Use of fuzzy logic for analyzing the socio-economic well-being of the population


  • Dorovtsi Adam



fuzzy logic, population welfare, household income, data fuzzification, linguistic variable, criterion matrix.


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.


Boguslavska, S.I. (2021). Transformatsiia rehionalnoho menedzhmentu v umovakh hlob-alizatsii dlia zabezpechennia staloho rozvytku rehioniv Ukrainy [Transformation of regional management in the conditions of globalization for maintenance of sustainable development of regions of Ukraine]. Business Inform,

, 90-97.

Kapinos, H., Larionova, K. (2023). Problemy upravlinnya stalym rozvytkom ukrayiny v umovakh viyny [Problems of managing the sustainable development of Ukraine in conditions of war]. Modeling the development of the economic systems, 1, 93-103. DOI:

Makedon, V.V., Makovetsʹka, A.O. (2023). Informatsiyne zabezpechennya ekonomichnoyi bezpeky pidpryyemstv v umovakh rynkovoyi nestabilʹnosti. [Information provision of eco-nomic security of enterprises in conditions of market instability]. Mizhnarodnyy naukovyy zhurnal "Internauka". Seriya: "Ekonomichni nauky", 12. DOI:

Olshanska, O.V., Khmelevskyi, M.O. (2021). Rehionalno-systemna orhanizatsiia eko-nomiky yak chynnyk realizatsii tsilei staloho rozvytku [Regional and systemic organization of the economy as a factor in realizing the goals of sustainable development]. Imperatyvy eko-nomichnoho zrostannia v konteksti realizatsii hlobalnykh tsilei staloho rozvytku, 17–21.

Ashok, S., Krishna, S., Ponnambalam, S. (2023). Fuzzy Logic-Based Multi-Objective Decision-Making Model for Design Evaluation in an Open Innovation Environment. Innovation Analytics. DOI: 10.1142/9781786349989_0011.

Doroudi, H., Goudarzi, F., Kandel, B.K. (2022). The effect of the competitiveness index on economic growth, with an emphasis on institutional and structural variables: 10 selected countries exporting oil. Philosophy, Economics and Law Review, 2, 2, 85-95. DOI 10.31733/2786-491X-2022-2-85-95.

Hernández-Hernández, M., Alfonso Bonilla Cruz, L., Cobián-Romero, L. (2023). Improvement of Validated Manufacturing Processes with Fuzzy Logic. IntechOpen. DOI: 10.5772/intechopen.113302.

Makedon, V., Hetman, O., Yemchuk, L., Paranytsia, N., Petrovska, S. (2019). Human resource management for secure and sustainable development. Journal of security and sustainability issues, 8(3), 345-354.

Mendez, G.M., Lopez-Juarez, I., Montes-Dorantes, P.N. and Garcia, M.A. (2023). A New Method for the Design of Interval Type-3 Fuzzy Logic Systems With Uncertain Type-2 Non-Singleton Inputs (IT3 NSFLS-2): A Case Study in a Hot Strip Mill. IEEE Xplore, 11, 44065-44081. DOI: 10.1109/ACCESS.2023.3272531.

Peckol, J.K. (2021). Introduction to Fuzzy Logic. Nashville, TN, Estados Unidos de América: John Wiley & Sons.

Sabounchi, M., Wei-Kocsis, J. (2022). FLTRL: A Fuzzy-Logic Transfer Learning Powered Reinforcement Learning Method for Intelligent Online Control in Power Systems. Explainable AI and Other Applications of Fuzzy Techniques. Proceedings of the 2021 Annual Conference of the North American Fuzzy Information Processing Society, NAFIPS 2021, Springer, Cham. DOI:

Servin, C., Becker, B., Eaton, E., Kumar, A. (2023). Fuzzy Logic++: Towards Developing Fuzzy Education Curricula Using ACM/IEEE/AAAI CS2023. Fuzzy Information Processing 2023 Lecture Notes in Networks and Systems. 184-193. DOI: 10.1007/978-3-031-46778-3_17.

Shang, К., Hossen, Z. (2013). Applying Fuzzy Logic to Risk Assessment and Decision-Making. Casualty Actuarial Society, Canadian Institute of Actuaries, Society of Actuaries.

Sivanandam, S.N. Sumathi, S.D.S. (2006). Introduction to Fuzzy Logic using MATLAB. Springer-Verlag Berlin Heidelberg.