• Assess the effect of each difference on the risk outcome, using model sensitivity
knowledge and complementary statistical and/or expert knowledge;
• Combine the joint effects of all differences in bias and uncertainty factors, and
compensate the risk value of the model with these bias and uncertainty factors.