An explorative Bayesian analysis of functional dependencies in emergency management systems

The study of emergency or crisis management practices acquires strategical relevance for resilient decision-making under uncertainty. The assessment of system resilience is an asset to identify potential design or operational improvements of a complex socio-technical system, such as an Emergency Management (EM) system. This research aims at analyzing the functional properties of an EM system recurring to a novel integration of the Functional Resonance Analysis Method (FRAM) and Bayesian Belief Networks (BBN). The FRAM is used to model and display the actors and the interactions in the system, while the BBN, dynamically updated when new data becomes available, supports a complementary quantitative assessment. The methodology is iterated in the analysis of an EM procedure, issued by a second-line Emergency Response organization for Oil and Gas (O&G) operators in Norwegian continental shelf. The results of the study show that the proposed stochastic methodology compensates the drawbacks of traditional FRAM modeling, via the outcomes of BBN quantitative analyses. The findings, contextualized in EM, can be transferred to different socio-technical contexts, both military and civil ones.