How do we most efficiently allocate limited resources to do the most good?

This has several advantages in the current regime over "what is the most cost-effective way to help people?":

1. "Cost-effective" can in theory be inclusive of all costs, but in practice the framing points people too quickly to thinking of financial costs, whereas plausibly we should worry about other costs more (most obviously EA human capital, but also plausibly stuff like branding, connections, human capital of adjacent people, etc).
2. "most cost-effective way" implies a bit of a single-player game, whereas maybe group coordination and total allocation is a more important framing.
3. "helps people" may run into issues with population ethics, not sure.

But I recognize this is still mostly a refinement of "what is the most cost-effective way to help people?" and maybe it's better to think of a more radically different question, as the prompt and some of the other answers may have suggested.

This doesn't exactly answer your question, but it answers your prompt to babble, and substituting "what is the new EA question" with "what have I been thinking about recently that sounds similar?" seems like a nice babble. So: "How do we do the most good" could now be:

• "How do we get expected value calculations in practice?"
• "Once we have expected value calculations in place, what is the highest expected value thing to do?"
• "What do we do in the meantime?"

If we start out trying to optimize money directed to global health, we can sort of get expected value calculations, by doing the hard research that GiveWell does, and hoping that nothing too suboptimal happens if we take their expected life saved numbers literally, or at least if our decision is to fund charities in decreasing order of estimated cost-effectiveness.

But once we accept that we could get higher expected impact by moving to more speculative cause areas, the number of different actions we can take becomes really large, and the way to choose between them becomes much more fuzzy, and relies much more on human judgment.

Like, it's not like we can tell that "going into AI alignment improves all that is of value by 0.0001% (in expectation), whereas going into EA movement building improves all that is of value by 0.003% (in expectation), so you should definitely try both, see if the difference in fit is more than 30x, and if not, choose the second."

As aside, I think that the central, or meaningful split in EA right now is not exactly between near- and long- termists. It's rather between looking for, in a sense, more certain promises of impact, and being willing to take more uncertain gambles if we think that they're worth it in expectation. But that seems mostly distinct from inter-temporal preferences.

I mostly believe that being willing to take speculative gambles is the correct thing to do. But this doesn't map neatly to the near/longtermist split. E.g., the work of OPIS on reducing the most intense forms of suffering today (e.g., cluster headaches using psilocybin) feels somewhat out-there in terms of speculativeness, but it is also pretty near-termist. Charity Entrepreneurship, and in particular creating a charity through them, rather than donating, might also be another good example of something which is both speculative and near-term.

So anyways, once we lose the ability to rely on GiveWell, which optimizes global health and development donations, because we just want to generally optimize actions, we can have a few types of answers: 

• Look for the best alternative processes
• Try to recover expected value calculations somehow

Both of these are tricky. I'm working on the second. I don't expect total success, and partial success seems like it would still require much more human judgment than GiveWell. 

I don't think that not having EV calculations means that "anything goes because we don't have expected value calculations". For instance, we can do sanity checks to determine that one option generally dominates another, and as we get better models of the world, we can do more such sanity checks.

So one answer to "What keeps us morally and epistemically honest?" can be "evaluations". Evaluations that cannot approach GiveWell in all their glory, but which can still point out missing or invalid parts of a pathway to impact, or options that are dominated by others, and still be really biting.

This feels like it is a somewhat personal answer. It reframes EA as kind of a research project (I'm a researcher), and paints quantitative methods as one possible solution to that research project (I like quantitative methods.) I'm curious to compare it to what others say.

What's on the critical path to doing good?

The reason explicit cost-effectiveness framings should be deemphasized is that money is not the primary bottleneck-- except in global health, there is enough money that it's difficult to determine the cost-effectiveness bar because we don't have all the ideas or megaproject infrastructure required to spend cost-effectively. Naturally, the critical path question emphasizes two of the current bottlenecks: time and project management. Time until AGI is now a critical resource, as is time until biotech capabilities, until we cure malaria anyway, and so on. Coordination is the other scarce resource, and project management is the correct framing for solving coordination issues in many (but not all) cases. 

If your primary goal is reducing x-risk, you could ask "what's on the critical path to saving the world?" which I think is slightly better.

Maybe a slight shift from “what is the most cost-effective way to help people?” to “what are cost-effective ways to help people at scale?”. I think we can set a bar of ~8x GiveDirectly and then aim to fund everything that meets that bar. I write about this in "In current EA, scalability matters".

I don't think that longtermism has changed the target: expected marginal utility per dollar, aka cost-effectiveness. The difficulty with applying this to interventions with long-term effects is measuring those effects. I think it's a mistake to throw out the ITN framework; instead, we should try harder to measure effects.

There's also the difficulty of putting everything on the same scale, eg. animal and human lives, or present and future lives.

How can we best allocate our limited resources to improve the world? Sub-question: Which resources are worth the effort to optimise the allocation of, and which are not, given that we all have limited time, effort and willpower?

I find this framing most helpful. In particular, for young people, the most valuable resource they have is their future labor. Initially, converting this to money and the money to donations was very effective, but now this is often outcompeted by working directly on high priority paths. But the underlying question remains. And I'd argue we often reach the point where optimising our use of money, as it manifests as frugality and thrift, is not worth the willpower and opportunity costs, given that there's a lot more money than vetting capacity or labor. (Implicit assumption: thrift has cost and is the non default option. This feels true for me but may not generalise)

[A quick babble based on your premise]

What are the best bets to take to fill the galaxies with meaningful value?

How can I personally contribute to the project of filling the universe with value, given other actors’ expected work and funding on the project?

What are the best expected-value strategies for influencing highly pivotal (eg galaxy-affecting) lock-in events?

What are the tractable ways of affecting the longterm trajectory of civilisation? Of those, which are the most labour-efficient?

How can we use our life’s work to guide the galaxies to better trajectories?

Themes I notice

• Thinking in bets feels helpful epistemically, though the lack of feedback loops is annoying
• The object of attention is something like ‘civilisation’, ‘our lightcone’, or ‘our local galaxies’
• The key constraint isn’t money, but it’s less obvious what it is (just ‘labour’ or ‘careers’ doesn’t feel quite right)