I'm a theoretical CS grad student at Columbia specializing in mechanism design. I write a blog called Unexpected Values which you can find here: https://ericneyman.wordpress.com/. My academic website can be found here: https://sites.google.com/view/ericneyman/.
Thanks for asking! The first thing I want to say is that I got lucky in the following respect. The set of possible outcomes isn't the interior of the ellipse I drew; rather, it is a bunch of points that are drawn at random from a distribution, and when you plot that cloud of points, it looks like an ellipse. The way I got lucky is: one of the draws from this distribution happened to be in the top-right corner. That draw is working at ARC theory, which has just about the most intellectually interesting work in the world (for my interests) and is also just about the most impactful place for me to work (given my skills and my models of what sort of work is impactful). I interned there for 4-5 months and I'll be starting there full-time soon!
Now for my report card, as for how well I checked in (in the ways listed in the post):
I'd say that this looks pretty good.
I do think that there are a couple of yellow flags, though:
I haven't figured out how to navigate this. These may be genuine trade-offs -- a case where I can't both work at ARC and be immune from these downsides -- or maybe I'll learn to deal with the downsides over time. I do think that the benefits of my decision to work at ARC are worth the costs for me, though.
Thanks -- I should have been a bit more careful with my words when I wrote that "measurement noise likely follows a distribution with fatter tails than a log-normal distribution". The distribution I'm describing is your subjective uncertainty over the standard error of your experimental results. That is, you're (perhaps reasonably) modeling your measurement as being the true quality plus some normally distributed noise. But -- normal with what standard deviation? There's an objectively right answer that you'd know if you were omniscient, but you don't, so instead you have a subjective probability distribution over the standard deviation, and that's what I was modeling as log-normal.
I chose the log-normal distribution because it's a natural choice for the distribution of an always-positive quantity. But something more like a power law might've been reasonable too. (In general I think it's not crazy to guess that the standard error of your measurement is proportional to the size of the effect you're trying to measure -- in which case, if your uncertainty over the size of the effect follows a power law, then so would your uncertainty over the standard error.)
(I think that for something as clean as a well-set-up experiment with independent trials of a representative sample of the real world, you can estimate the standard error well, but I think the real world is sufficiently messy that this is rarely the case.)
Let's take the very first scatter plot. Consider the following alternative way of labeling the x and y axes. The y-axis is now the quality of a health intervention, and it consists of two components: short-term effects and long-term effects. You do a really thorough study that perfectly measures the short-term effects, while the long-term effects remain unknown to you. The x-value is what you measured (the short-term effects); the actual quality of the intervention is the x-value plus some unknown, mean zero variance 1 number.
So whereas previously (i.e. in the setting I actually talk about), we have E[measurement | quality] = quality (I'm calling this the frequentist sense of "unbiased"), now we have E[quality | measurement] = measurement (what I call the Bayesian sense of "unbiased").
Great question -- you absolutely need to take that into account! You can only bargain with people who you expect to uphold the bargain. This probably means that when you're bargaining, you should weight "you in other worlds" in proportion to how likely they are to uphold the bargain. This seems really hard to think about and probably ties in with a bunch of complicated questions around decision theory.
This is probably my favorite proposal I've seen so far, thanks!
I'm a little skeptical that warnings from the organization you propose would have been heeded (especially by people who don't have other sources of funding and so relying on FTX was their only option), but perhaps if the organization had sufficient clout, this would have put pressure on FTX to engage in less risky business practices.
I think this fails (1), but more confidently, I'm pretty sure it fails (2). How are you going to keep individuals from taking crypto money? See also: https://forum.effectivealtruism.org/posts/Pz7RdMRouZ5N5w5eE/ea-should-taboo-ea-should
I think my crux with this argument is "actions are taken by individuals". This is true, strictly speaking; but when e.g. a member of U.S. Congress votes on a bill, they're taking an action on behalf of their constituents, and affecting the whole U.S. (and often world) population. I like to ground morality in questions of a political philosophy flavor, such as: "What is the algorithm that we would like legislators to use to decide which legislation to support?". And as I see it, there's no way around answering questions like this one, when decisions have significant trade-offs in terms of which people benefit.
And often these trade-offs need to deal with population ethics. Imagine, as a simplified example, that China is about to deploy an AI that has a 50% chance of killing everyone and a 50% chance of creating a flourishing future of many lives like the one many longtermists like to imagine. The U.S. is considering deploying its own "conservative" AI, which we're pretty confident is safe, and which will prevent any other AGIs from being built but won't do much else (so humans might be destined for a future that looks like a moderately improved version of the present). Should the U.S. deploy this AI? It seems like we need to grapple with population ethics to answer this question.
(And so I also disagree with "I can’t imagine a reasonable scenario in which I would ever have the power to choose between such worlds", insofar as you'll have an effect on what we choose, either by voting or more directly than that.)
Maybe you'd dispute that this is a plausible scenario? I think that's a reasonable position, though my example is meant to point at a cluster of scenarios involving AI development. (Abortion policy is a less fanciful example: I think any opinion on the question built on consequentialist grounds needs to either make an empirical claim about counterfactual worlds with different abortion laws, or else wrestle with difficult questions of population ethics.)
I guess I have two reactions. First, which of the categories are you putting me in? My guess is you want to label me as a mop, but "contribute as little as they reasonably can in exchange" seems an inaccurate description of someone who's strongly considering devoting their career to an EA cause; also I really enjoy talking about the weird "new things" that come up (like idk actually trade between universes during the long reflection).
My second thought is that while your story about social gradients is a plausible one, I have a more straightforward story about who EA should accept which I like more. My story is: EA should accept/reward people in proportion to (or rather, in a monotone increasing fashion of) how much good they do.* For a group that tries to do the most good, this pretty straightforwardly incentivizes doing good! Sure, there are secondary cultural effects to consider-- but I do think they should be thought of as secondary to doing good.
*You can also reward trying to do good to the best of each's ability. I think there's a lot of merit to this approach, but might create some not-great incentives of the form "always looking like you're trying" (regardless of whether you really are trying effectively).
I may have misinterpreted what exactly the concept-shaped hole was. I still think I'm right about them having been surprised, though.