REVIEW OF FUZZY MODELS AND FUZZY METHODS FOR ANALYSIS OF INFORMATION IN CONDITIONS OF EMOTIONAL DECISION MAKING

In the modern world, decision-making often takes place in an environment of uncertainty and under the significant influence of emotional factors, which requires the use of special methods for analyzing information. This study is devoted to an overview of fuzzy models and methods that allow such factors to be taken into account when making decisions. In particular, the approaches based on fuzzy logic, fuzzy cognitive maps and fuzzy clustering methods that provide flexibility and adaptability in conditions of uncertainty are considered. The study analyzes examples of the application of these methods in various fields, including risk management, medical diagnostics and financial forecasting, which demonstrates their versatility and practical value. The results of the review show that fuzzy models and methods can significantly improve the quality of decisions made by taking more adequate account of emotional factors and uncertainty. In addition, the study highlights the importance of developing hybrid models that integrate the capabilities of fuzzy logic and neural networks to achieve higher accuracy and reliability. Special attention is paid to the prospects of in-depth study of the influence of emotional states on the decision-making process, which opens up new horizons for improving models and methods of analysis. Thus, the presented review highlights the importance of fuzzy methods in the context of complex and emotionally charged decision-making tasks, offering directions for future research and practical applications. In conclusion, the need for further improvement of these methods is noted to ensure their widespread implementation in various fields of activity, where decisions are made in conditions of uncertainty and emotional impact.