By Knightian uncertainty, I mean “the lack of any quantifiable knowledge about some possible occurrence” i.e. you can’t put a probability on it (Wikipedia).
The TL;DR is that Knightian uncertainty is not a useful concept to make decisions, while the use subjective probabilities is: if you are calibrated (which you can be trained to become), then you will be better off taking different decisions on p=1% “Knightian uncertain events” and p=10% “Knightian uncertain events”.
For a more in-depth defense of this position in the context of long-term predictions, where it’s harder to know if calibration training obviously works, see the latest scott alexander post.
Is there a short summary on the rejecting Knightian uncertainty bit?
By Knightian uncertainty, I mean “the lack of any quantifiable knowledge about some possible occurrence” i.e. you can’t put a probability on it (Wikipedia).
The TL;DR is that Knightian uncertainty is not a useful concept to make decisions, while the use subjective probabilities is: if you are calibrated (which you can be trained to become), then you will be better off taking different decisions on p=1% “Knightian uncertain events” and p=10% “Knightian uncertain events”.
For a more in-depth defense of this position in the context of long-term predictions, where it’s harder to know if calibration training obviously works, see the latest scott alexander post.