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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 reoccurring. Using a priori expert judgments for an autonomous underwater vehicle 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 reoccurring. We conclude that it is also essential to include the effort to implement the failure mitigation when estimating the probability of fault not reoccurring. 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 reoccurring. 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.

Autonomous unmanned vehicles, expert judgment, extreme environments, risk mitigation, risk perception
0272-4332
1928-1943
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, 40 (10), 1928-1943. (doi:10.1111/risa.13539).

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 reoccurring. Using a priori expert judgments for an autonomous underwater vehicle 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 reoccurring. We conclude that it is also essential to include the effort to implement the failure mitigation when estimating the probability of fault not reoccurring. 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 reoccurring. 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.

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Accepted/In Press date: 22 May 2020
e-pub ahead of print date: 19 June 2020
Published date: 19 June 2020
Additional Information: Funding Information: The authors thank the anonymous reviewers for their very insightful comments. We also thank the experts who took part in the expert judgment elicitation conducted in Halifax and in Vancouver. This work was partly funded by the Natural and Environment Research Council Grant NE/I015647/1, “Tracking effectiveness of risk mitigation for autonomous underwater vehicles.” Publisher Copyright: © 2020 The Authors. Risk Analysis published by Wiley Periodicals, Inc. on behalf of Society for Risk Analysis Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
Keywords: Autonomous unmanned vehicles, expert judgment, extreme environments, risk mitigation, risk perception

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 Mario Brito: ORCID iD orcid.org/0000-0002-1779-4535
ORCID for Ian Dawson: ORCID iD orcid.org/0000-0003-0555-9682

Catalogue record

Date deposited: 26 May 2020 16:32
Last modified: 26 May 2022 04:01

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