The Management of Regional Information Space in the Conditions of Digital Economy
DOI:
https://doi.org/10.17059/2018-4-11Keywords:
digital economy, technological revolution, neural network, management, entropy approach, regional information spaces, network convergence, neural network effects, infrastructure, informatizationAbstract
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.References
Lieberman, M. D. (2007). Social cognitive neuroscience: a review of core processes. Annu. Rev. Psychol., 58. 259–289. doi 10.1146/annurev.psych.58.110405.085654.
Varma, S., McCandliss, B. D. & Schwartz, D. L. (2008). Scientific and pragmatic challenges for bridging education and neuroscience. Educ. Res., 37, 140–152. doi 10.3102/0013189X08317687.
Glimcher, P. W. & Rustichini, A. (2004). Neuroeconomics: the consilience of brain and decision. Science, 306, 447–452.
Klyucharev, V. A., Shmids, A. & Shestakova, A. N. (2011). Neyroekonomika. Neyrobiologiya prinyatiya resheniy [Neuroeconomics: the neurobiology of decision-making] Eksperimentalnaya psikhologiya [Experimental Psychology], 2, 14-35. (In Russ.)
Romanovskiy, A. V. & Shokin, Ya. V. (2010). Neyroekonomika i eyo integratsiya v ekonomicheskuyu nauku [Neuroeconomics and its integration into economic science]. Ekonomicheskie nauki [Economic sciences], 70, 42-44. (In Russ.)
Solodov, A. K. (2015). Sistemnaya neyroekonomika. Opyt postroeniya modeli natsionalnoy ekonomicheskoy sistemy i obespechivayushchey eyo podsistemy finansovogo menedzhmenta. Model sotsialnogo rynka [System Neuroeconomics: the experience of building a model of the national economic system and the financial management subsystem providing it (a model of the social market)]. Moscow, 164. (In Russ.)
Tapscott, D. (1997). The Digital Economy: Promise and Peril In The Age of Networked Intelligence. McGraw-Hill, 368; 1-37. ISBN 0-07-063342-8.
Mesenbourg, T. L. Measuring the Digital Economy. U.S. Bureau of the Census. Retrieved from: http://www.census.gov/content/dam/ Census/library/working-papers/2001/econ/digitalecon.pdf (date of access: 03.04.2018).
Fournier, L. Merchant Sharing. Cornell University Library arXiv:1405.2051. Retrieved from: https://arxiv.org/pdf/1405.2051 (date of access: 18.04.2018).
Vayber, R. (2003). Empiricheskie zakony setevoy ekonomiki [Empirical laws of the network economy]. Problemy teorii i praktiki upravleniya [Theoretical and Practical Aspects of Management], 4, 86-91. (In Russ.)
Varian, H. R. (2005). Ekonomicheskaya teoriya informatsionnykh tekhnologiy. Sotsialno-ekonomicheskie problemy informatsionnogo obshchestva [Economic theory of information technologies: Social and economic problems of the information society]. In: L. G. Melnik (Ed.). Sumy: ITD “Universitetskaya kniga” Publ., 430; 265, 226. (In Russ.)
Knool, S. (2008). Cross-Business Synergies: A Typology of Cross-business Synergies and a Mid-range Theory of Continuous Growth Synergy Realization. Wiesbaden, 388.
Dyatlov, S. A. (2017). Eneyro-setevaya giperkonkurentnaya ekonomika. Monografiya [E-neural-network hypercompetitive economy]. St. Petersburg: SPbGEU Publ., 133. (In Russ.)
Bellaïche, J.-M., Chassaing, T. & Kapadia, S. Digital’s Disruption of Consumer Goods and Retail. The Boston Consulting Group. Retrieved from: https://www.bcg.com/publications/2012/retail-consumer-products-digitals-disruption.aspx (date of access: 18.04.2018).
Bughin, J. & Manyika, J. (2013). Internet matters: Essays in digital transformation. McKinsey & Company. New Media Australia, 236.
Karlik, A. E. & Platonov, V. V. (2016). Mezhotraslevyye territorialnyye innovatsionnye seti [Cross-Industry Spatially Localized Innovation Networks]. Ekonomika regiona [Economy of Region], 12(4), 1218-1232. doi: 10.17059/2016-4-22. (In Russ.)
Dyatlov, S. A. & Lobanov, O. S. (2017). NBIC Convergence as a Stage of Transition of Saint-Petersburg’s E-Government Information Space to the Sixth Techno-Economic Paradigm. Communications in Computer and Information Science, 745, 347-361. doi: 10.1007/978-3-319-69784-0_30.
Dyatlov, S. A., Lobanov, O. S. & Selischeva, T. A. (2017). Information space convergence as a new stage of e-governance development in Eurasian economic space. ACM International Conference Proceeding Series, Part F130282, 99-106. doi: 10.1145/3129757.3129775.
Kovalchuk, M. V. (2011). Konvergentsiya nauk i tekhnologiy – proryv v budushcheye [Convergence of sciences and technologies – breakthrough to the future]. Rossiyskie nanotekhnologii [Nanotechnologies in Russia], 6(1-2), 13–23. (In Russ.)
Koka, B. & Prescott, J. (2002). Strategic Alliances as Social Capital: A Multidimensional View. Strategic Management Journal, 23(9), 795–816.
Shannon, C. (1948). A mathematical theory of communication. Bell System Tech. J., 27, 379–423.
Hartley, R. V. L. (1928). Transmission of Information. Bell Syst. Tech. J., 7, 535–563.
Gavrilova, T. & Khoroshevsky, V. (2000). Bazy znaniy intellektual’nykh sistem: Uchebnik dlya vuzov [Knowledge bases in intellectual systems: Textbook for high schools]. St. Petersburg: Piter Publ., 384. ISBN 5-94723-449-1. (In Russ.)
Plassmann, H., Venkatraman, V., Huettel, S. & Yoon, C. (2015). Consumer neuroscience: applications, challenges, and possible solutions. J. Mark. Res., 52, 427–435. doi: 10.1509/jmr.14.0048.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2018 Sergey Alexeevich Dyatlov, Oleg Sergeevich Lobanov, Weidi Zhou

This work is licensed under a Creative Commons Attribution 4.0 International License.

