Rating by Sorting into Relative Performance Quantiles

Raymond Bisdorff

Chapter Chapter 9 in Algorithmic Decision Making with Python Resources, 2022, pp 115-124 from Springer

Abstract: Abstract In this chapter, we apply order statistics for sorting a set X of n potential decision alternatives, evaluated on m incommensurable performance criteria, into q 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 O(nmq).

Date: 2022
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DOI: 10.1007/978-3-030-90928-4_9

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