The Library
Distortion models for estimating human error probabilities
Tools
Tools
Tools
Alonso-Martín, Pablo-Ramsés, Montes, Ignacio and Miranda, Enrique (2023) Distortion models for estimating human error probabilities. Safety Science, 157 . 105915. doi:10.1016/j.ssci.2022.105915 ISSN 0925-7535. [ 🗎 Public]. [ (✓) hoa:511 ]
|
PDF
WRAP-distortion-models-estimating-human-error-probabilities-Alonso-Martín-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (930Kb) | Preview |
Official URL: https://doi.org/10.1016/j.ssci.2022.105915
Abstract
Human Reliability Analysis aims at identifying, quantifying and proposing solutions to human factors causing hazardous consequences. Quantifying the influence of the human factors gives rise to human error probabilities, whose estimation is a cumbersome problem. Since these human factors are usually related to other organisational or technological factors, it has been proposed to apply probabilistic graphical models, such as Bayesian or credal networks. However, these can be problematic when conditional probabilities on missing data are involved. While the solutions proposed so far combine frequentist and subjective approaches and are in general not robust to small modifications in the dataset, in this paper we propose an alternative based on distortion models, which are a type of imprecise probabilities. We perform a comparative analysis, showing that our proposal is consistent with the previous studies while giving rise to robust estimations.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||
| SWORD Depositor: | Library Publications Router | ||||||||
| Library of Congress Subject Headings (LCSH): | Operations research, Programming (Mathematics) , Mathematical optimization -- Computer programs, System theory -- Mathematical models, Human engineering, Bayesian statistical decision theory | ||||||||
| Journal or Publication Title: | Safety Science | ||||||||
| Publisher: | Elsevier Science BV | ||||||||
| ISSN: | 0925-7535 | ||||||||
| Official Date: | January 2023 | ||||||||
| Dates: |
|
||||||||
| Volume: | 157 | ||||||||
| Article Number: | 105915 | ||||||||
| DOI: | 10.1016/j.ssci.2022.105915 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||
| Date of first compliant deposit: | 19 October 2022 | ||||||||
| Date of first compliant Open Access: | 21 October 2022 | ||||||||
| RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
