Eliciting the weights of attributes is a key step in multi-attribute decision-making methods. The weights usually represent the relative importance of the attributes or the tradeoffs among them in forming a decision. Various weight elicitation methods exist, each based on different assumptions and procedures. Still, many of these methods do not explicitly account for the potential influence of cognitive biases in their design. This study examines the anchoring bias, a well-known cognitive bias, in the weight elicitation step (the Tradeoff procedure) of multi-attribute value theory (MAVT). We developed the following three hypotheses: (i) Using the most important (best) attribute to construct the indifference pairs in the Tradeoff procedure leads to higher weights for the best and worst attributes and lower weights for the other attributes, (ii) using the least important (worst) attribute to construct the indifference pairs in the Tradeoff procedure leads to lower weights for the best and worst attributes and higher weights for the other attributes, and (iii) using both best and worst attributes to construct the indifference pairs (i.e., the best–worst tradeoff: BWT) mitigates the anchoring bias. To test the hypotheses, we conducted an experiment by designing a questionnaire based on MAVT and collected data from 336 participants for a decision problem. The findings indicate that the anchoring bias has a significant impact on the Tradeoff procedure and that the BWT is effective in mitigating this bias.

Anchoring bias refers to the human tendency to rely heavily on an initial piece of information when making judgments. This bias has significant implications for decision analysis methods that rely on human judgments. This study examines the influence of anchoring bias in the value function elicitation step of the multiattribute value theory, specifically within the midvalue splitting procedure. We hypothesize that the starting point provided by the analyst during elicitation creates a bias in decision makers’ judgments, leading to distorted value functions and ultimately affecting decision outcomes. We also hypothesize that counter-anchoring and avoiding the use of anchors mitigate the effect of anchoring bias. To test the hypotheses, we designed an experiment and collected data from 320 subjects. The findings show that the starting point in the midvalue splitting procedure could change the attribute-specific value functions and, consequently, the overall value of the alternatives. Additionally, two debiasing strategies, counter-anchoring and avoiding the use of anchors, were found to be effective in reducing the effect of anchoring bias. The implications of this study can extend to other structured value function elicitation methods.