I thought I replied to this. I guess I didn't.

Taleb's model seems to miss the distinction between two properties of predictions: discrimination and calibration.

Discrimination is the correctness of the forecast of a single event. Did X happen?

Calibration is the closeness of fit between the predictions made and the distribution of outcomes. Given predictions X1, X2 ... Xn, how closely do the probabilities fit outcomes Y1, Y2 ... Yn?

Even if your calibration is very good, there are always outliers which will upset your model. You didn't discriminate them.