Smart technologies of the risk-management and decision-making systems in a fuzzy data environment

Abstract

The purpose of this article is to provide a methodology for calculating and predicting the quality of solution implementation in complicated multi-parametric organizational and technological challenges with control agent uncertainty. The article's study findings are centered on the practical application of formal methods in predicting the outcomes of control and decision-making risks under the uncertainty of model agents. The proposed mathematics and simulation applications use a multi-agent strategy to handle the general problem of assessing quality control based on "producer risk (project customer)" and "user risk." Computer experiments with simultaneous graphical visualization of the results improve the accuracy of mathematical modeling, increasing the study's effectiveness. Under the uncertainty of system agents, a simulation model has been designed to analyze and anticipate the dependability of control and the hazards of decision-making. The suggested model is unique in that it takes into account the statistical nature of normative values as well as the rules of equal probability. To handle a frequent problem, the proposed system technique employs a dual approach. It accomplishes this by assessing the quality of the control process based on the magnitude of the risks in the decision-making system.