A Pairwise comparaison-based model of uncertainty in multi-person multi-criteria decision problems

1. SUMMARY This paper describes a simple model for the local uncertainty in a multi-person multi-criteria decision problem (MPMCDP). The model is motivated by the authors' experiences in the first stage of corporate innovation processes, which are characterized by a very large number of ideas, for which little or no clarifying information is available. Both the uncertainty model and the ranking algorithm are based on pairwise comparisons of alternatives in order to minimise the costs of processing ideas and to improve the reliability of the results. The model allows uncertainty to be detected cheaply and suggests an efficient method for its reduction.