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

Abstract

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).

Aiding philanthropic venture capitalists investing in social enterprises: A multi‐criteria decision‐making approach

“…2. It reduces potential biases such as anchoring and equalizing biases by using two pairwise comparison vectors based on the best and worst criteria (Rezaei et al, 2022a(Rezaei et al, , 2022b. While there are other methods with fewer pairwise comparisons than BWM, such as Tradeoff (Keeney & Raiffa, 1976), SMART, and Swing (Edwards & Barron, 1994), they lack an essential feature, which is the ability to check the consistency of provided pairwise comparisons (Brunelli, 2022;Liang et al, 2022).…”

“…The BWM effectively addresses these issues and is favored by researchers. BWM provides several advantages: It allows experts to make more reliable and consistent pairwise comparisons by clearly understanding the range of evaluation from the outset through the selection of the best and worst criteria. It reduces potential biases such as anchoring and equalizing biases by using two pairwise comparison vectors based on the best and worst criteria (Rezaei et al, 2022a, 2022b). It involves data and time‐efficient pairwise comparisons, requiring 2n‐3 comparisons, as opposed to AHP, which involves n(n−1)/2 comparisons, where n represents the number of criteria.…”

Perception-Based Analysis of the Perspectives of Users and Non-Users of Different Shared Mobility Services

“…Weights will be computed through a Bayesian best-worst method (BWM) since this requires a small amount of data compared with more traditional approaches such as an analytical hierarchical process (AHP), ideal solution (TOPSIS), preference ranking for organization method for enrichment evaluation (PROMETHEE), and elimination and choice translating reality (ELECTRE) ( 29 ). In addition, compared with other multi-criteria analysis methods, other advantages include fewer inconsistencies between criteria, lower equalizing bias ( 30 ), and better transparency for decision makers compared with PROMETHEE, ELECTRE, and TOPSIS ( 27 , 31 ).…”

Prioritizing population-based nutrition-related interventions to prevent and control hypertension in Iran: a multi-criteria decision-making approach

“…Although there are new and useful decision-making methods for MCDM, AHP still has many advantages as follows: (i) AHP is concise and easy to understand and grasp for decision-makers and scholars; (ii) Since it has been applied in various domains, especially in health care recently, the validity of this model can be verified through literature review; (iii) It uses hierarchical structures to model problems and has less equalizing bias; and (iv) This method has strong practicality and can be used in combination with other methods to compensate for its shortcomings [67][68][69][70][71]. Strategy prioritization through a transparent and systematic approach that key stakeholders take into account the available strategy options and all relevant criteria simultaneously, and relative weighting scheme in accordance with the current socioeconomic status of society can help to develop more appropriate measures to achieve the goals of national action plans for prevention and control of NCDs.…”