Reducing Bias and Uncertainty in Multievaluator Multicriterion Decision Making

Abstract

Many decisions are based on more than one criterion, judged by more than a single evaluator. Multievaluator multicriterion (MEMC) decision making can be controversial if bias or uncertainty find their way into the final decision. In fact, both public and private organizations have recently faced challenges when making decisions. In a previous study, the authors of this paper developed a multievaluator decision making model that reduces the effect of possible uncertainty resulting from an evaluator’s insufficient expertise in a particular criterion. This paper builds on the previous model by also correcting for any possible evaluator favoritism or bias. It presents a more comprehensive mathematical model that supports MEMC decisions and protects decision makers and their agencies from potential criticism. Testing of the model shows that it performs better than the simple averaging method on 100% of the simulations.