In this study, the existence of anchoring bias—people's tendency to rely on, evaluate, and decide based on the first piece of information they receive—is examined in two multi-attribute decision-making (MADM) methods, simple multi-attribute rating technique (SMART), and Swing. Data were collected from university students for a transportation mode selection. Data analysis revealed that the two methods, which have different starting points, display different degrees of anchoring bias. Statistical analyses of the weights obtained from the two methods show that, compared to Swing (with a high anchor), SMART (with a low anchor) produces lower weights for the least important attributes, while for the most important attributes, the opposite is true. Despite their differences in anchoring bias, analytical approaches supported by empirical studies suggest that both methods (SMART and Swing) overweigh the less important attributes and underweigh the more important attributes. As such, we examined whether the best-worst method (BWM), which has two opposite anchors in its procedure (a possible promising anchoring debiasing strategy), could produce results that are less prone to anchoring bias. Our findings show that the BWM is indeed able to produce lower weights (compared to SMART and Swing) for the less important attributes and higher weights for the more important attributes. This study shows the vulnerability of MADM methods with a single anchor and supports the idea that MADM methods with multiple (opposite) anchors, like BWM, are less prone to anchoring bias.

One of the most important steps in formulating and solving a multiattribute decision-making (MADM) problem is weighting the attributes. Most existing weighting methods are based on judgments by experts/decision-makers, which are prone to several cognitive biases, making it necessary to examine these biases in MADM weighting methods and develop debiasing strategies. This study uses experimental analysis to look at equalizing bias—one of the main cognitive biases, where decision-makers tend to assign the same weight to different attributes—in MADM methods. More specifically, we look at AHP (analytic hierarchy process), BWM (best-worst method), PA (point allocation), SMART (simple multiattribute rating technique), and Swing methods under two structuring formats, hierarchical and non-hierarchical. To empirically examine the existence of equalizing bias in these methods, we formulate several hypotheses, which are tested using a public transportation mode selection problem among 146 university students. The results indicate that AHP and BWM have less equalizing bias than SMART, Swing, and PA, and that the hierarchical problem structuring leads to a reduction in the equalizing bias in all five methods and that such a reduction significantly varies among the methods. Our findings prove some debiasing strategies suggested in existing literature, which could be used by real decision-makers (when selecting a method) as well as researchers (when developing new methods).