On Measuring the Fitness of a Multiple-Criteria Ranking

Raymond Bisdorff

Chapter Chapter 16 in Algorithmic Decision Making with Python Resources, 2022, pp 209-223 from Springer

Abstract: Abstract Starting from a motivating decision problem about how to list, from the best to the worst, a set of movies that are star-rated by journalists and movie critics, the chapter shows that Kendall’s ordinal correlation index tau can be extended to a bipolar-valued relational equivalence measure of bipolar-valued digraphs. This finding gives way, on the one hand, to measure the fitness and fairness of multiple-criteria ranking rules. On the other hand, it provides a tool for illustrating preference divergences between decision objectives and criteria.

Date: 2022
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DOI: 10.1007/978-3-030-90928-4_16

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