Basically it depends on the source of uncertainty. If all the uncertainty is in the random variable being modeled (as it is in the die example), adding more forecasts (or models) changes nothing—you still have the same uncertainty. However if part of the uncertainty is in the model itself—there is some model error—then you can reduce this model error by combining different (ideally, independent) models.
Imaging a forecast which says: I think A will win, but I’m uncertain so I will say 80% to A and 20% to B. And there is another, different forecast which says the same thing. If you combine the two, your probability of A should be higher than 80%.
Basically it depends on the source of uncertainty. If all the uncertainty is in the random variable being modeled (as it is in the die example), adding more forecasts (or models) changes nothing—you still have the same uncertainty. However if part of the uncertainty is in the model itself—there is some model error—then you can reduce this model error by combining different (ideally, independent) models.
Imaging a forecast which says: I think A will win, but I’m uncertain so I will say 80% to A and 20% to B. And there is another, different forecast which says the same thing. If you combine the two, your probability of A should be higher than 80%.