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Discrete Fuzzy Measures pp 55–73Cite as

Value and Interaction Indices

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 382))

Abstract

Fuzzy measures map each subset of a given set to a weight or importance, which allows for the modelling of complementary or redundant relationships between variables. The greater flexibility afforded, however gives rise to the problem of interpretation. Fortunately, this problem has received a great deal of attention in the context of aggregation and multicriteria decision making. This chapter presents the various indices that have been proposed for interpreting fuzzy measures, some of which lead to alternative representations that can be used to model requirements in various contexts.

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Notes

  1. 1.

    Such interactions are well known in game theory. For example, contributions of the efforts of workers in a group can be greater or smaller than the sum of their separate contributions (if working independently).

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Authors and Affiliations

  1. Deakin University, Burwood, VIC, Australia

    Gleb Beliakov & Simon James

  2. Ningbo University, Ningbo, Zhejiang, China

    Jian-Zhang Wu

Authors
  1. Gleb Beliakov
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  2. Simon James
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  3. Jian-Zhang Wu
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Correspondence to Gleb Beliakov .

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Beliakov, G., James, S., Wu, JZ. (2020). Value and Interaction Indices. In: Discrete Fuzzy Measures. Studies in Fuzziness and Soft Computing, vol 382. Springer, Cham. https://doi.org/10.1007/978-3-030-15305-2_3

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