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A Bayesian approach to treat expert-elicited probabilities in human reliability analysis model construction

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  • Podofillini, L.
  • Dang, V.N.

Abstract

In human reliability analysis (HRA), models are often used for the prediction of human error probabilities (HEPs), given a set of performance conditions, typically represented by ratings on a set of influencing factors. The relationships underlying these models (yielding HEPs for specific sets of factor ratings) should ideally be built on empirical data. However the derivation of these relationships in practice has to cope with limited availability of data, so that a strong component of expert judgment is always present. Nevertheless, the incorporation of expert judgment in HRA models is typically not done in a formal way, so that that it is often impossible to distinguish source data and judgments. In this context, this paper presents a Bayesian approach to aggregate expert estimates on human error probabilities to determine the relationships of an HRA model. The idea is to build a computable model using information from experts, provided as estimates. A numerical example demonstrates that the approach formally and transparently represents (and distinguishes) the inherent variability of the HEP quantity as well as that of the experts providing their estimates.

Suggested Citation

  • Podofillini, L. & Dang, V.N., 2013. "A Bayesian approach to treat expert-elicited probabilities in human reliability analysis model construction," Reliability Engineering and System Safety, Elsevier, vol. 117(C), pages 52-64.
    • Handle: RePEc:eee:reensy:v:117:y:2013:i:c:p:52-64
    DOI: 10.1016/j.ress.2013.03.015
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    References listed on IDEAS

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    1. Katrina M Groth & Ali Mosleh, 2012. "Deriving causal Bayesian networks from human reliability analysis data: A methodology and example model," Journal of Risk and Reliability, , vol. 226(4), pages 361-379, August.
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    Cited by:

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    2. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2015. "Bayesian belief networks for human reliability analysis: A review of applications and gaps," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 1-16.
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    5. Liao, Huafei & Groth, Katrina & Stevens-Adams, Susan, 2015. "Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 159-169.
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    7. Podofillini, Luca & Reer, Bernhard & Dang, Vinh N., 2023. "A traceable process to develop Bayesian networks from scarce data and expert judgment: A human reliability analysis application," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    8. Morais, Caroline & Estrada-Lugo, Hector Diego & Tolo, Silvia & Jacques, Tiago & Moura, Raphael & Beer, Michael & Patelli, Edoardo, 2022. "Robust data-driven human reliability analysis using credal networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
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    10. Pandya, Dhruv & Podofillini, Luca & Emert, Frank & Lomax, Antony J. & Dang, Vinh N. & Sansavini, Giovanni, 2020. "Quantification of a human reliability analysis method for radiotherapy applications based on expert judgment aggregation," Reliability Engineering and System Safety, Elsevier, vol. 194(C).
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    12. Greco, Salvatore F. & Podofillini, Luca & Dang, Vinh N., 2021. "A Bayesian model to treat within-category and crew-to-crew variability in simulator data for Human Reliability Analysis," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    13. Zhou, Jian-Lan & Yu, Ze-Tai & Xiao, Ren-Bin, 2022. "A large-scale group Success Likelihood Index Method to estimate human error probabilities in the railway driving process," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    14. Su, Xiaoyan & Mahadevan, Sankaran & Xu, Peida & Deng, Yong, 2014. "Inclusion of task dependence in human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 128(C), pages 41-55.
    15. Musharraf, Mashrura & Bradbury-Squires, David & Khan, Faisal & Veitch, Brian & MacKinnon, Scott & Imtiaz, Syed, 2014. "A virtual experimental technique for data collection for a Bayesian network approach to human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 1-8.
    16. Liu, Jianqiao & Zou, Yanhua & Wang, Wei & Zio, Enrico & Yuan, Chengwei & Wang, Taorui & Jiang, Jianjun, 2022. "A Bayesian belief network framework for nuclear power plant human reliability analysis accounting for dependencies among performance shaping factors," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    17. Groth, Katrina M. & Smith, Curtis L. & Swiler, Laura P., 2014. "A Bayesian method for using simulator data to enhance human error probabilities assigned by existing HRA methods," Reliability Engineering and System Safety, Elsevier, vol. 128(C), pages 32-40.
    18. Kim, Yochan & Park, Jinkyun & Jung, Wondea & Jang, Inseok & Hyun Seong, Poong, 2015. "A statistical approach to estimating effects of performance shaping factors on human error probabilities of soft controls," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 378-387.
    19. Zhang, Xiaoge & Mahadevan, Sankaran & Lau, Nathan & Weinger, Matthew B., 2020. "Multi-source information fusion to assess control room operator performance," Reliability Engineering and System Safety, Elsevier, vol. 194(C).
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