Expert’s Heuristic Biases in Airport Predictive Risk Assessments

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Authors Abstract
Content
Expert perceptions have been increasingly used to perform risk assessments in airport predictive risk assessments in recent years. Although it is known that biases are less influential in groups of experts when compared to laypeople, they still can be residually present in such tasks with this specific group. Therefore, this article aims to propose (1) the pragmatic organization of knowledge about the biases that may affect airport risk assessments by groups of experts and (2) which of them most often arise in this type of analysis and at what intensity. For the development of the work, we carried out a dense bibliographic review of the theme. Later, we performed a predictive risk assessment and a survey, with the support of an experienced group of 30 experts from Brazilian regulatory agency and airport operators. After 1224 risk judgments, experts were able to clearly indicate regulations and their sections that are disproportionately more and less important in terms of risk, leading States to a better regulatory quality only by changing their logic of actions to a risk-based approach. Later the group answered a survey on a list of 12 heuristic biases created from the bibliographic review. Results showed that, in fact experts have a resistance to biases influence, mainly based in their academic and professional background, but also showed this influence is not exactly negligible. It was also possible to rank the heuristic biases in terms of importance and indicate that experts tend to concern with three hierarchical information characteristics when judging risk: at first, would be the form in which risk problems are presented; second, how they interpret information presented; and third, the amount of information presented.
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DOI
https://doi.org/10.4271/09-10-01-0002
Pages
32
Citation
Cunha, D., and Andrade, M., "Expert’s Heuristic Biases in Airport Predictive Risk Assessments," SAE Int. J. Trans. Safety 10(1):23-50, 2022, https://doi.org/10.4271/09-10-01-0002.
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Publisher
Published
Oct 12, 2021
Product Code
09-10-01-0002
Content Type
Journal Article
Language
English