  Critical infrastructure systems must be both robust and resilient in order to
ensure the functioning of society. To improve the performance of such systems,
we often use risk and vulnerability analysis to find and address system
weaknesses. A critical component of such analyses is the ability to accurately
determine the negative consequences of various types of failures in the system.
Numerous mathematical and simulation models exist which can be used to this
end. However, there are relatively few studies comparing the implications of
using different modeling approaches in the context of comprehensive risk
analysis of critical infrastructures. Thus in this paper, we suggest a
classification of these models, which span from simple topologically-oriented
models to advanced physical flow-based models. Here, we focus on electric power
systems and present a study aimed at understanding the tradeoffs between
simplicity and fidelity in models used in the context of risk analysis.
Specifically, the purpose of this paper is to compare performances measures
achieved with a spectrum of approaches typically used for risk and
vulnerability analysis of electric power systems and evaluate if more
simplified topological measures can be combined using statistical methods to be
used as a surrogate for physical flow models. The results of our work provide
guidance as to appropriate models or combination of models to use when
analyzing large-scale critical infrastructure systems, where simulation times
quickly become insurmountable when using more advanced models, severely
limiting the extent of analyses that can be performed.
