(Egan’s Incandescence is relevant and worth checking out—though it’s not exactly thrilling :))
I’m not crazy about the terminology here:
Unfalsifiable-in-principle doesn’t imply false. It implies that there’s a sense in which the claim is empty. This tends to imply [it will not be accepted as science], but not [it is false].
Where something is practically unfalsifiable (but falsifiable in principle), that doesn’t suggest it’s false either. It suggests it’s hard to check.
It seems to me that the thing you’d want to point to as potentially suspicious is [practically unfalsifiable claim made with high confidence].
The fact that it’s unusual and inconvenient for something predictable to be practically unfalsifiable does not inherently make such prediction unsound.
I don’t think it’s foolish to look for analogous examples here, but I guess it’d make more sense to make the case directly:
No, a hypothesis does not always need to make advance predictions (though it’s convenient when it does!).
Claims predicting AI disaster are based on our notunderstanding how things will work concretely. Being unable to make many good predictions in this context is not strange.
Various AI x-risk claims concern patterns with no precedents we’d observe significantly before the end. This, again, is inconvenient—but not strange: they’re dangerous in large part because they’re patterns without predictable early warning signs.
I agree with all of this. (And good point about the high confidence aspect.)
The only thing that I would frame slightly differently is that: [X is unfalsifiable] indeed doesn’t imply [X is false] in the logical sense. On reflection, I think a better phrasing of the original question would have been something like: ‘When is “unfalsifiability of X is evidence against X” incorrect?’. And this amended version often makes sense as a heuristic—as a defense against motivated reasoning, conspiracy theories, etc. (Unfortunately, many scientists seem to take this too far, and view “unfalsifiable” as a reason to stop paying attention, even though they would grant the general claim that [unfalsifiable] doesn’t logically imply [false].)
I don’t think it’s foolish to look for analogous examples here, but I guess it’d make more sense to make the case directly.
That was my main plan. I was just hoping to accompany that direct case by a class of examples that build intuition and bring the point home to the audience.
When is “unfalsifiability of X is evidence against X” incorrect?′
In some sense this must be at least half the time, because if X is unfalsifiable, then not-X is also unfalsifiable, and it makes little sense to have this rule constitute evidence against X and also evidence against not-X.
I would generally say that falsifiability doesn’t imply anything about truth value. It’s more like “this is a hypothesis that scientific investigation can’t make progress on”. Also, it’s probably worth tracking the category of “hypotheses that you haven’t figured out how to test empirically, but you haven’t thought very hard about it yet”.
There may be useful heuristics about people who make unfalsifiable claims. Some of which are probably pretty context-dependent.
(Egan’s Incandescence is relevant and worth checking out—though it’s not exactly thrilling :))
I’m not crazy about the terminology here:
Unfalsifiable-in-principle doesn’t imply false. It implies that there’s a sense in which the claim is empty. This tends to imply [it will not be accepted as science], but not [it is false].
Where something is practically unfalsifiable (but falsifiable in principle), that doesn’t suggest it’s false either. It suggests it’s hard to check.
It seems to me that the thing you’d want to point to as potentially suspicious is [practically unfalsifiable claim made with high confidence].
The fact that it’s unusual and inconvenient for something predictable to be practically unfalsifiable does not inherently make such prediction unsound.
I don’t think it’s foolish to look for analogous examples here, but I guess it’d make more sense to make the case directly:
No, a hypothesis does not always need to make advance predictions (though it’s convenient when it does!).
Claims predicting AI disaster are based on our not understanding how things will work concretely. Being unable to make many good predictions in this context is not strange.
Various AI x-risk claims concern patterns with no precedents we’d observe significantly before the end. This, again, is inconvenient—but not strange: they’re dangerous in large part because they’re patterns without predictable early warning signs.
I agree with all of this. (And good point about the high confidence aspect.)
The only thing that I would frame slightly differently is that:
[X is unfalsifiable] indeed doesn’t imply [X is false] in the logical sense. On reflection, I think a better phrasing of the original question would have been something like: ‘When is “unfalsifiability of X is evidence against X” incorrect?’. And this amended version often makes sense as a heuristic—as a defense against motivated reasoning, conspiracy theories, etc. (Unfortunately, many scientists seem to take this too far, and view “unfalsifiable” as a reason to stop paying attention, even though they would grant the general claim that [unfalsifiable] doesn’t logically imply [false].)
That was my main plan. I was just hoping to accompany that direct case by a class of examples that build intuition and bring the point home to the audience.
In some sense this must be at least half the time, because if X is unfalsifiable, then not-X is also unfalsifiable, and it makes little sense to have this rule constitute evidence against X and also evidence against not-X.
I would generally say that falsifiability doesn’t imply anything about truth value. It’s more like “this is a hypothesis that scientific investigation can’t make progress on”. Also, it’s probably worth tracking the category of “hypotheses that you haven’t figured out how to test empirically, but you haven’t thought very hard about it yet”.
There may be useful heuristics about people who make unfalsifiable claims. Some of which are probably pretty context-dependent.