This paper documents research into the use of an adaptive cultural model and collective intelligence as a means of characterizing the reliability of bulk power networks. Historically, utilities support the reliable design and operation of bulk power networks through first-order contingency analysis. In contingency analyses the list of candidate elements for disruption are identified by engineers a priori based on the rate at which the elements failure through the course of normal grid operation. The new method, an implementation of particle swarm analysis, a swarm of 'virtual power engineers;' successfully identified the set of network elements which, if disrupted, would possibly lead to a cascading series of events resulting in the most wide spread damage. The methodology is technology independent: it can be applied on not only for reliability analysis of bulk power systems, but also other energy systems or transportation systems. The methodology is scale neutral: it can be applied to power distribution networks at the local, state or regional level.