what-when-how
In Depth Tutorials and Information
Technical Analysis
Organizational and Human Analysis
Expertise
General Survey
A. Definition of the system, the
context, the problematic
B. Specific Survey
B. Experience Feedback
C. Identification of Risks
C. Current Situation Analysis
D. Representation of the Situation
E. Quantified Treatment
(Risk estimation)
Decision Making/Communication
Figure9.7
Stepsofaglobalriskanalysisinaperspectiveofdecision-makingsup-
port.(FromLéger,A.,Duval,C.,Weber,P.,Levrat,E.,andFarret,R.,RiskAnalysis
ofComplexSocioTechnicalSystemsbyUsingBayesianNetworkModeling,avail-
able from: http://www.cper-misn-sectaf.cran.uhp-nancy.fr/Files/ACD/p42.pdf.),
November2006.IARAnnualMeeting.
hese barriers need to be integrated into the risk analysis model in order to deter-
mine the impact the barriers have on the system as a whole. Performing a risk
analysis model with the idea of integrating these barriers requires that presently
operating barriers be identified. Next, the model gives light to locations where addi-
tional barriers may be incorporated into the system. In doing so, the model must be
updated to represent the barriers resulting in an explicit representation of a barrier
impact on the entire system.
Of the existing tools to construct the risk analysis model, Bayesian network
models are most applicable to this modeling problem because they allow for the
use of qualitative and/or quantitative models [15]. Bayesian network models lend
themselves easily to generalizing the translation from fault trees and event trees.
With respect to the technical layer, a Bayesian network model is beneficial because
it allows for the representation of reconfigurations of the system, treatment of par-
tially correlated failures, and modeling and propagation of uncertainties in the
model from initiators to the output indicators [15]. With respect to the human/
organizational layer, a Bayesian network model is beneficial because it allows corre-
lations between variables, integration and updating of various kinds of knowledge,
