AUTHOR=Schrøder Søren Espersen , San Martin David , Foti Giuseppe , Gutierrez Monica , Iñarra Chastagnol Bruno , Nielsen J. Rasmus , Larsen Erling TITLE=Making the objectively best choice for side-stream resources—Verification of a debiasing method based on cognitive maps and attribute substitution JOURNAL=Frontiers in Food Science and Technology VOLUME=3 YEAR=2023 URL=https://www.frontiersin.org/journals/food-science-and-technology/articles/10.3389/frfst.2023.1068974 DOI=10.3389/frfst.2023.1068974 ISSN=2674-1121 ABSTRACT=

Multi-criteria decision-making (MCDM) tools are essentially methods to enable a decision maker to achieve a more objective approach to a given decision scenario using quantitative methods. One such complex decision scenario is the underutilization of side-streams in the seafood industry, which is brought about by a combination of complex decision challenges related to processing methods, storage methods, logistics, technical viability, status quo mindset, and the attitude of the decision maker. However, the influence and identification of cognitive biases (e.g., loss aversion bias) in MCDM tools are rarely accounted for and may result in a less objective decision process due to subjective influences, which can influence the valorization and utilization of seafood side-streams in a company. To enable a more objective approach where the influence of these cognitive biases is corrected, in this paper, we propose a debiasing method based on the UN’s 14 SDGs, cognitive mapping (CM), and attribute substitution (AS) as an extension of MCDM tools and the modeling of seafood processing. The results of the case-specific implementation show that the proposed method can identify cognitive biases and correct these by enabling the implementation of relevant debiasing techniques that can aid a decision marker in choosing the best alternative when it comes to decisions on reducing wasted side-streams and increasing the sustainability of their food processing. It was found that the debiasing application provided a correction of the user ranking for the best-evaluated alternative within a side-stream scenario to be in line with the experts’ ranking for the same scenario in terms of environmentally and economically efficient production. This is a novel approach combining existing theories and methods into a single bias identification and debiasing method, which is designed to be generic and can be implemented in other sectors and industries using MCDM tools in their decision process. The approach provides industry and science with a verified and structured method to achieve objectivity through the identification and correction of decision-making biases that also supports a balance between a company’s economic and environmental goals.