Decision Making with a Random Walk in a Discrete Time Markov Chain

The paper describes a Markov model for multi-criteria and multi-person decision making. The motivation results from a demand observed in the early stages of an innovation process. Here, many alternatives need to be evaluated by several decision makers with respect to several criteria. The model derivation and description can be split into the evaluation process and the decision process. The pair wise comparisons can be combined by weighting them according to the importance of the criteria and decision makers, resulting in a discrete-time Markov chain. A random walk on this DTMC models the decision process, where a longer state sojourn time implies a better alternative. We believe that this model meets the demands in the early stages of an innovation process.