There is hope in attribute weighting

Experimental evidence shows that attribute weighting in value trees is pron e to various biases. The origins of these biases are suggested to be either behavioral or procedural. It is most surprising that recent literature doe s not discuss weighting biases observed in connection with applications whe re these methods are used to support real decision making processes. This i s the situation both with multiattribute value tree analysis and the analyt ic hierarchy process. Can it really be true that biases are only experiment al 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 sugge st that practitioners can do something to avoid biases. We report prelimina ry research results how the increased understanding of the weight elicitati on methodology and the awareness of the biases can decrease their occurrenc e. The objective of this paper is to give research ideas for those mho are interested in studying these effects in practice.