In this paper, we address the issues of gathering the preferences of a user when they may be uncertain, and of handling the possible ensuing inconsistency. We suggest using possibility theory as a means of modelling this uncertainty and making inferences despite the possible presence of inconsistencies due to user errors. While some parts of our approach are general, we specifically apply it to the case of numerical models and show through synthetic experiments the potential benefits of our approach.