 Managing and predicting technological risk and human reliability: A new learning curve theoryR B Duffey and J W Saull Journal of Risk and Reliability, 2008, vol. 222, issue 2, 245-254 Abstract: In the use of homotechnological systems (HTSs) over the last two centuries, literally millions and millions of data points have been amassed on human deaths, injuries, losses, damages, disasters, and tragedies. The technological risk needs to be reduced by predicting the human reliability. A fundamental theory based on the learning hypothesis has been developed to provide a sound prediction, which is consistent with the theory of error correction. The rate of reduction of the error rate with increasing experience is proportional to the rate at which errors are occurring. The risk probability for any outcome or error is derived in the form of a ‘human bathtub’ curve. The probability of an outcome as a function of accumulated experience is predicted. The measure of the risk is shown to be the information entropy, which is an objective measure of order observed in organizational learning curves, which arise from the unobserved disorder and statistical fluctuations of human reliability. Thus, the technological risk is inextricably coupled to, and interwoven with, the human reliability by a prediction methodology that is validated by data. Keywords: technological risk; human reliability; learning curve theory; homotechnological systems; learning hypothesis; error correction; information entropy (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:222:y:2008:i:2:p:245-254 Access Statistics for this article More articles in Journal of Risk and Reliability |
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