Notes on the weighting biases in value trees

The variation in the structure of value trees can have undesirable effects on the attribute weights. Earlier experiments suggest that an attribute receives a higher weight if it is presented at an upper level in a value tree or if it is split into subattributes. Here we show that it is flawed to make conclusions about the biases at the individual level based on the averages of weights across large groups of subjects. Averages do not describe individual behavior. Furthermore, the averages of weights tend to approach even weights. By using the data from earlier experiments we illustrate how the averaging can produce different phenomena. We also show that the use of weights based on the rank order of attributes only can easily lead to biases when the structure of a value tree is changed. © 1998 John Wiley & Sons, Ltd

Combinatorics · Econometrics · Rank (graph theory · Statistics · Tree (set theory · Value (mathematics · Variation (astronomy · Weighting · Bayesian Modeling and Causal Inference · Mathematics · Multi-Criteria Decision Making