Mathematical Models for Evaluating the Effectiveness of State Support for the Dairy Industry
- 10.2991/aebmr.k.200509.002How to use a DOI?
- paradoxical theory of regulation, digital model, dairy industry
The digitalization of agro-industrial complex in Russia is at an extremely low level due to the insufficient level of their state support. The aim of this research is to offer an original concept for the effective regulation of the agricultural sector. The methodological basis of the research is a systematic and comparative analysis, cause-and-effect analysis, observation, comparison and grouping, as well as the methodology of the inno-diversification approach. The research was based on information taken from the database of the National Union of Milk Producers. A theory of paradoxical regulation is proposed, an action mechanism is developed that allows, using actual data taken from open sources, to develop digital models for regulating the parameters of the dairy industry in the Siberian Federal District and the Novosibirsk Region. The developed mechanism was taken as the basis for the design of the digital technology algorithm, based on formulas in the form of mathematical software, obtained by the method of the inno-diversification approach. Digital technologies developed on the basis of digital models made it possible to predict with a reasonable degree of certainty the probable values of industry parameters depending on the regulatory impact.
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Mikhail K. Chernyakov AU - Maria M. Chernyakova AU - Irina A. Chernyakova AU - Saidmukhtor S. Mokhtarzada PY - 2020 DA - 2020/05/12 TI - Mathematical Models for Evaluating the Effectiveness of State Support for the Dairy Industry BT - Proceedings of the International Conference on Economics, Management and Technologies 2020 (ICEMT 2020) PB - Atlantis Press SP - 5 EP - 10 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.200509.002 DO - 10.2991/aebmr.k.200509.002 ID - Chernyakov2020 ER -