Book ChapterDOI
Fuzzy Sets and Fuzzy Logic in the Human Sciences
Michael Smithson
- Vol. 341, pp 175-186
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This chapter surveys the history of fuzzy set applications in the human sciences, and then elaborates the possible reasons why fuzzy set concepts have been relatively under-utilized therein.Abstract:
The development of fuzzy set theory and fuzzy logic provided an opportunity for the human sciences to incorporate a mathematical framework with attractive properties. The potential applications include using fuzzy set theory as a descriptive model of how people treat categorical concepts, employing it as a prescriptive framework for “rational” treatment of such concepts, and as a basis for analysing graded membership response data from experiments and surveys. However, half a century later this opportunity still has not been fully grasped. This chapter surveys the history of fuzzy set applications in the human sciences, and then elaborates the possible reasons why fuzzy set concepts have been relatively under-utilized therein.read more
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Posted Content
Fuzzy-Set Social Science
TL;DR: F fuzzy sets allow a far richer dialogue between ideas and evidence in social research than previously possible, and can be carefully tailored to fit evolving theoretical concepts, sharpening quantitative tools with in-depth knowledge gained through qualitative, case-oriented inquiry.
Journal ArticleDOI
Fuzzy rating scales: Does internal consistency of a measurement scale benefit from coping with imprecision and individual differences in psychological rating?
TL;DR: Aiming to show the advantages of using fuzzy rating scales in the setting of questionnaires, the extended Cronbach α is considered to quantify the internal consistency associated with constructs involving fuzzy rating scale-based items.
Journal ArticleDOI
Transforming Family Resemblance Concepts into Fuzzy Sets
TL;DR: This article aims to clarify fundamental aspects of the process of assigning fuzzy scores to conditions based on family resemblance structures by considering prototype and set theories.
Dissertation
An investigation into fuzzy clustering quality and speed : fuzzy C-means with effective seeding
TL;DR: This chapter discusses clustering algorithms based on co-occurance of categorical data, Hierarchical algorithms, grid-based algorithms, and more.
Dissertation
Perceptually-driven computer graphics and visualization
TL;DR: A bottom-up approach incorporating both the spatial and temporal components of the low-level human visual system processes is suggested to develop a general-purpose quality metric designed to measure the local distortion visibility on dynamic triangle meshes.
References
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Journal ArticleDOI
Advances in prospect theory: cumulative representation of uncertainty
Amos Tversky,Daniel Kahneman +1 more
TL;DR: Cumulative prospect theory as discussed by the authors applies to uncertain as well as to risky prospects with any number of outcomes, and it allows different weighting functions for gains and for losses, and two principles, diminishing sensitivity and loss aversion, are invoked to explain the characteristic curvature of the value function and the weighting function.
Book
The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies
TL;DR: In this article, a Boolean approach to Qualitative Comparison: Basic concepts 7. Extensions of Boolean Methods of Qualitatve Comparison 8. Applications of Boolean methods of qualitative comparison 9. Dialogue of Ideas and Evidence in Social Research Bibliography Index 10.
Journal ArticleDOI
A Rasch Model for Partial Credit Scoring.
TL;DR: In this paper, an unidimensional latent trait model for responses scored in two or more ordered categories is developed, which can be viewed as an extension of Andrich's Rating Scale model to situations in which ordered response alternatives are free to vary in number and structure from item to item.
Journal ArticleDOI
Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment.
Amos Tversky,Daniel Kahneman +1 more
TL;DR: The conjunction rule as mentioned in this paper states that the probability of a conjunction cannot exceed the probabilities of its constituents, P (A) and P (B), because the extension (or the possibility set) of the conjunction is included in the extension of their constituents.