This seems reasonable—it basically makes use of the fact that most statements are wrong, therefore adding a given statement whose truth-value is as-yet-unknown is likely to be wrong.
However, that’s vague. It supports Occam’s Razor pretty well, but does it also offer good evidence that that those likelihoods will manifest in real-world probabilities IN EXACT PROPORTION to the bit-lengths of their inputs? That is a much more precise claim! (For convenience I am ignoring the problem of multiple algorithms where hypotheses have different bit-lengths.)
It supports Occam’s Razor pretty well, but does it also offer good evidence that that those likelihoods will manifest in real-world probabilities IN EXACT PROPORTION to the bit-lengths of their inputs?
Nope, and we have no idea where we’d even start on evaluating this precisely because of the various problems relating to different languages. I think this is an active area of research.
It does seem though, by observation and inference (heh, use whatever tools you have), that more efficient languages tend to formulate shorter hypotheses that tend to hint at this.
There’s also been some demonstrations of how well SI works for learning and inferring about a completely unknown environment. I think this was what AIXI was about, though I can’t recall specifics.
This seems reasonable—it basically makes use of the fact that most statements are wrong, therefore adding a given statement whose truth-value is as-yet-unknown is likely to be wrong.
However, that’s vague. It supports Occam’s Razor pretty well, but does it also offer good evidence that that those likelihoods will manifest in real-world probabilities IN EXACT PROPORTION to the bit-lengths of their inputs? That is a much more precise claim! (For convenience I am ignoring the problem of multiple algorithms where hypotheses have different bit-lengths.)
Nope, and we have no idea where we’d even start on evaluating this precisely because of the various problems relating to different languages. I think this is an active area of research.
It does seem though, by observation and inference (heh, use whatever tools you have), that more efficient languages tend to formulate shorter hypotheses that tend to hint at this.
There’s also been some demonstrations of how well SI works for learning and inferring about a completely unknown environment. I think this was what AIXI was about, though I can’t recall specifics.