale and direction of potential flows of scientific migration. The existing models and model complexes take into account macroeconomic dependencies to the detriment of the description of individual behavior, which limits the applicability of models to describe migration in a changing market environment. Due to the lack of a model complex that allows describing the variable dynamics of scientific migration between the labor markets at the macro level and scientific organizations at the micro level, it is necessary to analyze and evaluate methods for forecasting specific cases of labor resources redistribution between organizations, and to structure the main forecast methods. As a result, we developed a multivariate dynamic model of forecasting scientific migration with elements of the theory of positional games. © 2020 American Institute of Physics Inc.. All rights reserved.
Keywords: BEHAVIORAL ECONOMICS
BIBLIOMETRIC ASSESSMENT
BIG DATA
BRAIN DRAIN
DATA MINING
DYNAMIC MODEL
OPTIMIZATION
SCIENTIFIC MIGRATION
URI: http://hdl.handle.net/10995/103348
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85097994599
PURE ID: 20391573
d6bb8c85-4dca-45ff-b835-29cfacb92290
ISSN: 0094243X
ISBN: 9780735440258
DOI: 10.1063/5.0027280
metadata.dc.description.sponsorship: Research is supported by the project of the Russian Science Foundation No. 19-78-00080, “Investigation of migration of Russian scientists on the basis of predictive modeling and analysis of data on Big Data and Data Science technologies”.
RSCF project card: 19-78-00080
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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