  In Probabilistic Risk Management, risk is characterized by two quantities:
the magnitude (or severity) of the adverse consequences that can potentially
result from the given activity or action, and by the likelihood of occurrence
of the given adverse consequences. But a risk seldom exists in isolation: chain
of consequences must be examined, as the outcome of one risk can increase the
likelihood of other risks. Systemic theory must complement classic PRM. Indeed
these chains are composed of many different elements, all of which may have a
critical importance at many different levels. Furthermore, when urban
catastrophes are envisioned, space and time constraints are key determinants of
the workings and dynamics of these chains of catastrophes: models must include
a correct spatial topology of the studied risk. Finally, literature insists on
the importance small events can have on the risk on a greater scale: urban
risks management models belong to self-organized criticality theory. We chose
multiagent systems to incorporate this property in our model: the behavior of
an agent can transform the dynamics of important groups of them.
