Explaining robust additive utility models by sequences of preference
  swaps
  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.
