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Fuzzy models’ use in sociological researches

Fuzzy models’ use in sociological researches

Abstract

У сучасному світі ми все частіше стикаємося з невизначеністю, як у реальному житті, так і в науці. Ці невизначеності також зустрічаються і в соціологічних дослідженнях. У статті розглянемо, як нечіткі моделі можуть бути застосовані у соціологічних дослідженнях. Запропоновано метод застосування нечіктої логіки на прикладі дослідження добробуту населення. Оскільки добробут населення є багатогранним і складним явищем, ми спочатку побудуємо ієрархічну систему, щоб зробити його більш зрозумілим і легшим для моделювання. Таким чином, добробут населення можна поділити на три основні категорії: економічний, екологічний і соціальний. У даній роботі приділимо більше уваги дослідженню економічного добробуту, побудувавши його математичну модель за допомогою нечітких множин.

In the modern world, we are increasingly faced with uncertainty, both in real life and in science. These uncertainties also occur in sociological researches.The article considers how fuzzy models can be applied in sociological research. We propose a method of applying fuzzy logic as an example of a study of the well-being of the population. Since human well-being is a multifaceted and complex subject, we first build a hierarchical system to make it more understandable and easier to model. Thus, human well-being can be divided into three main categories: economic, environmental and social. In this paper, we will pay more attention to the study of economic well-being by building its mathematical model using fuzzy sets.

Keywords

fuzzy sets, modelling, economic well-being, економічний добробут, нечіткі множини, соціологічні дослідження, добробут населення, population well-being, моделювання, sociological research

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold