Questionable stepwise explanations for a robust additive preference model

In this paper, we study Multiple Criteria Decision Aiding (MCDA) problems modeled using an additive value function. We consider an epistemic framework in which the preferences of the decision-maker are imprecisely specified, yielding a robust additive preference model. In this context, we are interested in explaining recommendations derived from such robust model using a transitive sequence of preference swaps. Previous work laid the foundations for explaining the necessary preference relation through a sequence of necessary preference swaps. We extend this to take into account non-necessary preference, yielding to so-called “questionable explanations”: a chain of alternatives which is non-increasing w.r.t. preference of the decision-maker. This approach provides additional descriptive power for explaining robust recommendations. We propose an efficient resolution engine based on Mixed-Integer Linear Programs, and we conduct numerical experiments to assess the benefit of our explanation strategy.