It is the most common way of trying to cope with novelty: by means of metaphors and analogies we try to link the new to the old, the novel to the familiar. Under sufficiently slow and gradual change, it works reasonably well; in the case of a sharp discontinuity, however, the method breaks down: though we may glorify it with the name "common sense", our past experience is no longer relevant, the analogies become too shallow, and the metaphors become more misleading than illuminating. This is the situation that is characteristic for the "radical" novelty.—E.W. Dijkstra, On the cruelty of really teaching computer science
A black swan is a high-impact event that is hard to predict (but not necessarily of low probability). Also, an event that is not accounted for in a model, and therefore causes the model to break down when it occurs.
Considering some event a black swan doesn't give a leave to not assign any probabilities, since making decisions depending on the plausibility of such event is still equivalent to assigning probabilities that make the expected utility calculation give those decisions.