Equalizing bias in eliciting attribute weights in multiattribute decision?making: experimental research

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