Quantifying and Mitigating the Splitting Bias and Other Value Tree-Induced Weighting Biases

This paper develops a model for estimating and correcting attribute-weighting biases (such as the splitting bias) that result from the use of value trees when structuring value function weight elicitation. The model is based on the conjecture that attribute weights are influenced by tree structure and a subject's use of the “anchor-and-adjust” heuristic, meaning that the subject starts with an equal allocation of weight among attributes in each tree partition and then adjusts the weights to reflect his or her innate preferences. Adjustments tend to be insufficient, resulting in attribute weights that are closer in value to each other than if the anchor-and-adjust heuristic was not employed. Weights corresponding to environmental and economic attributes of electric system expansion alternatives are elicited from employees of an electric utility and used to illustrate the existence and correction of value tree-induced attribute-weighting biases. Two weight sets are elicited from each subject, one using a nonhierarchical assessment and the other using a hierarchical one. The model results support the hypothesis that a bias exists that is consistent with the anchor-and-adjust heuristic. An analysis of rankings of alternatives and value losses caused by using elicited versus model-estimated debiased weight sets is provided.