Scoring from Pairwise Winning Indices

TL;DR

This paper introduces a new scoring method based on pairwise winning indices within stochastic multicriteria analysis, enabling both ranking and explanation of alternatives by decomposing scores into criterion contributions.

Contribution

The proposed method uniquely expresses scores as additive functions, allowing for detailed explanation of why an alternative ranks as it does, unlike previous purely ranking-focused approaches.

Findings

The method accurately represents decision maker preferences.

Simulation results show high statistical robustness.

Case study demonstrates practical applicability.

Abstract

The pairwise winning indices, computed in the Stochastic Multicriteria Acceptability Analysis, give the probability with which an alternative is preferred to another taking into account all the instances of the assumed preference model compatible with the information provided by the Decision Maker in terms of pairwise preference comparisons of reference alternatives. In this paper we present a new scoring method assigning a value to each alternative summarizing the results of the pairwise winning indices. Several procedures assigning a score to each alternative on the basis of the pairwise winning indices have been provided in literature. However, while all of them compute this score just to rank the alternatives under consideration, our method, expressing the score in terms of an additive value function, permits to disaggregate the overall evaluation of each alternative in the sum of contributions of considered criteria. This will permit not only to rank the alternatives but also to explain the reasons for which an alternative obtains its evaluation and, consequently, fills a certain ranking position. To prove the efficiency of the method in representing the preferences of the Decision Maker, we performed an extensive set of simulations varying the number of alternatives and criteria. The results of the simulations, analyzed from a statistical point of view, show the goodness of our procedure. The applicability of the method to decision making problems is explained by means of a case study related to the evaluation of financial funds.