what do you mean “the solomonoff prior is correct”? do you mean that you assign high prior likelihood to theories with low kolmogorov complexity?
this post claims: many people assign high prior likelihood to theories with low time complexity. and this is somewhat rational for them to do if they think that they would otherwise be susceptible to fallacious reasoning.
what do you mean “the solomonoff prior is correct”?
I mean it is so fundamentally correct that it is just how statistical learning works—all statistical learning systems that actually function well approximate bayesian learning (which uses a solomnoff/complexity prior). This includes the brain and modern DL systems, which implement various forms of P(M|E) ~ P(E|M) P(M) - ie they find approximate models which ‘compress’ the data by balancing predictive capability against model complexity.
You could still be doing perfect bayesian reasoning regardless of your prior credences. Bayesian reasoning (at least as I’ve seen the term used) is agnostic about the prior, so there’s nothing defective about assigned a low prior to programs with high time-complexity.
This is true in the abstract, but the physical word seems to be such that difficult computations are done for free in the physical substrate (e.g,. when you throw a ball, this seems to happen instantaneously, rather than having to wait for a lengthy derivation of the path it traces). This suggests a correct bias in favor of low-complexity theories regardless of their computational cost, at least in physics.
what do you mean “the solomonoff prior is correct”? do you mean that you assign high prior likelihood to theories with low kolmogorov complexity?
this post claims: many people assign high prior likelihood to theories with low time complexity. and this is somewhat rational for them to do if they think that they would otherwise be susceptible to fallacious reasoning.
I mean it is so fundamentally correct that it is just how statistical learning works—all statistical learning systems that actually function well approximate bayesian learning (which uses a solomnoff/complexity prior). This includes the brain and modern DL systems, which implement various forms of P(M|E) ~ P(E|M) P(M) - ie they find approximate models which ‘compress’ the data by balancing predictive capability against model complexity.
You could still be doing perfect bayesian reasoning regardless of your prior credences. Bayesian reasoning (at least as I’ve seen the term used) is agnostic about the prior, so there’s nothing defective about assigned a low prior to programs with high time-complexity.
This is true in the abstract, but the physical word seems to be such that difficult computations are done for free in the physical substrate (e.g,. when you throw a ball, this seems to happen instantaneously, rather than having to wait for a lengthy derivation of the path it traces). This suggests a correct bias in favor of low-complexity theories regardless of their computational cost, at least in physics.