The validity of engineering judgment and expert opinion in hazard and risk analysis: The influence of cognitive biases

Baybutt, Paul

doi:10.1002/prs.11906

Cited by 21 publications

(8 citation statements)

References 12 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although heuristics are another common feature of engineering and engineering education, research on heuristicbased decision-making includes extensive work on cognitive biases that hinder successful outcomes. Baybutt's (2018) analysis of potential cognitive biases affecting the use of heuristics in hazards analysis is a particularly salient example of the challenges of replacing sound engineering judgment with routine heuristics. Though as Barner et al (2021) argue, heuristics can be a valuable component of undergraduate education, we exclude them from our current conceptualization because of these limitations.…”

Section: Studies In Engineering Educationmentioning

confidence: 99%

Theorizing Engineering Judgment at the Intersection of Decision-Making and Identity

Francis¹,

Paretti²,

Riedner³

2022

Studies in Engineering Education

View full text Add to dashboard Cite

Engineering judgment is critical to both engineering education and engineering practice, and the ability to practice or participate in engineering judgment is often considered central to the formation of professional engineering identities. In practice, engineers must make difficult judgments that evaluate potentially competing objectives, ambiguity, uncertainty, incomplete information, and evolving technical knowledge. Nonetheless, while engineering judgment is implicit in engineering work and so central to identification with the profession, educators and practitioners have few actionable frameworks to employ when considering how to develop and assess this capacity in students. In this paper, we propose a theoretical framework designed to inform both educators and researchers that positions engineering judgment at the intersection of the cognitive dimensions of naturalistic decision-making, and discursive dimensions of identity. Our proposed theory positions engineering judgment not only as an individual capacity practiced by individual engineers alone but also as the capacity to position oneself within the discursive community so as to participate in the construction of engineering judgments among a group of professionals working together. Our theory draws on several strands of existing research to theorize a working framework for engineering judgment that considers the cognitive processes associated with making judgments and the inextricable discursive practices associated with negotiating those judgments in context. In constructing this theory, we seek to provide engineering education practitioners and researchers with a framework that can inform the design of assignments, curricula, or experiences that are intended to foster students' participation in the development and practice of engineering judgment.*Author affiliations can be found in the back matter of this article 80 Francis et al. Studies in Engineering Education 83 Francis et al. Studies in Engineering Education 93 Francis et al. Studies in Engineering Education individual cognitive and discursive practices and the cumulative, collaborative enactment of engineering judgment remain practical problems.

show abstract

Section: Studies In Engineering Educationmentioning

confidence: 99%

Theorizing Engineering Judgment at the Intersection of Decision-Making and Identity

Francis¹,

Paretti²,

Riedner³

2022

Studies in Engineering Education

View full text Add to dashboard Cite

show abstract

“…Decision choices also vary between decisionmakers -another engineer faced with the same situation but with a different knowledge base may elect to ground the aircraft while awaiting specialist advice. Descriptive decision analysis provides insight into the motivational and cognitive biases that may cause this (see for example Tversky and Kahneman (1974), Brown and Utley (2019), or Baybutt (2018)). Although perhaps counter-intuitively, grounding an aircraft requires just as strong an argument as a decision to accept a fault and continue flying.…”

Section: Unsupported Judgement To Defer Maintenancementioning

confidence: 99%

“…Engineering and other domain experts are not immune from bias and heuristic influences when assessing risks, even in analytical assessments (Rae and Alexander, 2017;Baybutt, 2018;Brown and Utley, 2019). Furthermore, in an operational situation where the decision basis will not have the support of rigorous analysis, the use of purely subjective judgement can be explicitly permitted.…”

Section: Introductionmentioning

confidence: 99%

Engineering maintenance decision-making with unsupported judgement under operational constraints

McNaught

Saddington

2022

Safety Science

View full text Add to dashboard Cite

“…Thus, even fields which involve technical and statistical knowledge, which could be expected to promote "rationality", are not immune to framing and loss aversion biases. Similarly, these cognitive biases have been found to influence the decision making in a variety of fields, such as management [55], medical science [9,56], finance [57,58], engineering [59,60], law [61] and others. It is therefore likely, that MCDM problems close to any of these fields might suffer from the influence of cognitive biases.…”

Section: Implications For Mcdmmentioning

confidence: 99%

How to Influence the Results of MCDM?—Evidence of the Impact of Cognitive Biases

Melnik-Leroy¹,

Dzemyda²

2021

Mathematics

View full text Add to dashboard Cite

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.

show abstract

The validity of engineering judgment and expert opinion in hazard and risk analysis: The influence of cognitive biases

Cited by 21 publications

References 12 publications

Theorizing Engineering Judgment at the Intersection of Decision-Making and Identity

Theorizing Engineering Judgment at the Intersection of Decision-Making and Identity

Engineering maintenance decision-making with unsupported judgement under operational constraints

How to Influence the Results of MCDM?—Evidence of the Impact of Cognitive Biases

Contact Info

Product

Resources

About