METHODS FOR COGNITIVE MODELING OF AN EFFECTIVE LAND USE MANAGEMENT SYSTEM

DOI 10.32651/253-103

Issue № 3, 2025, article № 14, pages 103-113

Section: Problems of agroeconomic researches

Language: Russian

Original language title: МЕТОДЫ МОДЕЛИРОВАНИЯ ЭФФЕКТИВНОЙ СИСТЕМЫ УПРАВЛЕНИЯ ЗЕМЕЛЬНЫМИ РЕСУРСАМИ В СЕЛЬСКОМ ХОЗЯЙСТВЕ С ПРИМЕНЕНИЕМ ИНСТРУМЕНТОВ АЛГЕБРЫ НЕЧЁТКИХ МНОЖЕСТВ

Keywords: LAND RESOURCES, EFFECTIVE MANAGEMENT, METHOD, FUZZY COGNITIVE MODELING, MATRIX, CONCEPT

Abstract: In the modeled effective land management system, the method of fuzzy-cognitive modeling developed earlier by the authors was applied and significantly developed, different from the most common methods of pair comparisons V.D. Silova and T. Saati topics, that the negative-positive pairs of concepts recommended by them are used only as a special case in the model for correcting the economic behavior of land market entities, where negative and positive actions, events, etc. really occur. But even in this case, not transitively closed matrices are used, but a simple operation of the algebra of fuzzy sets "complement." This allows us to take advantage of a number of operations on fuzzy sets in order to obtain quite interesting states (stages) and parameters of the designed models, which make it possible to significantly enrich the tools for analyzing simulated processes, expand the range and concretize possible management decisions during and during the completion of modeling. This methodological technique made it possible to significantly simplify the formation of fuzzy-cognitive models (NKM), which made it possible to develop, along with the main basic model, several frequent ones that reveal certain aspects of building an effective land management system in agriculture. The options (methods) developed and applied during the study for gradually complicating the requirements for expert (primary) information for constructing the NKM can be used in other similar studies. The methodological technique of phased complication (from simple to complex) can be useful in the selection and assessment of the competence of invited experts and subject researchers: a simple comparison of two concepts; two options for setting the initial information ("the share of the i-th concept in the pair interaction" and "the strength of the inter-factor interaction"); the same with the addition of an event probability indicator; a full-fledged fuzzy-cognitive matrix. The computational and analytical mechanism used in the research process can be used for machine learning in order to develop artificial intelligence (AI).

Authors: Poluliakh IUrii Georgievich, Adadimova Liubov IUrevna