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Computer Research and Modeling, 2020, Volume 12, Issue 5, Pages 1223–1245
DOI: https://doi.org/10.20537/2076-7633-2020-12-5-1223-1245
(Mi crm844)
 

This article is cited in 1 scientific paper (total in 1 paper)

MODELS OF ECONOMIC AND SOCIAL SYSTEMS

Multicriterial metric data analysis in human capital modelling

G. K. Kameneva, I. G. Kamenevab

a GBI Federal Research Center of Computer Science and Control Dorodnitsyn Computing Centre of the Russian Academy of Sciences, 40 Vavilova st., Moscow, 119333, Russia
b FGAEI HE National research university “Higher school of economics” 20 Myasnickaya st., Moscow, 101000, Russia
Full-text PDF (744 kB) Citations (1)
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Abstract: The article describes a model of a human in the informational economy and demonstrates the multicriteria optimizational approach to the metric analysis of model-generated data. The traditional approach using the identification and study involves the model's identification by time series and its further prediction. However, this is not possible when some variables are not explicitly observed and only some typical borders or population features are known, which is often the case in the social sciences, making some models pure theoretical. To avoid this problem, we propose a method of metric data analysis (MMDA) for identification and study of such models, based on the construction and analysis of the Kolmogorov–Shannon metric nets of the general population in a multidimensional space of social characteristics. Using this method, the coefficients of the model are identified and the features of its phase trajectories are studied. In this paper, we are describing human according to his role in information processing, considering his awareness and cognitive abilities. We construct two lifetime indices of human capital: creative individual (generalizing cognitive abilities) and productive (generalizing the amount of information mastered by a person) and formulate the problem of their multi-criteria (two-criteria) optimization taking into account life expectancy. This approach allows us to identify and economically justify the new requirements for the education system and the information environment of human existence. It is shown that the Pareto-frontier exists in the optimization problem, and its type depends on the mortality rates: at high life expectancy there is one dominant solution, while for lower life expectancy there are different types of Pareto-frontier. In particular, the Pareto-principle applies to Russia: a significant increase in the creative human capital of an individual (summarizing his cognitive abilities) is possible due to a small decrease in the creative human capital (summarizing awareness). It is shown that the increase in life expectancy makes competence approach(focused on the development of cognitive abilities) being optimal, while for low life expectancy the knowledge approach is preferable.
Keywords: multicriteria optimization, metric net, data visualization, human development, model identification, feasible goals method, interactive decision maps, human capital, metric data analysis.
Funding agency Grant number
Russian Foundation for Basic Research 18-01-00465
The reported study was co-funded by RFBR, project number: 18-01-00465 a “Development of social data multidimensional metric analysis methods”.
Received: 19.12.2019
Revised: 11.07.2020
Accepted: 17.07.2020
Document Type: Article
UDC: 51-77, 517.977.5, 519.86, 303.094, 330.45, 331.101.26
Language: English
Citation: G. K. Kamenev, I. G. Kamenev, “Multicriterial metric data analysis in human capital modelling”, Computer Research and Modeling, 12:5 (2020), 1223–1245
Citation in format AMSBIB
\Bibitem{KamKam20}
\by G.~K.~Kamenev, I.~G.~Kamenev
\paper Multicriterial metric data analysis in human capital modelling
\jour Computer Research and Modeling
\yr 2020
\vol 12
\issue 5
\pages 1223--1245
\mathnet{http://mi.mathnet.ru/crm844}
\crossref{https://doi.org/10.20537/2076-7633-2020-12-5-1223-1245}
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  • https://www.mathnet.ru/eng/crm844
  • https://www.mathnet.ru/eng/crm/v12/i5/p1223
  • This publication is cited in the following 1 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Computer Research and Modeling
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    Full-text PDF :37
    References:21
     
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