Stochastic ordinal regression for multiple criteria sorting problems

We present a new approach for multiple criteria sorting problems. We consider sorting procedures applying general additive value functions compatible with the given assignment examples. For the decision alternatives, we provide four types of results: (1) necessary and possible assignments from Robust Ordinal Regression (ROR), (2) class acceptability indices from a suitably adapted Stochastic Multicriteria Acceptability Analysis (SMAA) model, (3) necessary and possible assignment-based preference relations, and (4) assignment-based pair-wise outranking indices. We show how the results provided by ROR and SMAA complement each other and combine them under a unified decision aiding framework. Application of the approach is demonstrated by classifying 27 countries in 4 democracy regimes.

► Method for multiple criteria sorting using general additive value functions
► Robustness and stochastic analysis combined under unified decision aiding framework
► Analysis of necessary and possible assignments enriched with presentation of class acceptability indices
► Framework for comparing alternatives in a pair-wise fashion on the basis of their possible assignments
► Application to a problem of assigning countries to different types of democracy regimes