Abstract
Multi-criteria decision-making (MCDM) methods aim at dealing with certain limitations of human information processing. However, cognitive biases, which are discrepancies of human behavior from the behavior of perfectly rational agents, might persist even when MCDM methods are used. In this article, we focus on two among the most common biases—framing and loss aversion. We test whether these cognitive biases can influence in a predictable way both the criteria weights elicited using the Analytic Hierarchy Process (AHP) and the final ranking of alternatives obtained with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). In a controlled experiment we presented two groups of participants with a multi-criteria problem and found that people make different decisions when presented with different but objectively equivalent descriptions (i.e., frames) of the same criteria. Specifically, the results show that framing and loss aversion influenced the responses of decision makers during pairwise comparisons, which in turn caused the rank reversal of criteria weights across groups and resulted in the choice of a different best alternative. We discuss our findings in light of Prospect Theory and show that the particular framing of criteria can influence the outcomes of MCDM in a predictable way. We outline implications for MCDM methodology and highlight possible debiasing techniques.
Highlights
Two groups of participants were asked to make pairwise comparisons on logically equivalent criteria which were framed in two different ways
Multi-criteria decision-making (MCDM) methods that involve judgments by decision-makers are likely to be affected by those biases
In the current study we focused on two among the most studied cognitive biases, namely, the framing and the loss aversion biases
Summary
Multi-criteria decision-making methods have been developed and widely used in the past decades to deal with limitations of human decision making that occur when complex decision environments are involved. The authors discuss in detail the importance and high potential of behavioral OR for bringing improvement into OR methods and propose nine topics for a respective research agenda One of these topics is the study of cognitive aspects, such as cognitive biases. The authors showed that the utility comparison of two alternatives was biased by the decoy effect, the addition of supplementary information about DEA results (efficiency scores and the mention of existing slacks) helped in debiasing the evaluation. Despite these first attempts to study experimentally the impact of cognitive biases on different OR techniques, even fewer studies addressed the issue of cognitive biases on MCDM, . Results revealed that participant were subject to overconfidence bias, and the debiasing techniques had a positive, though limited, debiasing effect
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