Many scientists have investigated noise annoyance caused by combined sources. However, general annoyance reported in a social survey still has many unknown features. In this work the cognitive process involved in coming to a general noise rating based on a known, in context, rating of annoyance by particular sources is studied. A comparison of classical and fuzzy models is used for this. The new fuzzy linguistic models give a meaning to the successful strongest component or dominant source model that was used in previous work. They also explain to some extent particular features not included in that previous model. The variance not predicted by the fuzzy linguistic model is contrasted with personal data of the test subjects (age, gender, and education level) and the context of the question in the questionnaire. Only age seems to play a significant role.

Predicting the effect of noise on individual people and small groups is an extremely difficult task due to the influence of a multitude of factors that vary from person to person and from context to context. Moreover, noise annoyance is inherently a vague concept. That is why, in this paper, it is argued that noise annoyance models should identify a fuzzy set of possible effects rather than seek a very accurate crisp prediction. Fuzzy rule based models seem ideal candidates for this task. This paper provides the theoretical background for building these models. Existing empirical knowledge is used to extract a few typical rules that allow making the model more specific for small groups of individuals. The resulting model is tested on two large-scale social surveys augmented with exposure simulations. The testing demonstrates how this new way of thinking about noise effect modeling can be used in practice both in management support as a "noise annoyance adviser" and in social science for testing hypotheses such as the effect of noise sensitivity or the degree of urbanization.

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-Many governments and international organizations recognize the fact that classical economic indicators do not accurately reflect the quality of life in a country or region. Indicators that reflect the subjective evaluation of well-being or quality of life by inhabitants have to be constructed based on multiple criteria. Multi-criteria evaluation systems based on fuzzy integrals seem very well suited for this task as they tend to approximate overall quality assessment by inhabitants quite well. This paper discusses the choice of fuzzy integrals and analyses the suitability of the approach based on a survey with 2000 people.

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