I am not convinced that there exists anything like aleatory uncertainty—even QM uncertainty lies in the map. Having said that I agree with your point: that this doesn’t matter, and value of information is the relevant measure (which is clearly not binary).
Having read your response to Dagon I am now confused—you state that:
This is in contrast to Eliezer’s point that “Uncertainty exists in the map, not in the territory”
but above you only show the orthogonal point that allowing for irresolvable uncertainty can provide useful models, regardless of the existence of such uncertainty. If this is your main point (along with introducing the standard notation used in these models), how is this a contrast with uncertainty being in the map? Lots of good models have elements that can not be found in real life, for example smooth surfaces, right angles or irreducible macroscopic building blocks.
My main point was that it doesn’t matter. Whether the irresolvable uncertainty exists in the territory isn’t a question anyone can answer—I can only talk about my map.
I am not convinced that there exists anything like aleatory uncertainty—even QM uncertainty lies in the map. Having said that I agree with your point: that this doesn’t matter, and value of information is the relevant measure (which is clearly not binary).
Having read your response to Dagon I am now confused—you state that:
but above you only show the orthogonal point that allowing for irresolvable uncertainty can provide useful models, regardless of the existence of such uncertainty. If this is your main point (along with introducing the standard notation used in these models), how is this a contrast with uncertainty being in the map? Lots of good models have elements that can not be found in real life, for example smooth surfaces, right angles or irreducible macroscopic building blocks.
My main point was that it doesn’t matter. Whether the irresolvable uncertainty exists in the territory isn’t a question anyone can answer—I can only talk about my map.