RISK PREDICTION FOR MODERN TECHNOLOGICAL SYSTEMS

We have already examined the worldwide trends for outcomes (measured as accidents, errors and events) using data available for large complex technological systems with human involvement. That analysis was a dissection of the basic available, published data on real and measured risks, for trends and inter-comparisons of outcome rates. We found and showed how all the data agreed with the learning theory when the accumulated experience is accounted for. Here, learning includes both positive and negative feedback, directly or indirectly, as a result of prior outcomes or experience gained, in both the organizational and individual contexts. Our purpose here and now is to try to introduce some predictability and insight into the risk or occurrence of these apparently random events. In seeking such a general risk prediction we adopt a fundamental theoretical approach that is and must be testable against the world’s existing data. Comparisons with outcome error data from the world’s commercial airlines, the two shuttle failures, and from nuclear plant operator transient control behaviour, show a reasonable level of accord. The results demonstrate that the risk is dynamic, and that it may be predicted using the MERE learning hypothesis and the minimum failure rate, and can be utilized for predictive risk analysis purposes. 1. The risk prediction purpose Modern technological systems fail, sometimes with catastrophic consequences, sometimes just everyday injuries and deaths. The risk is given by the probability of failure, error or more generally any outcome. Recently the crash of the NASA Space Shuttle Columbia, the great blackout of the North East USA and Canada, the explosion at the Texas City refinery all occurred. Other smaller but also key accidents have also occurred: the midair collision over Europe of two aircraft carrying the latest collision avoidance system; the glider landing of a jet aircraft out of fuel in the Azores; a concrete highway overpass collapsing in Laval, Quebec; the huge oil tank fire in England; more ships sinking, more trains derailing, even more cars colliding, and evermore medical errors. We have already examined the worldwide trends for outcomes (measured as accidents, errors and events) using data available for large complex technological systems with human involvement. That analysis was a dissection of the basic available, published data on real and measured risks, for trends and inter-comparisons of outcome rates. We found and showed how all the data agreed with the learning theory when the accumulated experience is accounted for. Here, learning includes both positive and negative feedback, directly or indirectly, as a result of prior outcomes or experience gained, in both the organizational and individual contexts as in [5]. Our purpose here and now is to try to introduce some predictability and insight into the risk or occurrence of these apparently random events. In seeking such a general risk prediction we adopt a fundamental theoretical approach that is and must be testable against the world’s existing data.

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