Abstract
Purpose: The paper is prepared in order to substantiate the model of information and analytical complex, which allows monitoring the processes of formation and realization of motivational potential of the population in the system of public administration, considered as a social technology of institutional ensuring the competitiveness of the region.
Design/Methodology/Approach: The achievement of this goal was facilitated by the use of such analytical methods as structural, complex and monographic ones, as well as private methods: typologization, structuring, comparison, verification, ranking, graphical extrapolation, economic and mathematical modeling within the framework of the fundamental system-functional approach to the object under study.
Findings: The proposed model is based on the use of modern methods of mathematical statistics (linear regression, Lasso regression and ARD regression) and methods of deep machine learning (neural networks). The advantage of the proposed economic and mathematical model in comparison with standard regression models is the ability to build effective estimates of coefficients for binary endogenous variables.
Originality/Value: Quantification of the factors interacting in formation and realization of motivational potential of the region’s population in the system of public administration will contribute to identifying the development trends of civil activity of the regional population and establishing tools to increase the citizens’ participation in the political process, and therefore neutralize provoking their opportunistic behavior in the system of public administration.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Coelho, L.P., Richard, V.: Building Machine Learning Systems in Python. DMK Press, Moscow (2016)
Inshakov, O.: “Institutional” man is the subject of socially sanctified action. In: Inshakov, O. (ed.) Homo institutius, pp. 59–87. VSU Publishing House, Volgograd (2005)
Kleiner, G.: Agents and institutions: on the problem of institutional choice. In: Inshakov, O. (ed.) Homo institutius, pp. 87–112. VSU Publishing House, Volgograd (2005)
Loginova, E., Filippov, A.: Public administration in postmodern coordinates. In: Politics and Society, no. 12, pp. 41–51 (2018)
Loginova, E., Loseva, N., Polkovnikov, A.: Model of the assessment of the implementation of public administration in the regions of Russia. In: Competitive, Sustainable and Secure Development of the Regional Economy: Response to Global Challenges Proceedings of the International Scientific Conference in Volgograd, Russia, Atlantis Press, Paris, pp. 364–369 (2018)
Loginova, E., Loseva, N., Polkovnikov, A.: Model-methodical complex of analysis and forecasting of public administration implementation in regional practice. Trends and management, no 4, pp. 17–24 (2018a)
MacKay, D.J.C.: A practical Bayesian framework for backpropagation networks. Neural Comput. 4(3), 448–472 (1992)
Nikolenko, S., Kadurin, A., Arkhangelsky, E.: Deep Learning. Peter, SPb (2018)
Protest potential (2019). https://wciom.ru/news/ratings/protestnyj_potencial/. Accessed 16 Aug 2019
Scholle, F.: Deep Learning in Python. Peter, SPb (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Loginova, E.V., Loseva, N.V., Polkovnikov, A.A. (2020). Realization of Population’s Motivational Potential in the System of Public Administration as a Factor of Institutional Ensuring the Competitiveness of the Region. In: Inshakova, A., Inshakova, E. (eds) Competitive Russia: Foresight Model of Economic and Legal Development in the Digital Age. CRFMELD 2019. Lecture Notes in Networks and Systems, vol 110. Springer, Cham. https://doi.org/10.1007/978-3-030-45913-0_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-45913-0_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45912-3
Online ISBN: 978-3-030-45913-0
eBook Packages: EngineeringEngineering (R0)