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Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM)

Editor
Michael G. Stamatelatos
Michael G. Stamatelatos
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Harold S. Blackman
Harold S. Blackman
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ISBN-10:
0791802442
No. of Pages:
2576
Publisher:
ASME Press
Publication date:
2006

Much information is available from operating experience about contexts under the condition of a decision-based error of commission (EOC). Illustrated by a set of 26 operational EOC cases, this paper presents a data analysis technique utilizing this information for the compilation of an interval scale that correlates with the EOC likelihood. In addition, conclusions for achieving advances in human reliability analysis (HRA) are drawn.

The cases are analyzed with a framework of two types of factors: causal factors (e.g. misleading indication) which relate to the initial motivation (or consideration) of the inappropriate action, and influencing factors related to situational features (e.g. availability of backup indication) that mediate the impact of a causal factor on the EOC likelihood.

Based on comparative assessments (supported by the evaluation results for these factors), a scale of reliability indices with six discrete categories is finally obtained. On this scale, an index of i=0 represents the worst case (error-forcing effect extremely high), and high indices are corresponding to low EOC probabilities. Since the 26 EOC cases appear as references points on this scale, the scale can be used to estimate the reliability index of a ‘new’ EOC case on the basis of a comparison of patterns of causal and influencing factors. Furthermore, the obtained distribution of contexts (with different reliability indices) under the condition of an EOC can be utilized for a Bayesian estimation of conditional EOC probabilities.

The paper discusses as well problems concerning the proposed scaling technique, namely (1) difficulties in performing comparisons with different causal factors involved, (2) hidden effects of factors not addressed so far, and (3) dependencies between comparisons.

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