The Management of Regional Information Space in the Conditions of Digital Economy

Strgey Dyatlov, Oleg Lobanov and Weidi Zhou
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Strgey Dyatlov: St. Petersburg State University of Economics
Oleg Lobanov: St. Petersburg State University of Economics
Weidi Zhou: School of Economics & Business Administration, Central China Normal University

Economy of region, 2018, vol. 1, issue 4, 1194 - 1206

Abstract: The article suggests an original uniquely designed model based on the entropic approach and the method determining the synergizing effect from the convergence of information spaces in the context of the digital economy. The model includes a 3D-modeling-built surface characterizing the reduction of the entropy of information systems clusters in the regional information space, which occurs in the process of network convergence. This model defines the entropy changes for the information systems clusters with the most typical parameters based on “The State Information System Registry of St. Petersburg†in terms of the number of modules, general typology, and functional purpose. Moreover, the model considers ranges of specific indicators characterizing the real regional information systems of St. Petersburg. We have concluded that the synergetic effects of convergence in the context of the digital economy lead to a reduction in the regional information space entropy. We have discovered that the increasing number of the converged clusters of information spaces leads to a stable entropy decrease in them. These features allow numerically describing the discovered convergence effects and estimating the effect of digital structural transformations of the economic system on the information space of a region in terms of its management efficiency. We have concluded that increasing the number of information systems involved in the digital convergence processes causes a more considerable entropy reduction and, consequently, a more significant increase in the effectiveness of regional system management. The research has revealed a relevant area of cross-disciplinary research, which consists in the emergence of a whole class of new neural network in the modern digital neural network economy. This research is of practical significance in developing new management algorithms and making effective managerial decisions in the conditions of large-scale digitalization and networking of regional and national management.

Keywords: digital economy; technological revolution; neural network; management; entropy approach; regional information spaces; network convergence; neural network effects; infrastructure; informatization (search for similar items in EconPapers)
JEL-codes: F (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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