Multi-Criteria-Decision-Analysis (MCDA)

I show that the current approach for ranking a set of candidates having multiple criteria does not work. There is a vast literature based on finding and using weighting factors to determine the relative importance of the criteria. The simplest example of this is the Weighted Sum Model (WSM), which is just the weighted sum equation used in probability to generate mean value. I have developed a different approach. It is a simple set of equations that model the importance and, more critically, the behavior of each criterion. The equations are utility functions, curves that represent how the decision maker views the contribution of each criterion to the overall goal. Each candidate has specific values for each criterion. The utility function equations are combined into an equation that generates a single number for each candidate, called its total value. It is a rating that represents how well each candidate performs the desired task. In this way, we avoid the use of criterion relative weights. Rather, each criterion is represented by its own curve whose x-axis contains its range of values and whose y-axis is a degradation factor that represents the criterion's contribution to the overall goal.