I think the COVID case usefully illustrates a broader issue with how “EA/rationalist prediction success” narratives are often deployed.

That said, this is exactly why I’d like to see similar audits applied to other domains where prediction success is often asserted, but rarely with much nuance. In particular: crypto, prediction markets, LVT, and more recently GPT-3 / scaling-based AI progress. I wasn’t closely following these discussions at the time, so I’m genuinely uncertain about (i) what was actually claimed ex ante, (ii) how specific those claims were, and (iii) how distinctive they were relative to non-EA communities.

This matters to me for two reasons.

First, many of these claims are invoked rhetorically rather than analytically. “EAs predicted X” is often treated as a unitary credential, when in reality predictive success varies a lot by domain, level of abstraction, and comparison class. Without disaggregation, it’s hard to tell whether we’re looking at genuine epistemic advantage, selective memory, or post-hoc narrative construction.

Second, these track-record arguments are sometimes used—explicitly or implicitly—to bolster the case for concern about AI risks. If the evidential support here rests on past forecasting success, then the strength of that support depends on how well those earlier cases actually hold up under scrutiny. If the success was mostly at the level of identifying broad structural risks (e.g. incentives, tail risks, coordination failures), that’s a very different kind of evidence than being right about timelines, concrete outcomes, or specific mechanisms.

I think the COVID case usefully illustrates a broader issue with how “EA/rationalist prediction success” narratives are often deployed.

That said, this is exactly why I’d like to see similar audits applied to other domains where prediction success is often asserted, but rarely with much nuance. In particular: crypto, prediction markets, LVT, and more recently GPT-3 / scaling-based AI progress. I wasn’t closely following these discussions at the time, so I’m genuinely uncertain about (i) what was actually claimed ex ante, (ii) how specific those claims were, and (iii) how distinctive they were relative to non-EA communities.

This matters to me for two reasons.

First, many of these claims are invoked rhetorically rather than analytically. “EAs predicted X” is often treated as a unitary credential, when in reality predictive success varies a lot by domain, level of abstraction, and comparison class. Without disaggregation, it’s hard to tell whether we’re looking at genuine epistemic advantage, selective memory, or post-hoc narrative construction.

Second, these track-record arguments are sometimes used—explicitly or implicitly—to bolster the case for concern about AI risks. If the evidential support here rests on past forecasting success, then the strength of that support depends on how well those earlier cases actually hold up under scrutiny. If the success was mostly at the level of identifying broad structural risks (e.g. incentives, tail risks, coordination failures), that’s a very different kind of evidence than being right about timelines, concrete outcomes, or specific mechanisms.

I can’t join this Sunday (finals season whoo!), but this is a really good idea. I’d love to see more initiatives like this to encourage writing on the Forum—especially during themed weeks.

Also, I’m always down to do (probably remote) co-working sessions with people who want to write Forum posts.

Strongly agreed. Organizing a group is probably one of the best things one could do for both their present and future impact. 

I (and many others) would be happy to get on a call/help anyone willing to take over (I have a bunch of experience from organizing the UChicago group)! Dm me here to take me up on that.  

Many questions in this space rely on assumptions about whether insect lives are positive or negative, though I haven't seen much discussion of this explicitly (mostly just heard them in conversations). Is there not much that can be done to learn more other than what has already been done? 

If so, the insect welfare initiatives that are interested in creating more or less are going to need to be dropped by 1-2 OOMs to account for massive uncertainty (say, 5% higher that they are positive), which is weird. It also wouldn't be very robust in that the probability we do something good vs bad is quite fragile (especially a problem for many non hedonistic utilitarian views). 

Do you have further takes here, Vasco? 

I would look out for Rethink Priorities' digital consciousness model (and other work they are doing here) which should be coming out soon-ish. I don't think they would call it definitive in any sense, but it could be helpful here. 

I think a major way this could be wrong is if you think we could get lots of digital minds in some amount of decades and early research/public engagement can have an oversized impact on shaping the conversation. This might make digital minds way more important, I think. 

I'm also generally pretty interested in people doing more digital minds cross-cause prio (I'm working on a piece now)! 

• Re the new 2024 Rethink Cause Prio survey: "The EA community should defer to mainstream experts on most topics, rather than embrace contrarian views. [“Defer to experts”]" 3% strongly agree, 18% somewhat agree, 35% somewhat disagree, 15% strongly disagree.
  • This seems pretty bad to me, especially for a group that frames itself as recognizing intellectual humility/we (base rate for an intellectual movement) are so often wrong.
  • (Charitable interpretation) It's also just the case that EAs tend to have lots of views that they're being contrarian about because they're trying to maximize the the expected value of information (often justified with something like: "usually contrarians are wrong, but if they are right, they are often more valuable for information than average person who just agrees").
    • If this is the case, though, I fear that some of us are confusing the norm of being contrarian instrumental reasons and for "being correct" reasons. 

Tho lmk if you disagree. 

This is great, and people should do this for more cause areas! 

Idea for someone with a bit of free time: 

While I don't have the bandwidth for this atm, someone should make a public (or private for, say, policy/reputation reasons) list of people working in (one or multiple of) the very neglected cause areas — e.g., digital minds (this is a good start), insect welfare, space governance, AI-enabled coups, and even AI safety (more for the second reason than others). Optional but nice-to-have(s): notes on what they’re working on, time contributed, background, sub-area, and the rough rate of growth in the field (you probably don’t want to decide career moves purely on current headcounts). And remember: perfection is gonna be the enemy of the good here.

Why this matters

Coordination.
It’s surprisingly hard to know who’s in these niches (independent researchers, part-timers, new entrants, maybe donors). A simple list would make it easier to find collaborators, talk to the right people, and avoid duplicated work.

Neglectedness clarity.
A major reason to work on ultra-neglected causes is… neglectedness. But we often have no real headcount, and that may push people into (or out of) fields they wouldn’t otherwise choose. Even technical AI safety numbers are outdated — the last widely cited 80k estimate (2022) was ~200 people, which is clearly very false now. (To their credit, they emphasized the difficulty and tried to update.)

Even rough FTE (full time equivalent) estimates + who’s active in each area would be a huge service for some fields.