Andrew Rae

Dr. Andrew Rae

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Research Focus & Metrics

Academic Career Length

24Years (2003-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 March 2020 in Reliability Engineering & System Safety
Verified
Journal Article
Open access
DPDavid J. Provan
David J. Provan
DWDavid D. Woods
David D. Woods
SDSidney Dekker
Sidney Dekker
ARAndrew Rae
Andrew Rae

1 October 2017 in Safety Science
Verified
Journal Article
Closed access
DPDavid J. Provan
David J. Provan
SDSidney W.A. Dekker
Sidney W.A. Dekker
ARAndrew Rae
Andrew Rae

1 May 2014 in Reliability Engineering & System Safety
Verified
Journal Article
Open access
ARAndrew Rae
Andrew Rae
RARob Alexander
Rob Alexander
JMJohn McDermid
John McDermid

1 June 2020 in Safety Science
Verified
Journal Article
Closed access
ARAndrew Rae
Andrew Rae
DPDavid Provan
David Provan
HAHossam Aboelssaad
Hossam Aboelssaad
RARob Alexander
Rob Alexander

1 January 2019 in Safety Science
Verified
Journal Article
Closed access
ARAndrew Rae
Andrew Rae
DPDavid Provan
David Provan

1 November 2017 in Safety Science
Verified
Journal Article
Open access
ARAndrew Rae
Andrew Rae
RARob Alexander
Rob Alexander
Safety analysis frequently relies on human estimates of the likelihood of specific events. For this purpose, the opinions of experts are given greater weight than the opinions of non-experts. Combinations of individual judgements are given greater weight than judgements made by a lone expert. Various authors advocate specific techniques for eliciting and combining these judgements. All of these factors – the use of experts, the use of multiple opinions, and the use of elicitation and combination techniques – serve to increase subjective confidence in the safety analysis. But is this confidence justified? Do the factors increase the actual validity of the analysis in proportion to the increase in subjective confidence? In this paper, by means of a critical synthesis of evidence from multiple disciplines, we argue that it is plausible that expert judgement deserves special standing, but only for well understood local causal mechanisms. We also conclude that expert judgements can be improved by using appropriate elicitation techniques, including by combining judgement from multiple experts. There is, however, no evidence to suggest that fuzzy, neural network, or any other form of complicated processing of expert judgement has any advantage over simple combination mechanisms.Arts, Education & Law Group, School of Humanities, Languages and Social SciencesFull Tex