Explaining robust additive utility models by sequences of preference swaps

TL;DR

This paper introduces a method for generating explanations for robust additive utility models in multicriteria decision analysis, using sequences of preference swaps, with a focus on binary reference scales.

Contribution

It proposes a novel scheme for explanations based on preference swaps and provides an algorithm for binary scales, addressing the issue of unbounded explanation length.

Findings

Explanation length can be unbounded in general cases.

For binary reference scales, explanation length is bounded.

An algorithm is provided for computing explanations in binary scale cases.

Abstract

Multicriteria decision analysis aims at supporting a person facing a decision problem involving conflicting criteria. We consider an additive utility model which provides robust conclusions based on preferences elicited from the decision maker. The recommendations based on these robust conclusions are even more convincing if they are complemented by explanations. We propose a general scheme, based on sequence of preference swaps, in which explanations can be computed. We show first that the length of explanations can be unbounded in the general case. However, in the case of binary reference scales, this length is bounded and we provide an algorithm to compute the corresponding explanation.