Titlebook: Algorithmic Decision Making with Python Resources; From Multicriteria P Raymond Bisdorff Textbook 2022 The Editor(s) (if applicable) and Th

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Algorithmic Decision Making with Python Resources978-3-030-90928-4Series ISSN 0884-8289 Series E-ISSN 2214-7934

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https://doi.org/10.1007/978-3-7091-4009-3iteria performance tableau, we construct the corresponding bipolar-valued outranking relation from pairwise comparisons. The resulting bipolar-valued outranking characteristics may be recoded. Finally, the codual outranking digraph gives us the associated strict outranking relation.

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https://doi.org/10.1007/978-3-7091-4009-3ia, into . quantile equivalence classes. The sorting algorithm is based on pairwise outranking characteristics involving the quantile class limits observed on each criterion. Thus we may implement a weak ordering algorithm of complexity .(.).

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Denial of Service: A Perspectivee quantiles learned from historical performance data gathered from similar decision alternatives observed in the past. We show how to learn performance quantiles from such historical performance tableaux. New performance records may now be rated with respect to these quantile norms.

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