Embedded agency & computational irreducibility implies that the smaller map cannot outpace the full time evolution of the territory because it is a part of it, which may or may not be important for real world agents.
In the case where the response time of the map does matter to some extent, embedded maps often need to coarse grain over the territory to “locally” outpace the territory
We may think of natural latents as coarse grainings that are convergent for a wide variety of embedded maps
the smaller map cannot outpace the full time evolution of the territory
A program can predict another program regardless of when either of them is instantiated in the territory (neither needs to be instantiated for this to work, or they could be instantiated at many times simultaneously). Statistical difficulties need to be set up more explicitly, there are many ways of escaping them in principle (by changing the kind of territory we are talking about), or even in practice (by focusing on abstract behavior of computers).
The claim was that a subprogram (map) embedded within a program(territory) cannot predict the *entire execution trace of that program faster than the program itself given computational irreducibility
“there are many ways of escaping them in principle, or even in practice (by focusing on abstract behavior of computers).”
Yes, I think this is the same point as my point about coarse graining (outpacing the territory “locally” by throwing away some info)
My point about computers-in-practice is that this is no longer an issue within the computers, indefinitely. You can outpace the territory within a computer using a smaller map from within the computer. Whatever “computational irreducibility” is, the argument doesn’t apply for many computations that can be set up in practice, that is they can be predicted by smaller parts of themselves. (Solar flares from distant future can’t be predicted, but even that is not necessarily an important kind of practical question in the real world, after the universe is overwritten with computronium, and all the stars are dismantled to improve energy efficiency.)
The point was exactly that although we can’t outpace the territory globally, we can still do it locally(by throwing out info we don’t care about like solar flares)
That by itself is not that interesting. The interesting part is given that different embedded maps throw out different info & retain some info, is there any info that’s convergently retained by a wide variety of maps? (aka natural latents)
The rest of the disagreement seems to boil down to terminology
The locally/globally distinction is suspicious, since “locally” here can persist at an arbitrary scale. If all the different embedded maps live within the same large legible computation, statistical arguments that apply to the present-day physical world will fail to clarify the dynamics of their interaction.
Yes, by “locally outpace” I simply meant outpace at some non-global scale, there will of course be some tighter upper bound for that scale when it comes to real world agents
What I’m saying is that there is no upper bound for real world agents, the scale of “locally” in this weird sense can be measured in eons and galaxies.
Yes, there’s no upper bound for what counts as “local” (except global), but there is an upper bound for the scale at which agents’ predictions can outpace the territory (eg humans can’t predict everything in the galaxy)
The relevance of extracting/formulating something “local” is that prediction by smaller maps within it remains possible, ignoring the “global” solar flares and such. So that is a situation that could be set up so that a smaller agent predicts everything eons in the future at galaxy scale. Perhaps a superintelligence predicts human process of reflection, that is it’s capable of perfectly answering specific queries before the specific referenced event would take place in actuality, while the computer is used to run many independent possibilities in parallel. So the superintelligence couldn’t enumerate them all in advance, but it could quickly chase and overtake any given one of them.
Even a human would be capable of answering such questions if nothing at all is happening within this galaxy scale computer, and the human is paused for eons after making the prediction that nothing will be happening. (I don’t see what further “first sense” of locality or upper bound that is distinct from this could be relevant.)
I intended ‘local’ (aka not global) to be a necessary but not sufficient condition for predictions made by smaller maps within it to be possible (cuz global predictions runs into problems of embedded agency)
I’m mostly agnostic about what the other necessary conditions are & what the sufficient conditions are
I agree, just changed the wording of that part
Embedded agency & computational irreducibility implies that the smaller map cannot outpace the full time evolution of the territory because it is a part of it, which may or may not be important for real world agents.
In the case where the response time of the map does matter to some extent, embedded maps often need to coarse grain over the territory to “locally” outpace the territory
We may think of natural latents as coarse grainings that are convergent for a wide variety of embedded maps
A program can predict another program regardless of when either of them is instantiated in the territory (neither needs to be instantiated for this to work, or they could be instantiated at many times simultaneously). Statistical difficulties need to be set up more explicitly, there are many ways of escaping them in principle (by changing the kind of territory we are talking about), or even in practice (by focusing on abstract behavior of computers).
The claim was that a subprogram (map) embedded within a program(territory) cannot predict the *entire execution trace of that program faster than the program itself given computational irreducibility
“there are many ways of escaping them in principle, or even in practice (by focusing on abstract behavior of computers).”
Yes, I think this is the same point as my point about coarse graining (outpacing the territory “locally” by throwing away some info)
My point about computers-in-practice is that this is no longer an issue within the computers, indefinitely. You can outpace the territory within a computer using a smaller map from within the computer. Whatever “computational irreducibility” is, the argument doesn’t apply for many computations that can be set up in practice, that is they can be predicted by smaller parts of themselves. (Solar flares from distant future can’t be predicted, but even that is not necessarily an important kind of practical question in the real world, after the universe is overwritten with computronium, and all the stars are dismantled to improve energy efficiency.)
I don’t think we disagree?
The point was exactly that although we can’t outpace the territory globally, we can still do it locally(by throwing out info we don’t care about like solar flares)
That by itself is not that interesting. The interesting part is given that different embedded maps throw out different info & retain some info, is there any info that’s convergently retained by a wide variety of maps? (aka natural latents)
The rest of the disagreement seems to boil down to terminology
The locally/globally distinction is suspicious, since “locally” here can persist at an arbitrary scale. If all the different embedded maps live within the same large legible computation, statistical arguments that apply to the present-day physical world will fail to clarify the dynamics of their interaction.
Yes, by “locally outpace” I simply meant outpace at some non-global scale, there will of course be some tighter upper bound for that scale when it comes to real world agents
What I’m saying is that there is no upper bound for real world agents, the scale of “locally” in this weird sense can be measured in eons and galaxies.
Yes, there’s no upper bound for what counts as “local” (except global), but there is an upper bound for the scale at which agents’ predictions can outpace the territory (eg humans can’t predict everything in the galaxy)
I meant upper bound in the second sense
The relevance of extracting/formulating something “local” is that prediction by smaller maps within it remains possible, ignoring the “global” solar flares and such. So that is a situation that could be set up so that a smaller agent predicts everything eons in the future at galaxy scale. Perhaps a superintelligence predicts human process of reflection, that is it’s capable of perfectly answering specific queries before the specific referenced event would take place in actuality, while the computer is used to run many independent possibilities in parallel. So the superintelligence couldn’t enumerate them all in advance, but it could quickly chase and overtake any given one of them.
Even a human would be capable of answering such questions if nothing at all is happening within this galaxy scale computer, and the human is paused for eons after making the prediction that nothing will be happening. (I don’t see what further “first sense” of locality or upper bound that is distinct from this could be relevant.)
I intended ‘local’ (aka not global) to be a necessary but not sufficient condition for predictions made by smaller maps within it to be possible (cuz global predictions runs into problems of embedded agency)
I’m mostly agnostic about what the other necessary conditions are & what the sufficient conditions are