Anchoring Bias in the Tradeoff Procedure Within Multi-Attribute Value Theory

Abstract

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.