A Markov model for multi-criteria multi-person decision making
René Chelvier, Kristina Dammasch, Graham Horton, Stefan Werner Knoll, Claudia Krull, Benjamin Rauch-Gebbensleben · 2008
This paper describes a new algorithm for the evaluation of alternatives by a group of decision makers according to multiple criteria. The algorithm is motivated by the need to quickly evaluate a large number of ideas in the early stages of an innovation process, when little or no information about the ideas is available. The algorithm is based on a Markov chain model which is derived from pairwise comparisons of ideas. The steady-state solution of this Markov chain yields a ranking vector for the alternatives. The algorithm is similar to the ldquoPageRankrdquo method used by Google. The new algorithm does not require absolute values and allows assignment of weights both to the decision makers and to the evaluation criteria.