Anchoring bias in eliciting attribute weights and values in multi-attribute decision-making

Rezaei, Jafar

doi:10.1080/12460125.2020.1840705

Cited by 27 publications

(28 citation statements)

References 75 publications

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“…There are versions of this method that consider the range of attributes, but in this paper, similar to Rezaei (2021) and Bottomley and Doyle (2001), we intentionally use the original version of SMART, and the ranges of attributes were not described to the subjects, which we already do in Swing (see Section 3.2), providing a more diverse set of methods.…”

Section: Multiattribute Decision‐makingmentioning

confidence: 99%

“…The channels for achieving these subjects were: public call in the LinkedIn, ResearchGate and then screening based on resume, taking advantage of the top national researchers in this field, entrusting them to the same subjects to introduce other subjects (snowball), and taking advantage of the opinions of professors at significant universities and introducing the subjects by them and the use of students is very common for this type of research (see, for instance, Buchanan and Corner (1997), Hämäläinen and Alaja (2008), Rezaei (2021)). Subjects participated voluntarily with no bonus for their participation, which reduced the chance of participation by unmotivated subjects (Rezaei, 2021). Although the necessary number of subjects for a reliable within‐subjects experiment design is relatively small, due to its high‐level controllability, to generate external validity, 158 questionnaires were sent to subjects, out of which 149 participants started the survey and 3 were dropped out due to incompleteness resulting in a collection of 146 correct and complete ones.…”

Section: Experimental Studiesmentioning

confidence: 99%

“…According to Marttunen et al (2018), Rezaei (2021), and Montibeller and von Winterfeldt (2015a), there are a few researchers who have discussed cognitive biases in MADM. In one of the earliest studies in this area, Gabrielli and von Winterfeldt (1978) examined the sensitivity of the weights of the attributes to the changes of alternatives range in an experimental study, using the SMART (simple multiattribute rating technique) weighting method.…”

Section: Introductionmentioning

confidence: 99%

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

Rezaei

Arab

Mehregan

2021

Behavioral Decision Making

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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).

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Section: Multiattribute Decision‐makingmentioning

confidence: 99%

Section: Experimental Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Rezaei

Arab

Mehregan

2021

Behavioral Decision Making

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“…This initial information (anchor) affects further evaluations/judgments conducted by DM and the adjustments which are later done to the evaluation are not sufficient. This source is analyzed comprehensively in the work of Lieder, Griffiths, Huys, and Goodman (2018) and Rezaei (2020) in the context of multi-criteria decisionmaking. Jacobi and Hobbs (2007) investigated the splitting bias, which refers to a situation where presenting an attribute in more detail, may increase the weight it receives.…”

Section: Literature Reviewmentioning

confidence: 99%

A decision support system for detecting and handling biased decision-makers in multi criteria group decision-making problems

Rabiee

Aslani

Rezaei

2021

Expert Systems with Applications

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Detecting and handling biased decision-makers in the group decision-making process is overlooked in the literature. This paper aims to develop an anti-biased statistical approach, including extreme, moderate, and soft versions, as a decision support system for group decision-making (GDM) to detect and handle the bias. The extreme version starts with eliminating the biased decision-makers (DMs). For this purpose, the DMs with a lower Biasedness Index value than a predefined threshold are removed from the process. Next, it continues with a procedure to mitigate the effect of partially biased DMs by assigning different weights to DMs with respect to their biasedness level. To do so, two ratios for the remaining DMs are calculated: (i) Overlap Ratio, which shows the relative value of overlap between the confidence interval (CI) of each DM and the maximum possible overlap value. (ii) Relative confidence interval CI which reflects the relative value of CI for each DM compared to the confidence interval CI of all DMs. The final step is assigning weight to each DM, considering the two values Overlap Ratio and Relative confidence interval. DMs with closer opinions to the aggregated opinion of all DMs, or those with an adequate level of discrimination in their judgments gain more weight. The framework addresses and prescribes possible actions for all possible cases in GDM including without outliers, cases with partial outliers, and extreme cases with complete disagreement among DMs, or when none of the DMs show an adequate level of discrimination power. The moderate version preassigns a minimum weight to all unbiased DMs and then follows the weighting step for the remaining total weight. However, the soft version follows the preassignmnet of weights to all DMs in the initial pool, meaning there is no elimination in this setting. The proposed approach is tested for several scenarios with different sizes. Four performance measures are introduced to evaluate the effectiveness of the proposed method. The resulted performance measures show the reliability of the outcomes.

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“…To this end, the best-worst method (BWM) is employed to determine the weight of the individual criteria. BWM that is a structured pairwise comparison-based method (i) helps decisionmakers conduct their evaluation in a systematic way, (ii) due to employing two pairwise comparison vectors, which are formed based on two opposite reference points, in a single optimization model, mitigates potential decision-maker's anchoring bias in the weighting process [13] (iii) requires an efficient number of data to reach conclusions, which, in turn, (v) produces consistent and reliable results [14e16]. Identifying the location of corn cultivation areas as a multi-criteria decision analysis (MCDA) problem and using BWM to solve that problem is the second contribution of this study.…”

Section: Introductionmentioning

confidence: 99%

Corn cultivation location selection for bioethanol production: An application of BWM and extended PROMETHEE II

Kheybari

Javdanmehr

Rezaie

et al. 2021

Energy

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The purpose of this paper is to streamline the supply of corn, one of the key ingredients in the production of bioethanol. Using a comprehensive sustainable framework of criteria (including social, economic, and environmental dimensions), the potential locations of corn cultivation in Iran are examined from a multicriteria decision analysis perspective. To this end, we need to find the importance of the relevant evaluation criteria and a ranking method to rank the potential locations. Best worst method (BWM) is used to determine the weight of the criteria presented in the framework based on the opinion of a sample of Iranian experts, after which the 'preference ranking organization method for enrichment evaluations II' (PROMETHEE II) is applied to ranking the different provinces of Iran. To improve the ranking results, we extend the PROMETHEE II by employing a set of piecewise linear value functions, for which the performance of alternatives with respect to the criteria calculated by the piecewise linear value functions is used to determine the amount of deviation in the first step of PROMETHEE II. The results of the hybrid methodology indicate that the presence of water, land cost and air pollution are the most important factors determining the ranking of the alternatives, and that Kordestan is the best province for corn cultivation in Iran.

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Anchoring bias in eliciting attribute weights and values in multi-attribute decision-making

Cited by 27 publications

References 75 publications

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

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

A decision support system for detecting and handling biased decision-makers in multi criteria group decision-making problems

Corn cultivation location selection for bioethanol production: An application of BWM and extended PROMETHEE II

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