Central Economics and Mathematics Institute

About: Central Economics and Mathematics Institute is a facility organization based out in Moscow, Russia. It is known for research contribution in the topics: Population & Foreign-exchange reserves. The organization has 297 authors who have published 580 publications receiving 6449 citations. The organization is also known as: Federal State Institution of Science Central Economics and Mathematics Institute of the Russian Academy of Sciences.

The strategy of russia’s economic development:from reference to referent (On the book by L. P. Evstigneeva and R. N. Evstigneev “The strategy of Russia’s economic development: a theoretical aspect”)

Abstract: (On the book by L. P. Evstigneeva and R. N. Evstigneev “The strategy of Russia’s economic development: A theoretical aspect”) Based on a brief review of the book “The strategy of Russia’s economic development: A theoretical aspect” by L. P. Evstigneeva and R. N. Evstigneev, the author introduces the reader to its basic ideas and shares with him/her his thoughts. The key idea is the economic synergy (or symbol in the language of semiotics), the essence of which (reference) is disclosed in detail in the book; the emphasis is not just on the new paradigm, adequate to humanitarian civilization (referent), but also on the ability to ensure the transition from the reference to the referent.

Corporate governance in connection with performance and digital

Abstract: The article concerned issues, related to the characteristics of Corporate governance and performance. The characteristics of Corporate governance are pervasive: ownership structure, separation from control, Board of directors, protection of rights. For Russian companies traditional organizational characteristics of life cycle stages are not decisive. Currently there is a tendency for owners to get out from management and focus on social, environmental results. The ownership of private insiders is the highest at the stage of formation, and the foreign ownership rises at the stage of growth. Based on the model was estimated significant positive impact of companies’ age on an increase in property rights, ownership and control, protecting minority investors encourages R&D. Results of estimation of 62 countries in 2020 represented significant positive relation Technology factors: adaptive attitudes, business agility, IT integration with performance and Corporate governance. An increase in digital is associated with a higher quality of Corporate governance.

Creation of a Supercomputer Simulation of a Society with Different Types of Active Agents and Its Approbation

Abstract: This article continues a series of works devoted to the creation of large agent-based models, built as an artificial society, and the development of software for their implementation—the MÖBIUS design system for scalable agent-based models. The basic core of the system is a demographic model that simulates the natural movement of the population. A new stage in the development of the work discussed in this article was the creation on the basis of this core of an agent-based model of Russia, which includes families as agents of a new type, hierarchically connected with human agents. In addition, objects of a new type were introduced into the model—projects that provide for the creation in an artificial environment of analogues of complex control actions aimed at stimulating fertility. Developed on the basis of simulating the reaction of individual families to the introduced regional support measures, the model makes it possible to track their impact on key demographic indicators. The agent-based model of Russia was tested on data for a long retrospective period using the example of the launch of maternal capital programs and showed good agreement with official statistics.

A spatial autocorrelation for modelling the spread of coronavirus infections

Abstract: Spatial autocorrelation methods are used to study spatial disproportions in the socio-economic development of territories. The most common research methods are the analysis of Moran local indices, Moran global index, Getis-Ord hot spots. In this study, we used spatial autocorrelation methods to estimate COVID-19 distribution patterns. As a result of the study, we identified the formed growth poles, the epicenters of the spread of infection (St. Petersburg, Sverdlovsk and Nizhny Novgorod regions) and only emerging ones. The practical application of this methodological approach allowed us to predict further spatial directions of the spread of coronavirus infection (Vladimir, Kaluga, Smolensk, Tula, Tver, Yaroslavl, Ryazan and Leningrad regions).