Authors:
Luisiena Puinko
and
Elena Tolkacheva
Affiliation:
Far-East Institute of management, branch of the Russian Presidential Academy of National Economy and Public Administration (hereinafter RANEPA), Khabarovsk, Russia, Russian Federation
Keyword(s):
Neural Networks, Socio-Economic Processes, Public Administration.
Abstract:
The digitalization of the economy in Russia is subject to management influence from Federal, regional, and
municipal Executive authorities. At the same time, there is a continuous search for methods and tools to
improve its effectiveness. This process is complicated, among other things, by the fact that trends at all levels
of economic digitalization management and mechanisms for its implementation in Russia currently remain
insufficiently studied. The use of classical statistical and econometric methods for assessing and predicting
socio-economic processes, both in Russia as a whole, and in individual regions and municipalities on its
territory, has proven itself well; and at the same time, they are very time-consuming, and the result obtained
through the use of statistical or econometric methods of analysis is obtained with a certain time delay; and at
the time of its receipt, it does not correspond to the stated goals of the study. Then econometric analysis and
statistica
l methods should be replaced by a tool that will allow you to get results faster with no less quality,
and use it in a timely manner when implementing and correcting management tasks. One of the directions of
development of new tools for analyzing many processes of dynamics, stochastic processes, and systems with
big data is artificial intelligence, or information systems based on it. In the conditions of incomplete
information about the socio-economic processes of any region, statistical methods of assessment can even
give an erroneous result, which can provoke fatal management errors. To minimize forecasting estimates and
optimize analysis and evaluation procedures, it is necessary to rely on modern, new and effective methods for
analyzing stochastic processes.
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