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.

Formalization of risk management in the context of digital business transformation

“…In a real situation, it is not always possible to measure the selected physical parameters in existing units of measurement, and then they resort to replacing them with indirect ones, informationally correlated with physical parameters, which are called diagnostic S. In Figure 1, the set {Si} is a set of diagnostic parameters, and f(s) is a function of the distribution density of some conditional diagnostic parameter. As follows from Figure 1, the process of managing the organizational and technical system consists of the following sequence: In most of the known works, this is the end of the control process [10]. At the same time, the main attention was focused on the quantitative assessment of control errors (control risks), which were functions of the statistical properties and characteristics of the above agents of the control system [11]- [16].…”

“…Regardless of the type of control process, the results of monitoring and restoring the normative functionality are of a stochastic nature, due to the uncertainty of the parameters and control conditions. Uncertainty generates errors and control risks [8]- [10]. In these studies, we limited ourselves to the consideration of the control system without taking into account feedback.…”

Research and implementation of the medical text analysis algorithm for predicting mortality

“…Simulation models allow you to take into account many variables, such as age, gender, medical history, lifestyle, and other factors that affect the likelihood of mortality. A graphical model explaining the process of formation of management risks is considered in work [4].…”

Digital twins technology in the educational process of the aviation equipment repair

“…A similar concept, in which software models simulate reality based on information coming from the physical world, was proposed by David Gelernter in 1991 and was called "Mirror Worlds" [5]. These approaches, expressed as a new concept of digital twins, were first proposed by M. Greaves in 2002 at the University of Michigan [6].…”

“…In the second 𝑡 ∑ = 𝑡 𝐷𝑇 + ∆𝑡 𝑅𝑂 -certification time equal to the sum of training time (𝑡 𝐷𝑇 )on digital twins and an additional time interval on real objects (∆𝑡 𝑅𝑂 ) while maintaining the main condition for quality learning 𝑡 𝑅𝑂 = 𝑡 ∑ , i.e. in accordance with the expression as shown in (5):…”