An explorative Bayesian analysis of functional dependencies in emergency management systems

Abstract

The study of emergency or crisis management practices acquires strategical relevancefor resilient decision-making under uncertainty. The assessment of system resilience isan asset to identify potential design or operational improvements of a complex socio-technical system, such as an Emergency Management (EM) system. This research aimsat analyzing the functional properties of an EM system recurring to a novel integrationof 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 thesystem, while the BBN, dynamically updated when new data becomes available, sup-ports a complementary quantitative assessment. The methodology is iterated in theanalysis of an EM procedure, issued by a second-line Emergency Response organiza-tion for Oil and Gas (O&G) operators in Norwegian continental shelf. The results of thestudy show that the proposed stochastic methodology compensates the drawbacks oftraditional FRAM modeling, via the outcomes of BBN quantitative analyses. The find-ings, contextualized in EM, can be transferred to different socio-technical contexts,both military and civil ones