A FUZZY MODEL FOR ASSESSING THE COMPONENT OF SOCIAL SECURITY - THE WELL-BEING OF THE POPULATION - SHARKADI, MARIANNA; DOROVTSI, ADAM (Herald of Khmelnytskyi National University. Technical sciences, 2024)
Recently, the issue of social security has attracted the attention of many researchers. Social processes are often characterized by a high degree of uncertainty, incomplete data and subjective assessments, which makes it difficult to accurately measure the level of well-being using classical approaches. In view of this, the use of fuzzy sets is an effective approach for modeling socio-economic indicators, where exact boundaries between different levels of well-being cannot be determined. This study examines one of the main components of social security - the well-being of the population. Similar to social security, the issue of population well-being is also the subject of a number of scientific studies. This paper examines the determination of the level of well-being of the population of Ukraine in comparison with the member countries of the Organisation for Economic Co-operation and Development. This study builds a fuzzy mathematical model that takes into account three aspects of human well-being: economic well-being, social well-being and environmental well-being. In the Matlab software, the stages of implication and aggregation are performed automatically, after selecting the appropriate methods. For the defuzzification stage, the center of gravity method is chosen.The Fuzzy Logic Designer application of the Matlab software package was used in the study. After constructing the membership functions, a knowledge base is built for each fuzzy inference system. In general, the built system consists of 307 logical rules of the "If-then" type. Matlab software implements two fuzzy logic inference algorithms - the Mamdani-type algorithm and the Sugeno-type algorithm. In the course of the study, Mamdani-type algorithm was used. In order to present the results of the study in a more understandable and transparent way, a FIS-tree was built, in which the aspects of well-being are presented separately. In total, 15 fuzzy inference systems were built in the course of the study.