A Markov Model for Multi-Criteria Multi-Person Decision Making

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 "PageRank" 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.