Rating-by-Ranking with Learned Performance Quantile Norms

Raymond Bisdorff

Chapter Chapter 10 in Algorithmic Decision Making with Python Resources, 2022, pp 125-136 from Springer

Abstract: Abstract We address in this chapter the problem of rating multiple-criteria performances of a set of potential decision alternatives with respect to performance 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.

Date: 2022
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DOI: 10.1007/978-3-030-90928-4_10

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