Explaining Results of Multi-Criteria Decision Making
This addresses the need for interpretability in decision-making systems, though it appears incremental as it builds on existing MCDM methods.
The paper tackles the problem of explaining results from multi-criteria decision-making techniques like WSM and AHP by maintaining fine-grained value representations and deriving explanations through operations like merging and filtering, showing usefulness through examples and computational experiments.
We introduce a method for explaining the results of various linear and hierarchical multi-criteria decision-making (MCDM) techniques such as WSM and AHP. The two key ideas are (A) to maintain a fine-grained representation of the values manipulated by these techniques and (B) to derive explanations from these representations through merging, filtering, and aggregating operations. An explanation in our model presents a high-level comparison of two alternatives in an MCDM problem, presumably an optimal and a non-optimal one, illuminating why one alternative was preferred over the other one. We show the usefulness of our techniques by generating explanations for two well-known examples from the MCDM literature. Finally, we show their efficacy by performing computational experiments.