The purpose of this paper is to present briefly the conceptual framework of fairness in the consensus reaching process with novel elements of grasping imprecision in intentions, preferences and adjustments of individuals. All solutions are based on the idea of "soft" degree of consensus under fuzzy preference relations and a fuzzy majority given as a fuzzy linguistic quantifier. Here, we propose a further extension of the human-consistent consensus reaching support system for group decision-making problems. We enhance a new knowledge-based system with socio-psychological aspects of fair behavior which ensures the satisfaction of justified expectations of its participants. We apply the resource allocation model to express fair influence on consensus reaching process among all decision makers. In this paper we present general conditions for fair solution concept to increase the effectiveness of consensus reaching process and a quality of final decision which becomes highly-justified.
Phillips & Silverstein's ambitious link between receptor abnormalities and the symptoms of schizophrenia involves a certain amount of fuzziness; No detailed mechanism is suggested through which the proposed abnormality would lead to psychological traits. We propose that detailed simulation of brain regions, using model neural networks, can aid in understanding the relation between biological abnormality and psychological dysfunction in schizophrenia.
While studies of the role of fuzzy logic in natural language certainly exist, it is not clear that the use of fuzzy logic to represent linguistic constructs is anything more than an engineering convenience. This paper suggests that one reason this situation obtains is because fuzzy logic has been used strictly to elucidate static aspects of natural language (particularly aspects of the lexicon). If one examines dynamic features of natural language, on the other hand, new possibilities for connections between fuzzy logic and natural language emerge. In particular, some results from category theory are used to show that fuzzy logic can have a role in explaining certain otherwise rather obscure properties of linguistic comparatives in English.
We compare several possible approaches to the measurement of similarity and differences in visual information management systems. For many visual information retrieval applications, it is important to order the data according to a scale as close as possible to the human judgement. Traditional techniques based on the measurement of Euclidean distance between points in feature space prove often unsatisfactory. Data about the measurement of similarity can be derived from the psychological literature. F. Attneave (1950) performed a series of experiments to measure how the perception of similarity changed when a number of features of the stimulus were changed continuously. His findings definitely rejected the hypothesis that the human perceptual system may compute a Euclidean distance between the two stimuli it compares. He found a strong nonlinearity that suggested that the same variation in the features was weighted more when the two stimuli were very similar than when the two stimuli were wide apart. Also, he supported experimentally an hypothesis due to Householder that distances along independent perceptual axes were summed, as in the "city block" distance.
The implementation of the fuzziness index in measuring response style in psychosocial questions was examined, The fuzziness index revealed significant individual differences in response styles in psychological questionnaires, Significant correlations between the fuzziness in psychosocial behaviors and intellectual abilities were presented. The study highlighted the future possibility in modeling the degree and location of fuzziness in order to better understand enormously diverse human characteristics and psychological systems.
The fuzziness index is described as one of the most efficient and useful experimental tools to examine human decision-making in the area of statistical judgement. This study investigated how statistical significance levels were treated and interpreted by various researchers by using the fuzziness index to measure the degree of fuzziness in hypothesis-testing. The fuzziness index showed a significant association between the magnitude of the fuzziness and the critical point of the human statistical decisions. The research demonstrated the possibility of measuring the human imprecision of the decision boundary, and the effectiveness of the fuzziness index in assessing underlying psychological mechanisms in decision-making.
By analyzing related issues in psychology and linguistics, two basic types of fuzziness can be attributed to similarity and relativity, respectively. In both cases, it is possible to interpret grade of membership as the proportion of positive evidence, so as to treat fuzziness and randomness uniformly.
In recent years a range of new methods have been proposed with which to describe and evaluate driver behaviour. One such method is that of fuzzy logic, where variables used in the driver decision-making process may be described linguistically, allowing a quantifiable degree of uncertainty to be introduced. This paper explores the use of such a formalism to describe the driver perception of 'closing speed' between two vehicles engaged in 'car-following' on a motorway, and by using data from an instrumented vehicle experiment, it tests a number of models using relative speed, visual angle and the time to collision. Several of these models fit the data quite well, and there is both a small positive perception bias present and a number of reversals in sign judgement. Additionally, a brief examination is made of potential variations on the methodology that may both make data collection easier and/or allow a 'more fuzzy' representation to be made.