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Collective Knowledge: An Enhanced Analysis of the Impact of Collective Cardinality

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Intelligent Information and Database Systems (ACIIDS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10191))

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Abstract

In this paper, we present an enhanced analysis of the impact of collective cardinality on the quality of collective knowledge. Collective knowledge is a knowledge state determined on the basis of collective members’ knowledge states on the same subject (matter) in the real world. The collective members (which are often autonomous units) have their own knowledge bases, thus their knowledge states can be different from each other. The quality is based on the difference between the collective knowledge and the real state of the subject. For this aim, we introduce a new factor named diam presenting the maximal difference between a knowledge state in the collective and the real state. The simulation experiments reveal that the quality is not only dependent on the collective cardinality but also dependent on the diam value. Additionally, the number of collective members needed to achieve a difference level between the collective knowledge and the real state is also investigated.

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Authors and Affiliations

  1. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Van Du Nguyen

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  1. Van Du Nguyen
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Correspondence to Van Du Nguyen .

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Editors and Affiliations

  1. Wrocław University of Science and Technology , Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Japan Advanced Institute of Science and Technology , Nomi, Japan

    Satoshi Tojo

  3. Japan Advanced Institute of Science and Technology , Nomi, Japan

    Le Minh Nguyen

  4. Wrocław University of Science and Technology , Wrocław, Poland

    Bogdan Trawiński

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Nguyen, V.D. (2017). Collective Knowledge: An Enhanced Analysis of the Impact of Collective Cardinality. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-54472-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54471-7

  • Online ISBN: 978-3-319-54472-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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