  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.
