Neuromathematics as an effective tool for forecasting social development of Russian regions - R.V. Gubarev; E.I. Dzyuba (Учёные записки Казанского университета: Серия Физико-математические науки)
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.