Explaining Results of Multi-Criteria Decision Making

TL;DR

This paper presents a novel explanation method for multi-criteria decision-making results, using fine-grained value representations and aggregation techniques to clarify why one alternative is preferred over another.

Contribution

It introduces a new explanation approach for MCDM techniques that maintains detailed value representations and derives explanations through merging and filtering.

Findings

Effective explanations generated for well-known MCDM examples

Method improves understanding of decision outcomes

Computational experiments demonstrate efficacy

Abstract

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.