Addressing the epistemic uncertainty in maritime accidents modelling using Bayesian network with interval probabilities - Zhang, Guizhen (2019)
Bayesian Network (BN) is often criticized for demanding a large number of crisp/exact/precise conditional probability numbers which, due to the lack of statistics, have to be obtained through experts’ judgment. These exact probability numbers provided by the experts often carry a high level of epistemic uncertainty due to the incompleteness of human knowledge, not to mention the hardness in obtaining them in the first place. The existence of uncertainty in risk modelling was well recognized but seldom discussed. This paper explores the extension of BN with interval probabilities to the modelling of maritime accidents, which allows for the quantification of the epistemic uncertainty. Ship collision is chosen for case study for the strategic importance of navigational safety. The user friendly linguistic terms defined with interval scales were used for elicitation of interval conditional probabilities from industry experts. Inferences were made directly with the interval probabilities with the GL2U algorithm. Meanwhile, the interval probabilities were converted into point probabilities and computed with the traditional BN method for comparison, which were all shown to be within the ranges of the calculated posterior intervals probability. Results with inputs from different experts reveal discrepancies, which in turn verify the existence of uncertainty in risk modelling. A discussion was also provided on how the uncertainty in risk assessment propagates to the decision making process and influences the ranking of potential risk control options.