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On Measuring the Fitness of a Multiple-Criteria Ranking

In: Algorithmic Decision Making with Python Resources

Author

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  • Raymond Bisdorff

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.

Suggested Citation

  • Raymond Bisdorff, 2022. "On Measuring the Fitness of a Multiple-Criteria Ranking," International Series in Operations Research & Management Science, in: Algorithmic Decision Making with Python Resources, chapter 0, pages 209-223, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-90928-4_16
    DOI: 10.1007/978-3-030-90928-4_16
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