Two Heuristics for Improving the Efficiency of a Markov Chain Based Decision Making Method

The paper describes two heuristics to reduce the number of comparisons necessary to reach a certain goal for 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. With the implementation of the heuristics the number of comparisons necessary could be decreased significant. By reducing the evaluation effort necessary to reach a given goal, we will make the Markov-chain decision making method applicable to real world settings with a larger number of alternatives.