Use of fuzzy logic for analyzing the socio-economic well-being of the population
DOI:
https://doi.org/10.34185/1562-9945-1-150-2024-06Keywords:
fuzzy logic, population welfare, household income, data fuzzification, linguistic variable, criterion matrix.Abstract
The article reveals a new approach to the analysis of population welfare using the theory of fuzzy sets. The purpose of the study is to develop and apply a methodology based on fuzzy logic to quantify and analyze the social and economic well-being of citizens. The main task includes the development of a methodological approach, the construction of a model for assessing well-being and the development of recommendations for planning socio-economic strategies. As a solution, the use of fuzzy logic is proposed, which allows better consideration of ambiguities and uncertainties in the data on the well-being of the population. The study presents a detailed description of the developed model, which includes the formation of lin-guistic variables, the definition of term sets, and the development of inference rules. These components allow the model to adapt to different conditions and types of data, providing flexibility and a wide field of application of the method. The study also emphasizes the impor-tance of analyzing factors that affect the well-being of the population, such as household in-come, expenditure on medicine and food, education level, housing conditions, the importance of personal economy and others. The authors indicate the dynamic influence of these factors and the need for their regular updating to increase the accuracy of forecasts. The results of the study demonstrate that the developed model provides more accurate and reasonable fore-casts of the level of socio-economic well-being compared to traditional methods. The ob-tained data can be used to plan socio-economic strategies at the micro and macro levels, con-tributing to the development of effective measures to improve the well-being of the population. The article makes a significant contribution to the development of socio-economic welfare analysis methods, showing the potential of fuzzy logic as a tool for increasing the accuracy of forecasting and developing sound socio-economic policies.
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