On preference elicitation processes which mitigate the accumulation of biases in multi-criteria decision analysis

Lahtinen, Tuomas J.; Hämäläinen, Raimo P.; Jenytin, Cosmo

doi:10.1016/j.ejor.2019.09.004

Cited by 25 publications

(13 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, we could think of other multi-attribute decision-making methods, such as DR (direct rating), where anchor is avoided. The second strategy, providing multiple and counter-anchors, which is also called consider-the-opposite strategy or Rotating the reference point (Lahtinen et al, 2020), that has been proven to be effective in some other studies (Adame, 2016;Joslyn et al, 2011;Mussweiler, 2002; could be considered here. Consider-the-opposite strategy means taking into account (multiple) opposite anchors.…”

Section: Suggestions For Debiasingmentioning

confidence: 99%

“…Lahtinen and Hämäläinen (2016) studied the accumulation of biases in different stages of a multi-attribute decision-making problem (path dependence) and they showed that path dependency can occur in Even-Swaps method. In a later study, Lahtinen et al (2020) proposed four debiasing techniques for multi-attribute decision-making: (i) 'Introducing a virtual reference alternative in the decision problem' (ii) 'Introducing an auxiliary measuring stick attribute' (iii) 'Rotating the reference point' (iv) 'Restarting the decision process at an intermediate step with a reduced set of alternatives'. They applied these techniques to Even-Swaps method to show the effectiveness of these techniques and recommended the same techniques for debiasing in SWING and Tradeoff.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Rezaei

2020

Journal of Decision Systems

View full text Add to dashboard Cite

The aim of this study is to look at anchoring bias-one of the main cognitive biases-in two multi-attribute decision-making methods, SMART and Swing. First, the existence of anchoring bias in these two methods for eliciting attribute weights and attribute values is theorised. Then, a special experiment is designed to compare the results estimated by the respondents and the actual results to measure potential anchoring bias. Data were collected from a sample of university students. The statistical analyses indicate the existence of anchoring bias in the two methods. It is also interesting to see that the impact of anchoring bias in estimates provided by the decision-makers on the obtained weights and values depends on the method that is used. These findings have significant implications for the actual decision-makers. Future research may consider the potential existence of cognitive biases in other multi-attribute decision-making methods and focus on developing mitigation strategies.

show abstract

Section: Suggestions For Debiasingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Rezaei

2020

Journal of Decision Systems

View full text Add to dashboard Cite

show abstract

“…Different decision paths were accompanied by corresponding errors affecting decision results. Lahtinen et al (2020) put forward four new techniques to mitigate biases in the even swap process, improving the accuracy of results. Besides these theoretical developments, the even swaps method were also applied in several realms, such as the strategy selection in a rural enterprise (Kajanus et al, 2001), plastic surgery (Gomes et al, 2012), and site selection of reservoir (Milutinovic et al, 2018).…”

Section: The Even Swaps Methodsmentioning

confidence: 99%

“…Assumption 2. (Lahtinen et al, 2020) Suppose that for alternative a i , the utility variation of criterion c j is i U j…”

Section: Assumption 1 the Marginal Utility Of Criterion Is A Constantmentioning

confidence: 99%

“…Using the trade-off, experts can make the values of a part of alternatives under certain criteria the same, leading to the convenience to compare alternatives. Due to this advantage, the even swaps method has attracted much attention, and its theoretical developments were mainly in three directions including the software development (Mustajoki & Hämäläinen, 2005, 2007, the combination with other methods (Dereli & Altun, 2012;Elahi & Yu, 2012;Li & Ma, 2008), and the consideration of biases (Lahtinen & Hämäläinen, 2016;Lahtinen et al, 2020). Meanwhile, the even swaps method have been applied to many realms (Gomes et al, 2012;Kajanus et al, 2001;Milutinovic et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Criterion Utility Conversion Technique for Probabilistic Linguistic Multiple Criteria Analysis in Emergency Management

Qin

Liao

Jiang

2021

Technological and Economic Development of Economy

View full text Add to dashboard Cite

In multiple criteria decision making (MCDM), the even swaps method uses the relationships of criteria to make trade-offs but the burdens of experts are heavy; the linear programming technique for multidimensional analysis of preference (LINMAP) method cannot deal with the inter-dependencies among criteria but the cognitive burdens of experts are low. Taking the advantages of both these methods, this study proposes a criterion utility conversion (CUC) technique to solve probabilistic linguistic MCDM problems given that the probabilistic linguistic term set (PLTS) can reflect the psychology of experts when making evaluations. The utility conversion process is first proposed based on the marginal utilities of criteria. Then, the criterion preference ratios of experts are refined from the utility conversion process. Based on the criterion preference ratios and the operations of PLTSs, the adjusted probabilistic linguistic expected values of alternatives are calculated. The consistency and inconsistency indexes of alternatives and criteria are defined to set up the linear programming used to work out the criterion preference ratios. An illustration about the selection of emergency logistics supplier is given to validate the proposed method. The comparative analysis indicates the low cognitive burden, high stability, and strong applicability of the proposed method.

show abstract

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

Rezaei

Arab

Mehregan

2021

Behavioral Decision Making

View full text Add to dashboard Cite

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).

show abstract

On preference elicitation processes which mitigate the accumulation of biases in multi-criteria decision analysis

Cited by 25 publications

References 37 publications

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

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

A Criterion Utility Conversion Technique for Probabilistic Linguistic Multiple Criteria Analysis in Emergency Management

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

Contact Info

Product

Resources

About