However, in some cases, they lead to error and deviations from rational decision-making, as a result of which the optimal results in a problem are distorted (Bazerman and Moore, 1994). Generally, heuristics help simplify the decision-making process. As Tversky and Kahneman ( 1974) explained, people's subjective judgments rely on a limited number of heuristic principles that reduce the complex tasks involved. Various MADM weighting methods have been developed in recent decades, most of which are based on DMs’ evaluation and subjective judgment concerning the weights of the attributes. The main subject of this study is the identification of the relative importance (weight) of the attributes. The attributes not only improve decision-makers’ (DMs) ability to define the alternatives, but their relative importance also plays a crucial role in formulating and solving the problems. In any MADM problem, a list of alternatives and a list of attributes must be identified. Multi-attribute decision-making (MADM), also called multi-criteria decision-making (MCDM), involves evaluating different alternatives (options) with respect to certain attributes (criteria) with the ultimate aim of ranking, sorting, or selecting the alternatives. 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. 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. 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. 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. 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. Data analysis revealed that the two methods, which have different starting points, display different degrees of anchoring bias. Data were collected from university students for a transportation mode selection. 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.
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