If I were going to go further with this idea, I’d even queer the map-territory dichotomy and recognize that the map-territory distinction can be illusory sometimes.
In what way? I find myself disagreeing vehemently, so I would appreciate an example.
Maps are territory in the sense that the territory is the substrate on which minds with maps run, but one of my main points here is that our experience is all map, and I don’t think any human has ever had a map which remotely resembles the substrate on which we all run.
I’m making a general comment, but yes what I mean is that in some idealized cases, you can model the territory under consideration well enough to make the map-territory distinction illusory.
Of course, this requires a lot, lot more compute than we usually have.
I think I see what you’re saying, let me try to restate it:
If the result you are predicting is course-grained enough, then there exist models which give a single prediction with probability so close to one that you might as well just take the model as truth.
Yes, and as a contrapositive, if you had enough computing power, you could narrow down the set of models to 1 for even arbitrarily fine-grained predictions.
If I were going to go further with this idea, I’d even queer the map-territory dichotomy and recognize that the map-territory distinction can be illusory sometimes.
In what way? I find myself disagreeing vehemently, so I would appreciate an example.
Maps are territory in the sense that the territory is the substrate on which minds with maps run, but one of my main points here is that our experience is all map, and I don’t think any human has ever had a map which remotely resembles the substrate on which we all run.
I’m making a general comment, but yes what I mean is that in some idealized cases, you can model the territory under consideration well enough to make the map-territory distinction illusory.
Of course, this requires a lot, lot more compute than we usually have.
I think I see what you’re saying, let me try to restate it:
If the result you are predicting is course-grained enough, then there exist models which give a single prediction with probability so close to one that you might as well just take the model as truth.
Yes, and as a contrapositive, if you had enough computing power, you could narrow down the set of models to 1 for even arbitrarily fine-grained predictions.