While the Bayesian network validation framework shows that the oil outflow probabilities can be expected

to be reasonable, there are several uncertainties and biases present in the underlying models. Systematically assessing these is important in terms of the adopted risk perspective, see Section 2.3 and also Oreskes, 1998 stresses the need to acknowledge weaknesses in policy-oriented models. The uncertainty and bias assessment presented in Table 9 is performed qualitatively and can be considered to moderate the strength of the argument put GW572016 forward by the probabilistic oil outflow quantification. Some relevant evidential and outcome uncertainties and biases are listed and scored using

a simple 5-point scale, followed by a brief justification why the model SB203580 solubility dmso element involves uncertainty or bias. Overall, while the underlying models used for the construction of the BN can be taken to provide reasonable approximations of the involved phenomena as discussed above, the presented BN provides a rather conservative estimate of potential oil outflows, conditional to medium evidential uncertainty. The assessment of Table 9 is useful for reflecting which parts of the model to improve using better underlying models to decrease uncertainty and bias. It is seen that improvements to decrease uncertainty are desirable mainly in relation to the applied damage extent model. Considering bias, a more elaborate model for oil spill volume conditional to an inner hull breach could reduce the conservativeness of the model. This shows that the framework presented in Section 3.2 can be applied again as more accurate damage extent and oil outflow models become isothipendyl available. It should however also be appreciated that under the adopted risk perspective of Eq. (4), the whole aim

of risk assessment is to express uncertainty about the possible occurrence of oil spills, being aware of uncertainties and biases related to the model construction. As also other state-of-the-art damage extent models for ship–ship collision involve uncertainties and biases as mentioned in Section 5.1, the presented model can be considered adequate for assessing oil spill risk under the adopted risk perspective. In this paper, a Bayesian network model for the evaluation of accidental cargo oil outflow in ship–ship collisions involving a product tanker has been presented. The main focus of the paper is the presented framework for the construction of this model and assessment of the underlying uncertainties and biases in line with the intended adopted risk perspective in risk assessment of maritime transportation. The probabilistic oil outflow model integrates a damage extent model conditional to impact scenarios with a model for evaluating the oil outflow based on an estimated tank arrangement.