1755 karmaJoined


Personally, I think specifically forecasting for drug development could be very impactful: Both in the general sense of aligning fields around the probability of success of different approaches (at a range of scales -- very relevant both for scientists and funders) and the more specific regulatory use case (public predictions of safety/efficacy of medications as part of approvals by FDA/EMA etc.) 

More broadly, predicting the future is hugely valuable. Insofar as effective altruism aims to achieve consequentialist goals, the greatest weakness of consequentialism is uncertainty about the effects of our actions. Forecasting targets that problem directly. The financial system creates a robust set of incentives to predict future financial outcomes -- trying to use forecasting to build a tool with broader purpose than finance seems like it could be extremely valuable. 

I don't really do forecasting myself so I can't speak to the field's practical ability to achieve its goals (though as an outsider I feel optimistic), so perhaps there are practical reasons it might not be a good investment. But overall to me it definitely feels like the right thing to be aiming at.

I think that’s the idea but I also don’t know that many details

Dan Watendorf at the Gates Foundation has said they've funded a few different companies that produce broadly effective antiviral prophylactics (e.g. a nasal spray that would keep you from getting colds, flus, and COVID for 3 months). He seemed to be optimistic about the technical solvability of the problem but pessimistic about a financing model that would make it viable (i.e. that transmission-reduction is not properly incentivized by the market)

I feel like as president of 1Day Sooner I should probably chime in -- first, I wanted to say this type of work -- critiquing advocacy campaigns and analyses from EA or EA-aligned groups -- is very valuable and should be encouraged. I'm appreciative of SoGIve for publishing this and think they should be commended for spending the time to conduct this analysis. I think creating a healthy ecosystem for disagreement and the right incentives to encourage criticism and full-throated debate is important. 

On the object-level question, I'm obviously biased but I think most of the difference in cost-effectiveness in the SoGive analysis goes away if you adjust for the fact that vaccines are only given to children under 5 but only ~15-20% of bednets cover children under 5. Because 75% of malaria mortality is in children under 5, bednets are cheaper per person protected but the vaccines are much more targeted to people whose protection is most valuable. (The development benefit effects of reducing morbidity in children are also age-skewed in vaccines' favor though that's less dramatic). 

Insecticide resistance (probably reduces bednets' effectiveness to about 80% of what they'd otherwise be) and durability (GW estimates each bednet purchased provides about 1.7 year of coverage) are probably also relevant. The AMF tab of the GiveWell spreadsheet is a useful resource in thinking through these questions. 

For more of my thinking, here's my side of the email correspondence with Sanjay at SoGive. (I didn't include text from other people on the thread because I haven't asked their permission to share). An interesting meta-question is what should be the norm about making these sort of red-teaming or adversarial post-review correspondences public. My guess is it's probably a good thing to default to because it incentivizes people to be on their best behavior (and the benefits to confidentiality of being able to speak frankly don't seem that strong in these cases). But I don't think it's obvious either way and would be curious what other people think.

Overall, I'm eager to see more analysis done digging into the Imperial/Oxford modeling of cost benefit of the R21 vaccine (which comes to about 630 lives saved per 100K vaccinated, see Table 2) and what's publicly available about the WHO estimate of 13% all-cause mortality reduction from RTS,S. (Here's an older preprint that finds a smaller benefit -- more recent data that was publicly reported is apparently higher). So I think generally the follow-on research plan Sanjay discusses makes sense from my perspective, and I'd be personally supportive of anyone who wants to contribute to that work. 

Thanks for your interest! I've copied a description of the pool work below to give a better sense, but basically it's mostly research tasks that are like "research how vaccine distribution (not purchasing doses) is normally funded for new vaccines and write a 3-5 paragraph summary)" or "take a 3-5 paragraph summary someone wrote and create 3-6 sentences of suggested text to include in the status report" or "cite-check a section of talking points to make sure all the facts mentioned have citations and that those citations actually support the facts." 

Overall, we're very much in a "more the merrier" stage and would love your help. 

Here are more details on the scheme:


Plan for a Pool System to Handle Research/Talking Points 


Our talking points are intended to be a live, continually updated document representing our best understanding of malaria vaccination and how to improve rollout. In a sense it is intended to be a “global workspace” for our campaign thinking, where new research on key questions is inputted and accurate and relevant information about vaccination is shared across the campaign. Stylistically the talking points are intended to emphasize brevity, simplicity, and ease of use by a general audience.


To create a manageable process to continually update and improve the document (i.e. by executing this rolling punch list of tasks), we propose a pool system where volunteers sign up for a five days per month where they are “on-call” and will be assigned a 1-2.5 hour task per day, with assignments going out the night before and due the following morning (e.g. a Monday pool task would go out Sunday night and be due Tuesday morning). The expectation would be pool members would ideally sign up for two 2-day blocks and one 1-day block in a month or 3-day and 2-day blocks. The blocks are so that larger tasks (3-5 hours) can be assigned over a two day period. 


We’d aim to have at least eight pool members and one pool manager.


A month at a time so just January at this point

I run an advocacy nonprofit, 1Day Sooner. When good things happen that we have advocated for, it raises the obvious question, "were we the but-for cause?" 

A recent experience in our malaria advocacy work (W.H.O. prequalification of the R21 vaccine, a key advocacy target of ours) is exemplary. Prequalification was on the critical path for malaria vaccine deployment. Based on analysis of public sources and conversations with insiders, we came to the view that there was friction and possibly political pressure delaying prequalification from occurring as quickly as would be ideal. We decided to focus public pressure on a faster process (by calling for a prequalification timeline, asking Peter Singer to include the request in his op-ed on the subject, discussing the issue with relevant stakeholders, and asking journalists to inquire about it). We thought it would take at least till January and probably longer. Then a few days before Christmas, a journalist we were talking to sent us a W.H.O. press release -- that morning prequalification had been announced. Did it happen sooner because of us?

The short answer is we don't know. The reason I'm writing about it is that it highlights a type of causal uncertainty that I think is common (though not universal) in advocacy campaigns and should be relevant to EA thinking. 

In some campaigns, you find yourself on the inside of a decision-maker's process in a way that can give you some amount of certainty as to your role.[1] For my kidney donor reimbursement campaign at Waitlist Zero (pre-1Day Sooner), I saw some text related to some Trump administration actions before they happened, had good transparency into the decision-making behind the Advancing American Kidney Health Initiative that my policy was a part of, and had decent confidence that my work was a but-for cause. 

But for others, like the W.H.O. prequalification above or the Biden Admnistration's announcement of Project NextGen, things are much fuzzier. Something you advocated for happens without your getting advance notice. You've made the case for it publicly and perhaps exercised some levers to pressure decision-makers. Did you influence the outcome? How can you know? 

I'm highlighting this experience because when it happened with NextGen I didn't really understand how to think about it, and now with prequalification I'm at least noticing the common pattern. (To be clear, I think the case for our causal influence on prequalification is stronger than for NextGen). 

I think it's of note for EA advocates because it raises the challenge of evaluating advocacy through a consequentialist framework. To me the strongest theoretical challenge to consequentialism is uncertainty and the unknowability of the future. The uncertainty of advocacy impact is a very practical example of this broader challenge. 

One thought about this I'd sent to a funder who asked about the NextGen campaign is the below:

So like ideally what you want as an advocacy group is to be inside-track on relevant decisions but maybe second-best is to be creating a narrative out of which the decision you are seeking manifests. That is to say we put on the performance of the need/desirability of a "Warp Speed 2" that the relevant decision-makers witnessed and participated in (even if it is unknowable whether it was causal or they were reactive to it) and this performative scaffold is sort of prima facie valuable for goals that are valuable (and the nature of advocacy/consequentialism is that oftentimes that's all that will be knowable even during a successful campaign). 

To be clear, the prequalification advocacy story was different than this performative scaffolding concept -- the most obvious way we may have been causally relevant is that the comms department of the relevant entities were likely getting journalistic inquiries about the issue from some major outlets, which very possibly scared the bejesus out of them and increased the desirability of hurrying up.

I raise these case studies because I hope they can provoke further thought, discussion, and insights from EAs involved in advocacy work.

  1. ^

    Even this is complicated because decision-makers often have an incentive to flatter you and for many issues, even if you're on the inside of the process you don't know if the process would have happened without you

As someone who runs one of EAs advocacy contingents, I think the overall idea of more criticism is probably a good idea (though I suspect I'll find it personally unpleasant when applied to things I work on), but I'd suggest a few nuances I think exist here:

  1. EA is not unitary, and different EAs and EA factions will have different and at times opposing policy goals. For example, many of the people who work at OpenAI/Anthropic are EAs (or EA adjacent), but many EAs think working at OpenAI/Anthropic leads to AI acceleration in a harmful way (EAs also have differing views of the relevant merits of those two firms). 
    1. Which views are considered EA can change the composition of who identifies as EA, EA-adjacent, unopposed, and EA-hostile -- e.g. my perception of Sam Altman would be as EA-adjacent, but the perception that EAs have been critical of OpenAI, along with other events, likely pushed him further away from EA than he'd otherwise be; Elon Musk and Peter Thiel may also be related examples.
  2. Advocacy is inherently information-lossy, since it involves translating information from one context into a format that will be persuasive in some sort of politically useful way. Usually this involves simplification (because a popular or decision-maker audience has less bandwidth than an expert audience) and may also involve differentiation (since the message will probably tend to be optimized to fit something like the existing views of its audience). This is a hard challenge to manage. 
    1. One type of simplification I've noticed is from an internal EA-organizing perspective -- where the experts/leaders at the center tend to have nuanced, reasonable views, but those views, when being transmitted to organizers who again transmit to less experienced people interested in EA, can become translated into a dogma that is simplistic and rigid.
    2. Two case studies of EA (or EA-adjacent) advocacy -- monetary/macroeconomic policy and criminal justice reform -- have had interestingly different trajectories. With monetary policy in the U.S., EA-funded groups tended to foreground technical policy-understanding and (in my opinion) did a good job transitioning their recommendations as macroeconomic conditions changed (am thinking mainly of Employ America). The criminal justice reform movement (where I founded a volunteer advocacy organization, the Rikers Debate Project) has in my opinion been mostly unable to reorient its recommendations and thinking in response to changing conditions. In my opinion, the macroeconomic policy work had more of a technocratic theory of change than more identity-oriented criminal justice reform efforts funded by EA though there were elements of technocracy and identitarianism in both fields. (Rikers Debate, which was not funded by EA groups, has historically been more identitarian in focus).  

This has some of 1Day's thoughts (though it was published before PQ happened). This tweet thread is briefer and has some more technical ideas. 

Big picture -- probably need to raise about $500 million to pay for distribution and need to get WHO/GAVI/UNICEF to announce a much more aggressive plan for implementation. Also need to do a ton of work in African countries to create the political will and technical plans for rapid rollout.

From an advocacy perspective, 1Day's main idea is to generate a ton of public attention to achieve the goals above (particularly the money) -- ideally by bringing in new philanthropic funders, though that may be unrealistic. But there will also be more targeted campaigns at the international institution and African country levels.

Current rollout plans are unclear, but the below probably gives the best sense of current international institution goals. 

As you can see, current vision doesn't get to the predicted steady state (80 million doses) until about 2027. Serum Institute can currently produce 100 million doses per year and already has on-hand material to make 20 million. About 80 million children are at risk of malaria and in the age range in which R21 was tested and demonstrated high efficacy. About 25 million are born each year. Each child requires 4 doses (3 doses the first year followed by a booster).

For a while I've been thinking about an idea I call Artificial Environment risk, but I haven't had the time to develop the concept in detail (or come up with a better name). The idea is roughly that the natural environment is relatively robust (since it's been around for a long time, and average species extinction rates and reasons are somewhat predictable), but as a higher proportion of humanity's environment is artificially created we voyage into an unknown without a track record of safety or stability. So the risk of dangerous phenomena increase dramatically -- as we go to a paradigm where we depend on an environment that has been roughly stable for millions of years to one for which exists evidence measured maybe in decades (or less!). Global warming and ozone degradation are obvious examples of this. But I also think that AI risk, biosecurity, nuclear war etc. fall under this (perhaps overly large) umbrella -- as humans gain more and more ability to manipulate our environment, we accumulate more and more opportunities for systemic destruction. 

Some risks, like global warming, are fairly observable and relatively straightforward epistemically. But then things like the attention environment and influence of social media are more complicated -- as tools for attracting attention become more and more powerful, it creates unforeseen (and hard-to-specify or verify) effects (e.g. perhaps education polarization, weakening of elite mediation of information, harm to public discourse) that may be quite influential (and may interact with or compound other effects) but will be hard to plan for, understand, or mitigate. 

One reason I find the idea worth trying to sketch out is, assuming technological development continues to progress, as humanity's control over our environment increases, risk will generally continue to rise, and fewer technologies will be realistically considered riskless. (So we will have more tradeoffs about things like whether to develop technologies that can end infectious disease but also enable better weapons). 

The idea of differential technological acceleration is aimed at this problem, but I am not sure how predictable offense/defense will be or how to effectively make political decisions about which fields and industries to nurture or cull. Part of the implication I draw from categorizing this broad set of risks together is that the space for new scientific and technological development will become more crowded -- with fewer value-neutral or obviously positive opportunities for growth over time. 

I think this may also tend to manifest in more of a clear division between what you might call left-EAs (progress-focused) and right-EAs (security-focused) (in some sense this corresponds to global health focused EAs vs. existential risk focused EAs currently, but the division is less clear). But that also  goes to a separate view I have that EAs will have to accept more internal ideological diversity over time and recognize that the goals of different effective altruists will conflict with one another (e.g. extending human lifespan may be bad for animal welfare; synthetic biology advances to cure infectious disease may increase risks to biohazards etc.). 

Very possible these ideas aren't original -- as I said they're very thinly sketched at the moment, but have been thinking about them for a while so figured I should write them out. 

Load more