Building a fuzzy model for determining the level of social well-being of the population

Abstract

This paper considers objects that affect the social security of the country. The complexity of such objects makes the development of computer systems in sociological research a difficult algorithmic task because of information uncertainty. Human thinking is based on inaccurate, approximate data, the analysis of which makes it possible to formulate clear decisions. In practice, there are usually no precise mathematical models that describe social objects. In such cases, it is advisable to use fuzzy mathematics as a tool for solving this problem. The main advantage of this approach compared to other artificial intelligence methods is the ability to interpret the results obtained. To assess the level of social well-being of the population, we used the mathematical apparatus of fuzzy set theory and fuzzy inference. The study is based on the OECD Better Life Index, which was developed by the Organization for Economic Cooperation and Development (OECD) to help countries assess and improve the quality of life of their citizens. In the course of the study, a fuzzy inference system was built to measure the social well-being of the population based on the indicators of the OECD Better Life Index. Since determining the level of social well-being is a complex task, a hierarchical structure with two main groups of social well-being indicators was constructed to simplify it. The resultant system evaluates each social indicator included in the OECD’s Better Life Index. Using the fuzzy inference model built, it was possible to assess the social well-being of the country’s population in a simple and transparent way in comparison with the OECD member countries. The results of the study make it possible to understand which indicators of social well-being of the country’s population are desirable or need to be improved in the future