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Predicting the validity of expert judgments in assessing the impact of risk mitigation through failure prevention and correction

Predicting the validity of expert judgments in assessing the impact of risk mitigation through failure prevention and correction
Predicting the validity of expert judgments in assessing the impact of risk mitigation through failure prevention and correction
Operational risk management of autonomous vehicles in extreme environments is heavily dependent on expert judgments and, in particular, judgments of the likelihood that a failure mitigation action, via correction and prevention, will annul the consequences of a specific fault. However, extant research has not examined the reliability of experts in estimating the probability of failure mitigation. For systems operations in extreme environments, the probability of failure mitigation is taken as a proxy of the probability of a fault not re-occurring. Using a priori expert judgments for an AUV mission in the Arctic and a posteriori mission field data, we subsequently developed a generalized linear model that enabled us to investigate this relationship. We found that the probability of failure mitigation alone cannot be used as a proxy for the probability of fault not re-occurring. We conclude that it is also essential to include the effort to implement the failure mitigation when estimating the probability of fault not re-occurring. The effort is the time taken by a person (measured in person months) to execute the task required to implement the fault correction action. We show that once a modicum of operational data is obtained, it is possible to define a generalized linear logistic model to estimate the probability a fault not re-occurring. We discuss how our findings are important to all autonomous vehicle operations and how similar operations can benefit from revising expert judgments of risk mitigation to take account of the effort required to reduce key risks.
0272-4332
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Dawson, Ian
dff1b440-6c83-4354-92b6-04809460b01a
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Dawson, Ian
dff1b440-6c83-4354-92b6-04809460b01a

Brito, Mario and Dawson, Ian (2020) Predicting the validity of expert judgments in assessing the impact of risk mitigation through failure prevention and correction. Risk Analysis. (In Press)

Record type: Article

Abstract

Operational risk management of autonomous vehicles in extreme environments is heavily dependent on expert judgments and, in particular, judgments of the likelihood that a failure mitigation action, via correction and prevention, will annul the consequences of a specific fault. However, extant research has not examined the reliability of experts in estimating the probability of failure mitigation. For systems operations in extreme environments, the probability of failure mitigation is taken as a proxy of the probability of a fault not re-occurring. Using a priori expert judgments for an AUV mission in the Arctic and a posteriori mission field data, we subsequently developed a generalized linear model that enabled us to investigate this relationship. We found that the probability of failure mitigation alone cannot be used as a proxy for the probability of fault not re-occurring. We conclude that it is also essential to include the effort to implement the failure mitigation when estimating the probability of fault not re-occurring. The effort is the time taken by a person (measured in person months) to execute the task required to implement the fault correction action. We show that once a modicum of operational data is obtained, it is possible to define a generalized linear logistic model to estimate the probability a fault not re-occurring. We discuss how our findings are important to all autonomous vehicle operations and how similar operations can benefit from revising expert judgments of risk mitigation to take account of the effort required to reduce key risks.

Text
AUV Risk Mitigation Paper - Accepted Manuscript
Restricted to Repository staff only until 26 May 2022.
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Accepted/In Press date: 22 May 2020

Identifiers

Local EPrints ID: 440977
URI: http://eprints.soton.ac.uk/id/eprint/440977
ISSN: 0272-4332
PURE UUID: 61eb4031-3bf3-4b1b-b7a9-42a56aafcc27
ORCID for Ian Dawson: ORCID iD orcid.org/0000-0003-0555-9682

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Date deposited: 26 May 2020 16:32
Last modified: 29 Jul 2020 01:40

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