I think so. But I'd want to sit down and prove something more rigorously before abandoning the strategy, because there may be times we can get value for free in situations more complicated than this toy example.
Ok this is going to be messy but let me try to convey my hunch for why randomization doesn't seem very useful.- Say I have an intervention that's helpful, and has a baseline 1/4 probability. If I condition on this statement, I get 1 "unit of helpfulness", and a 4x update towards manipulative AGI.- Now let's say I have four interventions like the one above, and I pick one at random. p(O | manipulative) = 1/4, which is the same as baseline, so I get one unit of helpfulness and no update towards manipulative AGI!- BUT, the four interventions have to be mutually exclusive. Which means that if I'd done no simulation at all, I would've gotten my one unit of helpfulness anyway, since the four interventions cover all possible outcomes.- Ok, well, what if my four interventions 1/8 baseline probability each, so only 50% total. Then I pick one at random, p(O | natural) = 1/8, p(O | manipulative) = 1/4, so I get a 2x update towards manipulative AGI. This is the same as if I'd just conditioned on the statement "one of my four interventions happens", and let the randomization happen inside the simulation instead of outside. The total probability of that is 50%, so I get my one unit of helpfulness, at the cost of a 2x update.Maybe the core thing here is a consequence of framing our conditions as giving us bits of search to get lottery outcomes that we like. Rolling the dice to determine what to condition on isn't doing anything different from just using a weaker search condition - it gives up bits of search, and so it has to pay less.
“Just Retarget the Search” directly eliminates the inner alignment problem.
I think deception is still an issue here. A deceptive agent will try to obfuscate its goals, so unless you're willing to assume that our interpretability tools are so good they can't ever be tricked, you have to deal with that.
It's not necessarily a huge issue - hopefully with interpretability tools this good we can spot deception before it gets competent enough to evade our interpretability tools, but it's not just "bada-bing bada-boom" exactly.
Not confident enough to put this as an answer, but
presumably no one could do so at birth
If you intend your question in the broadest possible sense, then I think we do have to presume exactly this. A rock cannot think itself into becoming a mind - if we were truly a blank slate at birth, we would have to remain a blank slate, because a blank slate has no protocols established to process input and become non-blank. Because it's blank.
So how do we start with this miraculous non-blank structure? Evolution. And how do we know our theory of evolution is correct? Ultimately, by trusting our ability to distinguish signal from noise. There's no getting around the "problem" of trapped priors.
Agree that there is no such guarantee. Minor nitpick that the distribution in question is in my mind, not out there in the world - if the world really did have a distribution of muggers' cash that was slower than 1/x, the universe would be comprised almost entirely of muggers' wallets (in expectation).
But even without any guarantee about my mental probability distribution, I think my argument does establish that not every possible EV agent is susceptible to Pascal's Mugging. That suggests that in the search for a formalism of ideal decison-making algorithm, formulations of EV that meet this check are still on the table.
First and most important thing that I want to say here is that fanaticism is sufficient for longtermism, but not necessary. The ">10^36 future lives" thing means that longtermism would be worth pursuing even on fanatically low probabilities - but in fact, the state of things seems much better than that! X-risk is badly neglected, so it seems like a longtermist career should be expected to do much better than reducing X-risk by 10^-30% or whatever the break-even point is.
Second thing is that Pascal's Wager in particular kind of shoots itself in the foot by going infinite rather than merely very large. Since the expected value of any infinite reward is infinity regardless of the probability it's multiplied, there's an informal cancellation argument that basically says "for any heaven I'm promised for doing X and hell for doing Y, there's some other possible deity that offers heaven for Y and hell for X".
Third and final thing - I haven't actually seen this anywhere else, but here's my current solution for Pascal's Mugging. Any EV agent is going to have a probability distribution over how much money the mugger really has to offer. If I'm willing to consider (p>0) any sum, then my probability distribution has to drop off for higher and higher values, so the total integrates to 1. As long as this distribution drops off faster than 1/x as the offer increases, then arbitrarily large offers are overwhelmed by vast implausibility and their EV becomes arbitrarily small.
My best guess at mechanism:
Note that this all still boils down to narrative-stuff. I'm nowhere near the level of zen that it takes to Just Pursue The Goal, with no intermediating narratives or drives based on self-image. I don't think this patch has been particularly moved me towards that either, it's just helpful for where I'm currently at.
When you have a self-image as a productive, hardworking person, the usual Marshmallow Test gets kind of reversed. Normally, there's some unpleasant task you have to do which is beneficial in the long run. But in the Reverse Marshmallow Test, forcing yourself to work too hard makes you feel Good and Virtuous in the short run but leads to burnout in the long run. I think conceptualizing of it this way has been helpful for me.
perhaps this problem can be overcome by including checks for generalization during training, i.e., testing how well the program generalizes to various test distributions.
I don't think this gets at the core difficulty of speed priors not generalizing well. Let's we generate a bunch of lookup-table-ish things according to the speed prior, and then reject all the ones that don't generalize to our testing set. The majority of the models that pass our check are going to be basically the same as the rest, plus whatever modification that causes them to pass with is most probable on the speed prior, i.e. the minimum number of additional entries in the lookup table to pass the training set.
Here's maybe an argument that the generalization/deception tradeoff is unavoidable: according to the Mingard et al. picture of why neural nets generalize, it's basically just that they are cheap approximations of rejection sampling with a simplicity prior. This generalizes by relying on the fact that reality is, in fact, simplicity biased - it's just Ockham's razor. On this picture of things, neural nets generalize exactly to the extent that they approximate a simplicity prior, and so any attempt to sample according to an alternative prior will lose generalization ability as it gets "further" from the True Distribution of the World.
In general, I'm a bit unsure about how much of an interpretability advantage we get from slicing the model up into chunks. If the pieces are trained separately, then we can reason about each part individually based on its training procedure. In the optimistic scenario, this means that the computation happening in the part of the system labeled "world model" is actually something humans would call world modelling. This is definitely helpful for interpretability. But the alternative possibility is that we get one or more mesa-optimizers, which seems less interpretable.