Neuromathematics as an Effective Tool for Forecasting Social Development of Russian Regions

R. Gubarev, E. Dzyuba
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Abstract

In the context of the national economic turbulence, it becomes important to forecast the social development of constituent entities of the Russian Federation. In order to provide highly accurate forecasting, neural network technologies are used in the research (a Bayesian assembly of the dynamic neural network of various configurations is formed). As a result of the forecasting, it is found, that the leading Russian regions should have a lower social development index in 2016–2017 as compared to 2014–2015. A slowdown of social development is also predicted for the leading regions of the Volga Federal District in 2016–2017, but only as compared to 2015. The obtained data show that the social development index in the Republic of Bashkortostan changes a little. Nevertheless, a significant lagging of Bashkortostan behind the leading regions of the Russian Federation and the Volga Federal District in the social sphere is predicted: Bashkortostan is a competitive region in terms of the living standards, but not in the sphere of scientific research and innovations. For this reason, measures encouraging innovative development of Russian regions as exemplified by the Republic of Bashkortostan are introduced and discussed in the paper.
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神经数学作为预测俄罗斯地区社会发展的有效工具
在国家经济动荡的背景下,预测俄罗斯联邦组成实体的社会发展变得非常重要。为了提供高度精确的预测,研究中使用了神经网络技术(形成了各种配置的动态神经网络的贝叶斯组合)。预测结果发现,2016-2017年俄罗斯领先地区的社会发展指数应低于2014-2015年。预计2016-2017年伏尔加河联邦区主要地区的社会发展也将放缓,但仅与2015年相比。所得数据表明,巴什科尔托斯坦共和国的社会发展指数变化不大。然而,预计巴什科尔托斯坦在社会领域将明显落后于俄罗斯联邦和伏尔加联邦区的主要地区:巴什科尔托斯坦在生活水平方面是一个有竞争力的地区,但在科学研究和创新领域却不是。为此,本文以巴什科尔托斯坦共和国为例,介绍并讨论了鼓励俄罗斯地区创新发展的措施。
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CiteScore
0.60
自引率
0.00%
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0
审稿时长
17 weeks
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