WOW, this is a rude comment. Good grief.

No, they thought it was controversial, but I took on board many suggestions to soften it.

Separately if it’s a useful data point, reading these comments made me not wish to engage with this piece at the object level, at least at the moment.

So in your view I’m being defensive/​unpleasant in the comments? Or at least significantly more so?

Thank you for sharing your experience, and I’m really sorry you had to experience this.

Writing the below not as a response to you, but for other readers.

(1) each instance felt minor, (2) it felt like reporting would be more trouble than it was worth (time-consuming, burdensome), and (3) I want to maintain relationships with some of these people. [...] (4) it was not clear to me (at the time, and in some cases, even now) that certain behaviors were inappropriate

I recently reported something to the community health team and it was surprisingly straightforward, they have a form here: https://​​www.centreforeffectivealtruism.org/​​community-health/​​contact-us with a text field and everything else is optional

CEA has been investing between 5 and 7 FTEs (including fairly senior ones) for years in community health, which is several millions of dollars in salaries and opportunity costs, so my sense is that they really value these reports. The page starts with:

Please have a low bar for getting in touch. We welcome small pieces of information, even if you’re not sure they warrant us taking action. Often people are worried that their issue is too minor to tell us, but we’re glad they shared it. We can likely still be helpful, and it’s useful information that could help us spot patterns of problems in the community.”

That said, for me it was possible to report things anonymously, I realize that’s not always the case.

1-on-1s where the other person seemed to only want to talk about personal matters

I also had this happen several times, and while it was never flirtatious or anything close to harassment, the first time I found it really frustrating, and I wish there was a stronger norm about being clear if you’re requesting a social 1-1 instead of a professional/​impact-focused 1-1

Hey Nick, I don’t know if we are that far apart on the conceptual issue of potential bias but I think we are approaching this differently.

However, I really like the Stieglitz and Friedman analogy and think it is useful in many ways. Simply, there are lots of decision points here. And I really wish there were some other groups working on building such work so we could compare and contrast our work with theirs like you can in that context. If that were so, I think this type of meta-debate would be either less likely to occur, or more productive because we could point to more specific things.

At the same time, I think “count up the cause area backgrounds of the staff who worked on this” is misleading even if I agreed with the characterization of our staff, and I don’t. This is for a number of reasons and, if you’d permit, I’ll largely extend the economics and social science analogy to raise my points.

First, in some simple sense, I think looking at our staff’s background like this is like looking at the subfields GiveWell’s staff worked in prior to joining and if a disproportionate number worked on malaria using that to argue this is evidence of potential bias for why multiple of their top donation opportunities overall involve malaria. It’s not that this bias is not possible, it’s just a very blunt guide, at best, and the causal arrow may point the other way. That is, GiveWell may employ a lot of people who have backgrounds in malaria to help create their final estimates because they are particularly well suited to the task and/​or malaria is an important area they have to cover.

Second, for this model many of the relevant choice points come from fields that have less ideological valence on the cause area lines (i.e. what do you think of risk attitudes) and potential bias in those areas is likely just as relevant to reaching conclusions as thinking at the level of cause area. Further, some inputs have effects difficult to pin down in the overall model, which limits the ability of people to even implicitly put their thumb on the scale because they don’t know what changing that input would change in the result. There are a few areas where it is obvious (i.e. make the animal moral weights higher or lower, reduce the cost-effectiveness of a given area) but those are the areas precisely where anyone looking at our model can see what we choose and object if they disagree.

But suppose you were convinced that Stiglitz tends to be biased in a liberal direction, and Friedman in a conservative one. What’s the best way to demonstrate that? I think it would often be to point to a specific assumption or choice they made that is questionable. This is why I asked you to point to something specific that you think is wrong. Not because I think it can’t be the case that we’re biased or that it’s inherently illegitimate to bring up the possibility, but because it’s the specifics that demonstrate that we are biased. I really do agree that too often some people in EA think everything they do is objective. I wrote this just last month and stand behind this as applying to basically everyone:

As Keynes observed about a parallel dynamic in economics: “practical men, who believe themselves to be quite exempt from any intellectual influences, are usually the slaves of some defunct economist.” The same applies here. Views that people describe as mere intuitions or common sense are often downstream of philosophical ideas promoted in their environments. When someone thinks such intuitions and implicit stances are indisputable or obviously correct, it’s more often than not evidence they haven’t examined or tested those ideas at all.

If someone doesn’t have the time to investigate an area, it can be at times reasonable to check for this kind of bias and discount appropriately (I think this is most useful as a guide when a group or individual has shown repeated bias in the past) but I also think this can at times serve as a shortcut to dismissing anything that doesn’t already ideologically align with yourself. Balancing these two competing forces can be a constant struggle, particularly in areas outside of your expertise and I know it’s something constantly in my mind when I read about politics.

Finally, to continue the analogy with economics, often the field of experts are debating a much more narrow range of opinions than exists in the public debate. For example, in immigration the economist debate about the impact of immigration on wages and employment is far more narrow than the general public discussion of the same topic. Stiglitz and Friedman (assuming you mean Milton Friedman) might have disagreed a lot but it was often within a largely shared framework. I’m pretty sure neither of them thought mass central planning would work, or that tariffs were largely paid by the exporting countries. I think many of the opinions people give about cause prioritization may fall into this category of something very few who think about the topic carefully dare to defend but, because those outside haven’t put in the time, they are unaware of this (in some sense through no fault of their own, not everyone can be an expert or very informed about every topic).

I think this type of distinction is likely to hold among serious efforts to build cross cause models.

we aim to include more topics (which could include “meta” work and research) in future iterations of the model

Good to know.

Hey, just stepping into this thread to say that Toby’s out with a family emergency. I know many of you put a lot of time and care into the Forum, and I’m sorry the rest of moderation team doesn’t have capacity to engage in this comment section in his stead.

Interesting analysis! I’m somewhat skeptical that space based cells will perform as well as you find in this model. This seems like a classic case where something that looks good on paper will run into problems in real world applications for something as complicated as putting massive datacenters in space. I don’t think your model is accounting for murphy’s law here.

Also, on a technical note, do you have the source for the starlink solar cell specifications used for the phrase “Back-calculated from Starlink V3 specifications (50,400 W from 554 kg array, 257 m² area)”? This seems like one of the critical figures of your model but I couldn’t find where you linked the source for it.

If you’re interested in this work, then let me point out that RP is hiring.

I keep admiring AIM’s dedication to continuous iteration in the service of impact!

”EAG London this Saturday, 12–1pm: Office hours with Aaron and the AIM team. Drop in to ask questions, kick ideas around, or just say hi. Find it on Swapcard.”

Still not seeing this on Swapcard. Am I alone in this?

Happy to hear your thoughts.

I should clarify that I wrote this as someone who left a very EA bubble and came back after working in spaces that were remarkably worse. I agree with other commenters that we should aim to be better than the baseline. Regardless of where we fall on general harassment levels, I appreciate the community’s efforts to address issues as they happen.

Personal anecdote: multiple members of the CEA events team called out a vendor for behavior that I didn’t think to report as harassment. They proactively looped in HR and offered to negotiate this person’s exclusion from future event contracts at the venue. I was shocked, given my other recent work experiences.

This response didn’t erase the initial interactions, but it made the rest of the event (and future events) so much better. I want everyone to feel like they are working on a team /​ in a community that cares about them, even if making resources visible causes some people to think we have an outsized problem with harassment.

I don’t think I’m capable of raising this issue in a way that didn’t get critical comments...Suffice it to say that I spent a reasonable amount of time, rewrote it several times, looked for more data, showed it to friends. To me it seems like a pretty uncontroversial piece.

Did the friends you shared this with you all agree with you that this was a pretty uncontroversial piece that wasn’t likely to get critical comments? If so I think probably worth sharing with a broader set of people for feedback in future, as I think this pushback was reasonably predictable. Not saying that posts aren’t worth making if you get critical comments, but insofar as this is something you want to minimise, as it sounds like you were clearly trying to do this.

Separately if it’s a useful data point, reading these comments made me not wish to engage with this piece at the object level, at least at the moment

1, 2, 3, 4

Sent draft on DM—I prefer to keep this non-public both because my employer needs to review this before it goes out and also because I would like a few people to find my biggest errors before I put it out there. But I aim to get this posted publicly in the next couple of weeks. Thanks!

I mean if you can move DCs and production/​mining/​etc. to space that’s a solid win for AI safety? We then basically delineate AI and the only human-inhabitable planet we know about. That’s a heavy lift though, but maybe water on that one moon of Uranus (?) and other parts could be assembled off-Earth. This is literally galaxy brain thinking but something I feel might be worth pursuing, or at least showing AIs that it might be cheaper for them to pursue off-Earth living than engage in uncertain and high-cost conflicts on Earth. Maybe they don’t care about time, and would be happy to build towards this over the next 1000 years instead of spending valuable compute and industrial capacity to prepare for AI-human conflict.

While the future is very hard to predict, it would be very surprising if there was no possible action which benefitted the long-term future in expectation. And if there are actions like that, then they swamp the expected value of other actions. It is an error to infer from the fact that the future is very unpredictable that we’re totally in the dark about which actions can make it better.

I think this needs additional assumptions to make it go through in realistic situations. There is a bit of an errors-in-variables problem. I think as stated, the argument only works if we assume actual knowledge of expected values, but in realistic situations these would need to be estimated. For reasons similar to what is discussed here and here, I think you can have a situation where there are actions that have positive EV on the future, but your estimation + intervention selection process can’t reliably identify them, and integrating out this estimation/​selection based uncertainty can change the overall dynamic. Basically, your expected outcome isn’t the population expected value of a given action, the expected value you realize needs to take into account estimation/​selection.

This doesn’t mean that there aren’t positive EV estimation/​selection procedures, just that the existence of positive EV actions isn’t sufficient for the arugment to go through.

Happy to read a draft! We also looked a sea-based data centers, SMRs, and permitting/​buildout arbitrage in other nations. My current thinking is that you’ll see a lot of friction but its possible in principle to scale energy up, perhaps to the tune of hundreds of GW/​yr even if only using solar. The more fundamentally binding constraint to me seems to just be chip production but thats not to say that these various bottlenecks don’t at least partially bind or create friction at various points. You’d probably have a situation in hyperscalers are water flowing down hill, pivoting to the next best way to scale compute, then I think that eventually just goes to space. Given the vested interest of SpaceX and SpaceX’s longterm plans for lunar industrial base then Dyson Swarm construction I expect they’ll invest heavily in making them work. Seems to me they will probably successful in making ODCs viable and you’d see this dynamic where space prices are falling while headwinds for terrestrial buildout worsen. Many uncertainties remain however, difficult to forecast Starship scaling timelines. The regulatory environment is also currently favorable but untested at the scales we’re talking about and could change e.g. in a democratic admin.

All in all though, my personal guess is that its better for data centers to be in space but there are arguments to be made for and against. Seems like a hard call.

In the context of my post, the term “narrative argument” was really clearly used to refer to fictional stories embedded in a text as part of its argument, such as AI2027′s short story or the parables in Y&S’s book. My text simply does not have that.

By completely misunderstanding even basic concepts such as “narrative argument”, your comment was just a snarky jab and had little value. It didn’t comment the actual content of my text, instead, and largely unjustified by any real analysis, it tried to “get back at me” by blaming me for things I blame others for, like non-evidence based argumentation and narrative arguments.

Since I find it difficult to believe that you really don’t understand what a narrative argument is, I reasonably count a high probability for you simply acting in bad faith.

I don’t know if “snitching” you is allowed or not, and I apologize for the moderators if it’s not, but I think using examples of bad behavior is relevant in discussions of what is allowed on the forum.

p.s. I’m Sophie Davison on LinkedIn

Super nice post. Impressed by the ‘translation into EA language’ from the original post. It’s also a skill I’m working on.

I’d like to ask a little more about your point in “2. Fund rigorous evaluations of participatory allocation”
How does this process respond to participatory grantmaking inherently having a lower statistical verifiability, given that participatory grantmaking is generally much less uniform and traceable using quantitative techniques? There’s no good counterfactual for Extinction Rebellion (funded by Guerilla Foundation), and FundAction’s grantees are tiny and extremely diverse. I’ve seen granters have to give up on statistics in these cases and lean into other ways of knowing e.g. relationships, trust and feelings dreaded by all EAs;)
I think that a vast amount of time and resources will be needed to get a large enough sample size, and I’m not sure that social movement groups will be too happy about that.

E.g. mo’s megaprojects link in another comment!

FWIW I agree with the sentiment!

However, I think this post as it stands leans a lot on the Importance dimension of the ITN framework, gestures at Neglectedness, and doesn’t directly address Tractability. To be clear, that’s fine! Not every post needs to address everything; some of the linked posts address those somewhat; other resources out there do too.

But I do think that the question of ‘what can be done with 100s of extra $bn’ is also a question of T and N, and I think part (majority?) of what Ransohoff is saying is that we’re woefully unprepared ~1 level down from ‘cause area’ at the infrastructure /​ intervention /​ ecosystem /​ etc. level (where T and N mostly cash out, IMO).

If people have or can point to write-ups about how many and what size of shovel-ready projects we have for the various animal problems listed: definitely post and signal boost those!!

Maybe a crux here might be ‘what sort of net effect one expects from more or less just spending any amount of $ to hire anyone you can to try to find those projects’. Not saying that’s the author’s take per se; but I am somewhat skeptical of things shaped like “we need[...] a lot more people working on [X]” if what they mean is ‘we need to get warm bodies on this at all costs’. People can have net negative impact! There are opportunity costs to going all-in like that!

Overall I hope a large chunk of this wave of philanthropy goes to animal welfare, but also that it can be spent well and deployed quickly :)