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International Conference on Computational Science

ICCS 2021: Computational Science – ICCS 2021 pp 514–525Cite as

Combining Heterogeneous Indicators by Adopting Adaptive MCDA: Dealing with Uncertainty

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12747))

Abstract

Adaptive MCDA systematically supports the dynamic combination of heterogeneous indicators to assess overall performance. The method is completely generic and is currently adopted to undertake a number of studies in the area of sustainability. The intrinsic heterogeneity characterizing this kind of analysis leads to a number of biases, which need to be properly considered and understood to correctly interpret computational results in context. While on one side the method provides a comprehensive data-driven analysis framework, on the other side it introduces a number of uncertainties that are object of discussion in this paper. Uncertainty is approached holistically, meaning we address all uncertainty aspects introduced by the computational method to deal with the different biases. As extensively discussed in the paper, by identifying the uncertainty associated with the different phases of the process and by providing metrics to measure it, the interpretation of results can be considered more consistent, transparent and, therefore, reliable.

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Author information

Authors and Affiliations

  1. School of Information, Systems and Modelling (ISM), University of Technology Sydney, Sydney, Australia

    Salvatore F. Pileggi

  2. Centre on Persuasive Systems for Wise Adaptive Living (PERSWADE), University of Technology Sydney, Sydney, Australia

    Salvatore F. Pileggi

Authors
  1. Salvatore F. Pileggi
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Correspondence to Salvatore F. Pileggi .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Pileggi, S.F. (2021). Combining Heterogeneous Indicators by Adopting Adaptive MCDA: Dealing with Uncertainty. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12747. Springer, Cham. https://doi.org/10.1007/978-3-030-77980-1_39

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  • DOI: https://doi.org/10.1007/978-3-030-77980-1_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77979-5

  • Online ISBN: 978-3-030-77980-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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