Always correct your probability estimates for the possibility that you’ve made an incorrect assumption.
I think that’s good too—Jaynes advocated including a “something else that I didn’t think of” hypothesis to your hypothesis to avoid accepting something strongly when all you’ve done is eliminate the alternatives you’ve considered.
I don’t know about this being a category error though. I think “map 1 is accurate with respect to X” is a valid proposition
“Is accurate” isn’t much of a proposition in itself, as it leaves out the level of accuracy.
Probability of a proposition. Propositions are true or false.
Level of accuracy of a model. Models are more or less accurate.
Maybe “Is accurate enough that it doesn’t change our answer by an unacceptable amount”? The level of accuracy we want depends on context.
How would you measure the accuracy of a model, other than by its probability of giving accurate answers? “Accurate” depends on what margin of error you accept, or you can define it with increasing penalties for increased divergence from reality.
I think that’s good too—Jaynes advocated including a “something else that I didn’t think of” hypothesis to your hypothesis to avoid accepting something strongly when all you’ve done is eliminate the alternatives you’ve considered.
“Is accurate” isn’t much of a proposition in itself, as it leaves out the level of accuracy.
Probability of a proposition. Propositions are true or false. Level of accuracy of a model. Models are more or less accurate.
Maybe “Is accurate enough that it doesn’t change our answer by an unacceptable amount”? The level of accuracy we want depends on context.
How would you measure the accuracy of a model, other than by its probability of giving accurate answers? “Accurate” depends on what margin of error you accept, or you can define it with increasing penalties for increased divergence from reality.