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Issue title: Collective intelligent information and database systems
Guest editors: Ngoc-Thanh Nguyen, Manuel Núñez and Bogdan Trawiński
Article type: Research Article
Authors: Nguyen, Ngoc Thanha; b | Nguyen, Van Dub; c | Hwang, Dosamc; *
Affiliations: [a] Division of Knowledge and System Engineering for ICT, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam | [b] Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Poland | [c] Department of Computer Engineering, Yeungnam University, Korea
Correspondence: [*] Corresponding author. Dosam Hwang, Division of Knowledge and System Engineering for ICT, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City,Vietnam. Tel.: +82 53 810 3515; E-mail: [email protected].
Abstract: Collective knowledge is understood as the common knowledge state of a collective consisting of autonomous units. The knowledge states referred from these autonomous units to some degree reflect the real knowledge state of a subject in the real world, but it is not known to what degree because of incompleteness and uncertainty. Although collective knowledge determination is an important task because these knowledge states can be different from each other, there exists another important issue with its quality. The quality of collective knowledge is based on the difference between the real knowledge state and the collective knowledge. In this study, we investigate the influence of the number of collective members on the quality of collective knowledge. Through experimental analysis, the larger collective we use, the better the quality of collective knowledge will be. In other words, the large number of collective members positively affects the quality of collective knowledge. Besides, some theorems about the relationship between the collective knowledge and the knowledge states in a collective, the influence of adding or removing members on the quality of collective knowledge are also proved.
Keywords: Collective knowledge, consensus choice, inconsistency knowledge
DOI: 10.3233/JIFS-169121
Journal: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1217-1228, 2017
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