Previously "Lanrian" on here. Research analyst at Open Philanthropy. Views are my own.
Have you tried using different AI models within perplexity? Any ideas about which one is best? I don't know whether to expect better results from Sonnet 3.5 (within perplexity) or one of the models that perplexity have finetuned themselves, like Sonar Huge.
To be clear, uncertainty about the number of iterations isn’t enough. You need to have positive probability on arbitrarily high numbers of iterations, and never have it be the case that the probability of p(>n rounds) is so much less than p(n rounds) that it’s worth defecting on round n regardless of the effect of your reputation. These are pretty strong assumptions.
So cooperation is crucially dependent on your belief that all the way from 10 rounds to Graham’s number of rounds (and beyond), the probability of >n rounds conditional on n rounds is never lower than e.g. 20% (or whatever number is implied by the pay-off structure of your game).
it sounds to me like ruling this out requires an assumption about the correlations of an action being the same as the correlations of an earlier self-modifying action to enforce that later action.
I would guess that assumption would be sufficient to defeat my counter-example, yeah.
I do think this is a big assumption. Definitely not one that I'd want to generally assume for practical purposes, even if it makes for a nicer theory of decision theory. But it would be super interesting if someone could make a proper defense of it typically being true in practice.
E.g.: Is it really true that a human's decision about whether or not to program a seed AI to take action A has the same correlations as that same superintelligence deciding whether or not to take action A 1000 years later while using a jupiter brain for its computation? Intuitively, I'd say that the human would correlate mostly with other humans and other evolved species, and that the superintelligence would mostly correlate with other superintelligences, and it'd be a big deal if that wasn't true.
However, there is no tiling theorem for UDT that I am aware of, which means we don't know whether UDT is reflectively consistent; it's only a conjecture.
I think this conjecture is probably false for reasons described in this section of "When does EDT seek evidence about correlations?". The section offers an argument for why son-of-EDT isn't UEDT, but I think it generalizes to an argument for why son-of-UEDT isn't UEDT.
Briefly: UEDT-at-timestep-1 is making a different decision than UEDT-at-timestep-0. This means that its decision might be correlated (according to the prior) with some facts which UEDT-at-timestep-0's decision isn't correlated with. From the perspective of UEDT-at-timestep-0, it's bad to let UEDT-at-timestep-1 make decisions on the basis of correlations with things that UEDT-at-timestep-0 can't control.
Notice that learning-UDT implies UDT: an agent eventually behaves as if it were applying UDT with each Pn. Therefore, in particular, it eventually behaves like UDT with prior P0. So (with the exception of some early behavior which might not conform to UDT at all) this is basically UDT with a prior which allows for learning. The prior P0 is required to eventually agree with the recommendations of P1, P2, ... (which also implies that these eventually agree with each other).
I don't understand this argument.
"an agent eventually behaves as if it were applying UDT with each Pn" — why can't an agent skip over some Pn entirely or get stuck on P9 or whatever?
"Therefore, in particular, it eventually behaves like UDT with prior P0." even granting the above — sure, it will beahve like UDT with prior p0 at some point. But then after that it might have some other prior. Why would it stick with P0?
Incidentally: Were the persuasion evals done on models with honesty training or on helpfulness-only models? (Couldn't find this in the paper, sorry if I missed it.)
Tbc: It should be fine to argue against those implications, right? It’s just that, if you grant the implication, then you can’t publicly refute Y.
I also like Paul's idea (which I can't now find the link for) of having labs make specific "underlined statements" to which employees can anonymously add caveats or contradictions that will be publicly displayed alongside the statements
Link: https://sideways-view.com/2018/02/01/honest-organizations/
Maybe interesting: I think a similar double-counting problem would appear naturally if you tried to train an RL agent in a setting where:
In a repeated version of the calculator example (importantly: where in each iteration, you randomly decide whether the people who saw "true" get offered a bet or the people who saw "false" get offered a bet — never both), the RL algorithms would learn that, indeed:
The reason that this happens is because, when the RL agents lose money, there's fewer agents that associate negative reinforcement with having taken a bet just-before. Whereas whenever they gain money, there's more agents that associate positive reinforcement with having taken a bet just-before. So the total amount of reinforcement is greater in the latter case, so the RL agents learn to bet. (Despite how this loses them money on average.)
What is this referring to? Are you thinking about something like: varying small facts about the scenario to get a function from “details of the scenario”->p(escape attempt) and then switch to a scenario with a higher p and then repeat?