  Extreme shock models have been introduced in Gut and H\"usler (1999) to study
systems that at random times are subject to shock of random magnitude. These
systems break down when some shock overcomes a given resistance level. In this
paper we propose an alternative approach to extreme shock models using
reinforced urn processes. As a consequence of this we are able to look at the
same problem under a Bayesian nonparametric perspective, providing the
predictive distribution of systems' defaults.
