Modelling Critical Infrastructures in Presence of Lack of Data with Simulated Annealing – Like Algorithms

We propose a method to analyze inter-dependencies of technological networks and infrastructures when dealing with few available data or missing data. We suggest a simple inclusive index for inter-dependencies and note that even introducing broad simplifications, it is not possible to provide enough information to whatever analysis framework. Hence we resort to a Simulated Annealing–like algorithm (SAFE) to calculate the most probable cascading failure scenarios following a given unfavourable event in the network, compatibly with the previously known data. SAFE gives an exact definition of the otherwise vague notion of criticality and individuates the “critical” links/nodes. Moreover, a uniform probability distribution is used to approximate the unknown or missing data in order to cope with the recent finding that Critical Infrastructures such as the power system exhibit the self-organizing criticality phenomenon. A toy example based on a real topology is given; SAFE proves to be a reasonably fast, accurate and computationally simple evaluation tool in presence of more than 50% missing data.