This post is reminiscent of this old one from Daniel

Some thoughts skimming this post generated:

If a catastrophe happens, then either:

1. It happened so discontinuously that we couldn't avoid it even with our concentrated effort
2. It happened slowly but for some reason we didn't make a concentrated effort. This could be because:
   1. We didn't notice it (e.g. intelligence explosion inside lab)
   2. We couldn't coordinate a concentrated effort, even if we all individually would want it to exist (e.g. no way to ensure China isn't racing faster)
   3. We didn't act individually rationally (e.g. Trump doesn't listen to advisors / Trump brainwashed by AI)

1 seems unlikelier by the day.
2a is mostly transparency inside labs (and less importantly into economic developments), which is important but at least some people are thinking about it.
There's a lot to think through in 2b and 2c. It might be critical to ensure early takeoff improves them, rather than degrading them (missing any drastic action to the contrary) until late takeoff can land the final blow.
If we assume enough hierarchical power structures, the situation simplifies into "what 5 world leaders do", and then it's pretty clear you mostly want communication channels and trustless agreements for 2b, and improving national decision-making for 2c.
Maybe what I'd be most excited to see from the "systemic risk" crowd is detailed thinking and exemplification on how assuming enough hierarchical power structures is wrong (that is, the outcome depends strongly on things other than what those 5 world leaders do), what are the most x-risk-worrisome additional dynamics in that area, and how to intervene on them.
(Maybe all this is still too abstract, but it cleared my head)

No, the utility here is just the amount of money $b$ gets

I meant that it sounded like you "wanted a better average score (over as) when you are randomly sampled as b than other programs". Although again I think the intuition-pumping is misleading here because the programmer is choosing which b to fix, but not which a to fix. So whether you wanna one-box only depends on whether you condition on a = b.

(Just skimmed, also congrats on the work)

Why is this surprising? You're basically assuming that there is no correlation between what program Omega predicts and what program you actually are. That is, Omega is no predictor at all! Thus, obviously you two-box, because one-boxing would have no effect on what Omega predicts. (Or maybe the right way to think about this is: it will have a tiny but non-zero effect, because you are one of the |P| programs, but since |P| is huge, that is ~0.)

When instead you condition on a = b, this becomes a different problem: Omega is now a perfect predictor! So you obviously one-box.

Another way to frame this is whether you optimize through the inner program or assume it fixed. From your prior, Omega samples randomly, so you obviously don't want to optimize through it. Not only because |P| is huge, but actually more importantly, because for any policy you might want to implement, there exists a policy that implements the exact opposite (and might be sampled just as likely as you), thus your effect nets out to exactly 0. But once you update on (condition on) seeing that actually Omega sampled exactly you (instead of your random twin, or any other old program), then you'd want to optimize through a! But, you can't have your cake and eat it too. You cannot shape yourself to reap that reward (in the world where Omega samples exactly you), without also shaping yourself to give up some reward (relative to other players) in the world where Omega samples your evil twin.
(Thus you might want to say: Aha! I will make my program behave differently depending on whether it is facing itself or an evil twin! But then... I can also create an evil twin relative to that more complex program. And we keep on like this forever. That is, you cannot actually, fully, ever condition your evil twin away, because you are embedded in Omega's distribution. I think this is structurally equivalent to some commitment race dynamics I discussed with James, but I won't get into that here.)

Secretly, I think this duality only feels counter-intuitive because it's an instance of dynamic inconsistency, = you want to take different actions once you update on information, = the globally (from the uncorrelated prior) optimal action is not always the same as the locally optimal action (from a particular posterior, like the one assuming correlation). Relatedly, I think the only reason your Universal framing differs from your Functional and Anthropic framings is exactly that they are (implicitly) using these two different distributions (one without correlation, the other with):

• The Universal framing assumes that Omega samples randomly (no correlation).
• The Functional framing assumes that "you have control over the inner program". But this is sneaking something in. You obviously have control over your own program. But you don't actually have any control over "the program that Omega will randomly sample from P" (because anything you do is cancelled by your evil twin). Thus, assuming you have control over the inner program, is equivalent to assuming that Omega sampled you. That is, yes correlation.
• The Anthropic framing is also implicitly assuming "Omega samples you", although it's a bit more complicated since it also depends on your utility function (how much you care about different programs getting different amounts of money):
  • If your distribution is truly "Omega samples programs at random", then when you observe two equal numbers, you are almost certain to be in a simulation. Given that, if you for example care equally about all programs getting as much money as possible, then of course you should one-box. That will entail that each random program (with a tiny probability) gets a million, which is a huge win. But the intuition that you were expressing in Question 2 ("p2 is better than p1 because it scores better") isn't compatible with "caring equally about all programs". Instead, it sounds as if you positively want to score better than other programs, that is, maximize your score and minimize theirs! If that's the case, then you obviously should two-box, since almost certainly you are subtracting a million from another program, not yourself. Even assuming, for simplicity, that you only care about beating one other program (as in the p1 and p2 example), you should two-box, because you are subtracting the same million dollars from both, but you are gaining a very slight edge with the thousand dollars.
  • If your distribution is instead "a = b" (assumes correlation), then, regardless of whether you want to maximize everyone's payoff or you want to beat another program that could be sampled, you want to one-box, since the million dollar benefit is coming straight to you, and is bigger than the thousand dollar benefit.

It's unclear what the optimal amount of thinking per step is. My initial guess would have been that letting Claude think for a whole paragraph before each single action (rather than only each 10 actions, or whenever it's in a match, or whatever) scores slightly better than letting it think more (sequentially). But I guess this might work better if it's what the streamer is using after some iteration.

The story for parallel checks could be different though. My guess would be going all out and letting Claude generate the paragraph 5 times and then generate 5 more parallel paragraphs about whether it has gotten something wrong, and then having a lower-context version of Claude decide whether there are any important disagreements, and if not just majority-vote, would improve robustness problems (like "I close a goal before actually achieving it"). But maybe this adds too much bloat and opportunities for mistakes, or makes some mistakes better but others way worse.

I don't see how Take 4 is anything other than simplicity (in the human/computational language). As you say, it's a priori unclear whether a an agent is an instance of a human or the other way around. You say the important bit is that you are subtracting properties from a human to get an agent. But how shall we define subtraction here? In one formal language, the definition of human will indeed be a superset of that of agent... but in another one it will not. So you need to choose a language. And the natural way forward every time this comes up (many times), is to just "weigh by Turing computations in the real world" (instead of choosing a different and odd-looking Universal Turing Machine), that is, a simplicity prior.

Imo rationalists tend to underestimate the arbitrariness involved in choosing a CEV procedure (= moral deliberation in full generality).

Like you, I endorse the step of "scoping the reference class" (along with a thousand other preliminary steps). Preemptively fixing it in place helps you to the extent that the humans wouldn't have done it by default. But if the CEV procedure is governed by a group of humans so selfish/unthoughtful as to not even converge on that by themselves, then I'm sure that there'll be at lesat a few hundred other aspects (both more and less subtle than this one) that you and me obviously endorse, but they will not implement, and will drastically affect the outcome of the whole procedure.
In fact, it seems strikingly plausible that even among EAs, the outcome could depend drastically on seemingly-arbitrary starting conditions (like "whether we use deliberation-and-distillation procedure #194 or #635, which differ in some details"). And "drastically" means that, even though both outcomes still look somewhat kindness-shaped and friendly-shaped, one's optimum is worth <10% to the other's utility (or maybe, this holds for the scope-sensitive parts of their morals, since the scope-insensitive ones are trivial to satisfy).

To pump related intuitions about how difficult and arbitrary moral deliberation can get, I like Demski here.

I'm sure some of people's ignorance of these threat models comes from the reasons. But my intuition is that most of it comes from "these are vaguer threat models that seem very up in the air, and other ones seem more obviously real and more shovel-ready" (this is similar to your "Flinch", but I think more conscious and endorsed).

Thus, I think the best way to converge on whether these threat models are real/likely/actionable is to work through as-detailed-as-possible example trajectories. Someone objects that the state will handle it? Let's actually think through how the state might look like in 5 years! Someone objects that democracy will prevent it? Let's actually think through the actual consequences of cheap cognitive labor in democracy!
This is analogous to what pessimists about single-single alignment have gone through. They have some abstract arguments, people don't buy them, so they start working through them in more detail or provide example failures. I buy some parts of them, but not others. And if you did the same for this threat model, I'm uncertain how much I'd buy!

Of course, the paper might have been your way of doing that. I enjoyed it, but still would have preferred more fully detailed examples, on top of the abstract arguments. You do use examples (both past and hypothetical), but they are more like "small, local examples that embody one of the abstract arguments", rather than "an ambitious (if incomplete) and partly arbitrary picture of how these abstract arguments might actually pan out in practice". And I would like to know the messy details of how you envision these abstract arguments coming into contact with reality. This is why I liked TASRA, and indeed I was more looking forward to an expanded, updated and more detailed version of TASRA.

Fantastic snapshot. I wonder (and worry) whether we'll look back on it with similar feelings as those we have for What 2026 looks like now.

There is also no “last resort war plan” in which the president could break all of the unstable coordination failures and steer the ship.

[...]

There are no clear plans for what to do under most conditions, e.g. there is no clear plan for when and how the military should assume control over this technology.

These sound intuitively unlikely to me, by analogy to nuclear or bio. Of course, that is not to say these protocols will be sufficient or even sane, by analogy to nuclear or bio.

This makes it really unclear what to work on.

It's not super obvious to me that there won't be clever ways to change local incentives / improve coordination, and successful interventions in this direction would seem incredibly high-leveraged, since they're upstream of many of the messy and decentralized failure modes. If they do exist, they probably look not like "a simple cooridnation mechanism", and more like "a particular actor gradually steering high-stakes conversations (through a sequence of clever actions) to bootstrap minimal agreements". Of course, similarity to past geopolitical situations does make it seem unlikely on priors.

There is no time to get to very low-risk worlds anymore. There is only space for risk reduction along the way.

My gut has been in agreement for some time that the most cost-effective x-risk reduction now probably looks like this.