Intelligent decision support system for iterative analysis of dysfunctions of the institutional system of human capital development based on neural structures

¹ Expert and Analytical Center, 33, Talalikhina str., Moscow, 109316, Russia
² Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, 40, Vavilov Street, Moscow, 119333, Russia
³ Central Economics and Mathematics Institute of Russian Academy of Sciences, 47, Nakhimovsky Prospekt, Moscow, 117418, Russia
⁴ National University of Science & Technology MISiS, 4, Leninsky Prospect, Moscow, 119049, Russia
⁵ Marine Hydrophysical Institute, Russian Academy of Sciences, 2, Kapitanskaya str., Sevastopol, 299011, Russia

^* Corresponding author: kartsan2003@mail.ru

Published online: 10 March 2025

Abstract

Intelligent analysis of dysfunctions of the institutional system of human capital development is an iterative process of extracting useful information and decision-making templates from organized or unorganized arrays of big data of different formats. However, existing algorithms and approaches to intelligent analysis of human capital development data cannot be applied to solving problems of effective implementation of innovation programs, since they have a specific goal aimed at creating the results of intellectual activity. As a methodological basis for the system analysis of human capital development in the digital economy, it is proposed to use ensembles of decision trees and models of the Markov decision-making process together. A software and analytical toolkit for forecasting time series based on neural structures has been developed. For the iterative analysis of dysfunctions of the national innovation system, a multi-step forecasting of the human capital development trend has been performed and forecasting horizons in the digital economy have been determined.