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Explaining Hierarchical Multi-linear Models

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Scalable Uncertainty Management (SUM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11940))

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Abstract

We are interested in the explanation of the solution to a hierarchical multi-criteria decision aiding problem. We extend a previous approach in which the explanation amounts to identifying the most influential criteria in a decision. This is based on an influence index which extends the Shapley value on trees. The contribution of this paper is twofold. First, we show that the computation of the influence grows linearly and not exponentially with the depth of the tree for the multi-linear model. Secondly, we are interested in the case where the values of the alternatives are imprecise on the criteria. The influence indices become thus imprecise. An efficient computation approach is proposed for the multi-linear model.

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Author information

Authors and Affiliations

  1. Thales Research & Technology, Palaiseau, France

    Christophe Labreuche

Authors
  1. Christophe Labreuche
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Correspondence to Christophe Labreuche .

Editor information

Editors and Affiliations

  1. Institut Supérieur de Gestion de Tunis, Bouchoucha, Tunisia

    Nahla Ben Amor

  2. University of Technology of Compiègne, Compiègne, France

    Benjamin Quost

  3. University of Luxembourg, Esch-Sur-Alzette, Luxembourg

    Martin Theobald

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Labreuche, C. (2019). Explaining Hierarchical Multi-linear Models. In: Ben Amor, N., Quost, B., Theobald, M. (eds) Scalable Uncertainty Management. SUM 2019. Lecture Notes in Computer Science(), vol 11940. Springer, Cham. https://doi.org/10.1007/978-3-030-35514-2_15

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  • DOI: https://doi.org/10.1007/978-3-030-35514-2_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35513-5

  • Online ISBN: 978-3-030-35514-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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