SMART TECHNOLOGIES IN RISK MANAGEMENT CONTROL AND DECISION-MAKING SYSTEMS IN A FUZZY DATA ENVIRONMENT

Abstract

The aim of the article is to develop a methodology for quantitative evaluation and forecasting of the quality of decision-making in complex multi-parametric organizational and technical systems under the conditions of uncertainty of control agents. The research results proposed in the article are focused on the practical use of formal tools in predicting the reliability of control results and decision-making risks under the uncertainty of model agents. The proposed mathematical and simulation applications implement a multi-agent approach to solving the general problem of assessing the quality of control according to the criteria of "producer risk (project customer)" and "consumer risk". For the purposes of modeling, such sections of mathematics and methods as probability theory and mathematical statistics, regression analysis, simulation and structural-functional modeling, agent approach are used. The quality of mathematical modeling is supported by computer experiments with simultaneous graphical visualization of the results, which increases the effectiveness of the study. A simulation model has been developed to assess and predict the reliability of control and the risks of decision-making under the uncertainty of system agents. The novelty of the proposed model lies in taking into account the statistical nature of normative values ​​and the laws of equal probability. The proposed system methodology implements a dual approach to solving a common problem, assessing the quality of the control process by the magnitude of risks in the decision-making system. In the first case, the problem of quantitative risk assessment is solved for given statistical characteristics of control agents, and in the second case, the required measurement accuracy is determined for given uncertainties and risk levels in the control system. The paper proposes the results of computer modeling in 3D format.