Explaining robust additive utility models by sequences of preference swaps
This work addresses the need for convincing explanations in decision support systems, offering a domain-specific incremental improvement.
The paper tackles the problem of explaining robust additive utility models in multicriteria decision analysis by proposing a method based on sequences of preference swaps to generate explanations, showing that explanation length can be unbounded in general but is bounded for binary reference scales with an algorithm provided.
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.