COGNITIVE MODELS OF INSTITUTIONAL REGULATION OF AGRICULTURAL LAND RELATIONS

DOI 10.32651/243-107

Issue № 3, 2024, article № 14, pages 107-116

Section: Problems of agroeconomic researches

Language: Russian

Original language title: КОГНИТИВНЫЕ МОДЕЛИ ИНСТИТУЦИОНАЛЬНОГО РЕГУЛИРОВАНИЯ ЗЕМЕЛЬНЫХ ОТНОШЕНИЙ АГРАРНОЙ СФЕРЫ

Keywords: LAND RELATIONS OF AGRARIAN SPHERE, INSTITUTIONAL ENVIRONMENT, ECONOMIC AGENTS, INSTITUTIONS-DRIVERS, GRAPHIC MAPPING, MACHINE LEARNING

Abstract: Theoretically, the need for institutional regulation of land relations in the agrarian sphere is justified in order to ensure expanded reproduction of land rent using fuzzy cognitive modeling that unites the institutions of the basic block in the form of important legal relations, norms and rules that affect the behavior of economic agents that are influenced by the economic environment and use driver institutions to obtain maximum economic result, while simultaneously reproducing land rent. To this end, a corresponding fuzzy cognitive matrix has been developed, and the necessary mechanism has been created, which differs from most of the current ones in that instead of the widespread Saati pair comparison method, operations of fuzzy set algebra are used, such as complement, intersection, union, symmetric difference and others. This allows you to obtain quite a few hidden states of the land relations system, expanding the toolkit, increasing the effectiveness of modeling. Paired comparisons are carried out directly in the main matrix with two-element concept cells - i-th (factor) and j-th (system). This representation of the original information allows you to directly, i.e. without creating transitive matrices, determine the dominant concepts, calculate their relative and absolute superiority over opposite concepts in the corresponding pairs Using the "union" operation, all maximum values of concepts are selected, forming a certain potential of the system, the "intersection" operation reveals its lower limits, highlighting all minimum values of concepts, which are simultaneously values of direct interference between concepts (the content of one concept in another). Using the "symmetric difference" operation, the resulting (residual) influences are determined: i-th concepts on the system (on j-th concepts) and systems (j-th concepts on its behalf) on i-th concepts. Based on them, regulatory matrices and their graphic displays are formed, which allows for machine learning.

Authors: Poluliakh IUrii Georgievich, Adadimova Liubov IUrevna, Bryzgalin Timur Valerevich, Belokon Mariia Viktorovna