  From biotechnology to cyber-risks, most extreme technological risks cannot be
reliably estimated from historical statistics. Therefore, engineers resort to
predictive methods, such as fault/event trees in the framework of probabilistic
safety assessment (PSA), which consists in developing models to identify
triggering events, potential accident scenarios, and estimate their severity
and frequency. However, even the best safety analysis struggles to account for
evolving risks resulting from inter-connected networks and cascade effects.
Taking nuclear risks as an example, the predicted plant-specific distribution
of losses is found to be significantly underestimated when compared with
available empirical records. Using a novel database of 99 events with losses
larger than $50'000 constructed by Sovacool, we document a robust power law
distribution with tail exponent mu \approx 0.7. A simple cascade model suggests
that the classification of the different possible safety regimes is
intrinsically unstable in the presence of cascades. Additional continuous
development and validation, making the best use of the experienced realized
incidents, near misses and accidents, is urgently needed to address the
existing known limitations of PSA when aiming at the estimation of total risks.
