Just a passing though here. Is probability really the correct term? I wonder if what we do in these types of cases is more an assessment of our confidence in our ability to extrapolate from past experience into new, and often completely different, situations.
If so that is really not a probability about the event we’re thinking about—though perhaps is could be seen as one about our ability to make “wild” guesses (and yes, that is hyperbole) about stuff we don’t really know anything about. Event there I’m not sure probability is the correct term.
With regard to the supernatural things, that tends to be something of a hot button to a lot of people I think. Perhaps a better casting would be things we have some faith in—which tend to be things we must infer rather than have any real evidence providing some proof. I think these change over time—we’ve had faith in a number of theories that have been later proven—electrons for example or other sub atomic particles.
But then what about dark matter and energy? The models seem to say we need that but as yet we cannot find it. So we have faith in the model and look to prove that faith was justified by finding the dark stuff. But one might as why we have that faith rather than being skeptical of the model, even while acknowledging it has proven of value and helped expand knowledge. I think we have better discussion about faith in this context (perhaps) that if we get into religion and supernatural subjects (though arguably we should treat them the same as the faith we have in other models to my view).
Going from probability of anticipated experience to more aggregated, hard-to-resolve probabilities about modeled groupings of experiences (or non-experiences) is not clearly required for anything, but is more of a compression of models, because you can’t actually predict things at the detailed level the universe runs on.
So the map/territory distinction seems VITAL here. Probability is in the map. Models are maps. There’s no similarity molecules or probability fields that tie all die rolls together. It’s just that our models are easier (and still work fairly well) if we treat them similarly because, at the level we’re considering, they share some abstract properties in our models.
Ah, these two comments, and that of G Gordon Worley III, have made me realise that I didn’t at all make explicit that I was taking the Bayesian interpretation of probability as a starting assumption. See my reply to G Gordon Worley III for more on that, and the basic intention of this post (which I’ve now edited to make it clearer).
(See my other comments for what I meant by probability)
I don’t know much about dark matter and energy, but I’d say they’re relatively much less challenging cases. I take it that whether they exist or not should already affect the world in observable ways, and also that we don’t have fundamental reasons to expect we could never get more “direct observations” of their existence? I could be wrong about that, but if that’s right, then that’s just something in the massive category of “Things that are very hard to get evidence about”, rather than “Things that might, by their very nature, never provide any evidence of their existence or lack of existence.” I’d say that’s way closer to the AGI case than to the “a god that will literally never interact with the natural world in any way” case. So it seems pretty clear to me that it can be handled with something like regular methods.
My intention was to find a particularly challenging case for arriving at, and making sense of, subjective probabilities, so I wanted to build up to claims where whether they’re true or not would never have any impact at all on the world. (And this just happens to end up involving things like religion and magic—it’s not that I wanted to cover a hot button topic on purpose, or debate religion, but rather I wanted to debate how to arrive at and make sense of probabilities in challenging cases.)
Just a passing though here. Is probability really the correct term? I wonder if what we do in these types of cases is more an assessment of our confidence in our ability to extrapolate from past experience into new, and often completely different, situations.
If so that is really not a probability about the event we’re thinking about—though perhaps is could be seen as one about our ability to make “wild” guesses (and yes, that is hyperbole) about stuff we don’t really know anything about. Event there I’m not sure probability is the correct term.
With regard to the supernatural things, that tends to be something of a hot button to a lot of people I think. Perhaps a better casting would be things we have some faith in—which tend to be things we must infer rather than have any real evidence providing some proof. I think these change over time—we’ve had faith in a number of theories that have been later proven—electrons for example or other sub atomic particles.
But then what about dark matter and energy? The models seem to say we need that but as yet we cannot find it. So we have faith in the model and look to prove that faith was justified by finding the dark stuff. But one might as why we have that faith rather than being skeptical of the model, even while acknowledging it has proven of value and helped expand knowledge. I think we have better discussion about faith in this context (perhaps) that if we get into religion and supernatural subjects (though arguably we should treat them the same as the faith we have in other models to my view).
Yeah, this seems like we’re using “probability” to mean different things.
Probabilities are unavoidable in any rational decision theory. There is no alternative to assigning probabilities to expected experiences conditional on potential actions. https://www.lesswrong.com/posts/a7n8GdKiAZRX86T5A/making-beliefs-pay-rent-in-anticipated-experiences .
Going from probability of anticipated experience to more aggregated, hard-to-resolve probabilities about modeled groupings of experiences (or non-experiences) is not clearly required for anything, but is more of a compression of models, because you can’t actually predict things at the detailed level the universe runs on.
So the map/territory distinction seems VITAL here. Probability is in the map. Models are maps. There’s no similarity molecules or probability fields that tie all die rolls together. It’s just that our models are easier (and still work fairly well) if we treat them similarly because, at the level we’re considering, they share some abstract properties in our models.
Ah, these two comments, and that of G Gordon Worley III, have made me realise that I didn’t at all make explicit that I was taking the Bayesian interpretation of probability as a starting assumption. See my reply to G Gordon Worley III for more on that, and the basic intention of this post (which I’ve now edited to make it clearer).
(See my other comments for what I meant by probability)
I don’t know much about dark matter and energy, but I’d say they’re relatively much less challenging cases. I take it that whether they exist or not should already affect the world in observable ways, and also that we don’t have fundamental reasons to expect we could never get more “direct observations” of their existence? I could be wrong about that, but if that’s right, then that’s just something in the massive category of “Things that are very hard to get evidence about”, rather than “Things that might, by their very nature, never provide any evidence of their existence or lack of existence.” I’d say that’s way closer to the AGI case than to the “a god that will literally never interact with the natural world in any way” case. So it seems pretty clear to me that it can be handled with something like regular methods.
My intention was to find a particularly challenging case for arriving at, and making sense of, subjective probabilities, so I wanted to build up to claims where whether they’re true or not would never have any impact at all on the world. (And this just happens to end up involving things like religion and magic—it’s not that I wanted to cover a hot button topic on purpose, or debate religion, but rather I wanted to debate how to arrive at and make sense of probabilities in challenging cases.)