There is hope in attribute weighting

Experimental evidence shows that attribute weighting in value trees is prone to various biases. The origins of these biases are suggested to be either behavioral or procedural. It is most surprising that recent literature does not discuss weighting biases observed in connection with applications where these methods are used to support real decision making processes. This is the situation both with multiattribute value tree analysis and the analytic hierarchy process. Can it really be true that biases are only experimental artifacts which do not occur in applications? We believe that the risks of biases exist but so far they have been ignored in applications. We think that this is a serious missing link in current research. However, we suggest that practitioners can do something to avoid biases. We report preliminary research results on how the increased understanding of the weight elicitation methodology and the awareness of the biases can decrease their occurrence. The objective of this paper is to give research ideas for those who are interested in studying these effects in practice.