Two heuristics for improving the efficiency of a markov chain based decision making method

The paper describes two heuristics to reduce the nu mber of comparisons necessary to reach a certain goal fo r a Markov model for multi-criteria and multi-person decision making. The motivation results from a dema nd observed in the early stages of an innovation proce ss. Here, many alternatives need to be evaluated by sev eral decision makers with respect to several criteria. W ith the implementation of the heuristics the number of comparisons necessary could be decreased significan t. By reducing the evaluation effort necessary to reac h given goal, we will make the Markov-chain decision making method applicable to real world settings wit h a larger number of alternatives.