• While this is very detailed, and looks like a good political newsletter, I’m not sure it’s a good fit for the forum. The constant newsflow of partisan topics can consume a lot of attention without adding much value, and relatively few of the topics covered here seem like plausible EA topic areas.

I also worry that in your attempt to cover a lot of area some of these summaries are quite misleading. This is probably inevitable, because it takes a lot of time to become informed about an issue, but it means that in many cases I think that someone reading this might end up coming to conclusions quite at odds to reality.

For example, consider this summary, of the CDC/​FDA’s decision to ban and then re-allow the JnJ vaccine:

Speaking of, the J&J pause was lifted, after 10 days. Much ink has been spilled describing the situation, but I think there were basically no good options here. Polls do seem to show that it hasn’t increased vaccine hesitancy.

You suggest that there were no good options, and link to a Mother Jones article on the subject. I would have expected this article to supply evidence for your claim by enumerating the possible options and showing that all of them had major problems. Indeed, the article does discuss two options:

• The CDC/​FDA could have hidden the data about the blot clots, hoped no-one noticed, and continued the rollout. But this risks undermining public trust if/​when the truth gets out.

• The CDC/​FDA could ban distribution of the the vaccine and investigate the issue.

However, the article does not consider what I and many other people consider to be obviously the best strategy:

• Disclose the blood clots to the public, but continue to allow its distribution.

It is possible that this is not in fact the best strategy. But “Tell people the truth, then let them make up their own minds” is normally the best strategy. At the very least an article arguing that there were no good options needs to at least consider this third option.

Secondly, you suggest that this pause has not damaged vaccine takeup. I do not think this view is supported by the evidence:

• The temporary suspensions of the AstraZeneca vaccine dramatically reduced public trust in the EU, especially when compared to the UK which had not done a similar suspension.

• The percentage of people who thought the JnJ vaccine was safe fell by about 15 percentage points.

• Vaccination rates for every age group dropped dramatically when the FDA banned the JnJ vaccine, regardless of vaccine penetration.

• Even after the ban was lifted, almost no-one wants to get the J&J vaccine now. Takeup rates for the other vaccines have not risen to compensate; they have actually fallen.

• Many people in America would have preferred the J&J vaccine, because it only requires one shot, and is not ‘new, unsafe’ technology.

• Even if no lasting damage has been done to vaccine willingness, around 50 people will have died of covid who would otherwise have been saved by the J&J vaccine. In exchange, around 2 probably non-fatal blood clots were averted.

Given the above as well as my wider reading around the issue, I actually think a fairer summary would have been:

Speaking of, the J&J pause was lifted, after 10 days. While initially establishment public health experts were supportive of the decision, political scientists and well informed amateurs were sharply critical, arguing that this pause was unnecessary given the extremely low incidence of blood clots and importance of ending the pandemic. Subsequent data suggests that the fear generated by this decision has significantly undermined the US vaccination program.

Unfortunately gathering the data to illustrate this point is quite time consuming. Despite being already well versed in the subject, the above section took me over an hour to put together in response to a single bullet point, and I count 38 similar news bullet points in the post. I provide it as an illustration of the difficulties involved in this project; I think many of your other bullet points contain similar inaccuracies and misleading statements, but it is simply too time consuming to go into them.

If you do continue, I would strongly encourage you to consider getting a Republican to review your writeups. Many of the sections have a distinct pro-administration bias, and I think checking this would be the easiest way to significantly improve the overall accuracy.

• I think the current title of the sequence is fine and probably better than the rest of the alternatives you put!

• Nondogmatic Social Discounting seems very loosely related. Could be an entry point for further investigations, references, etc.

The long-run social discount rate has an enormous effect on the value of climate mitigation, infrastructure projects, and other long-term public policies. Its value is however highly contested, in part because of normative disagreements about social time preferences. I develop a theory of “nondogmatic” social planners, who are insecure in their current normative judgments and entertain the possibility that they may change. Although each nondogmatic planner advocates an idiosyncratic theory of intertemporal social welfare, all such planners agree on the long-run social discount rate. Nondogmatism thus goes some way toward resolving normative disagreements, especially for long-term public projects.

• I think this post unhelpfully mixes general, systemic criticisms around innovation, public goods and IP (which I’m very interested in and sympathetic to) with the “news hook”—COVID vaccines. It strikes me as incredibly unlikely that we’ll determine and shift to a better solution in the current crisis. I think the most likely outcome of action here would be to shift us out of the local maximum but not into the global maximum. I think a proposal of an alternative system, an analysis of its cost and benefits relative to the status quo , and a plan for how to get there from here would receive a very different reception.

• I am not sure Effective Altruism has been a net hedonic positive for me. In fact, I think it has not been.

Recently in order to save money to donate more, I chose to live in very cheap housing in California. This resulted in many serious problems. Looking back arguably the biggest problem was the noise. If you cram a bunch of people into a house it’s going to be noisy. This very badly affected my mental health. There were other issues as well. My wife and I could have afforded a much more expensive place. That would have been money very well spent. I was really quite miserable.

During the 2017 crypto bull run, I held a decent amount of ETH. Pretty close to the top I gave away half since I felt like I had hit a huge windfall. Of course, ETH crashed to around 87 from a high of 1400. So I ended up not as rich as I thought. It didn’t help that I handled the bear market poorly. Maybe it was good that I donated the ETH instead of selling it for far less. But maybe I would have handled the bear market better had I kept more ETH or cashed some out for myself.

In the end, things went fine for me. But the decision to donate so much at the top really haunted me for years. Of course, I did not donate 10%. A 10% donation threshold would mean donating 10% of the ETH I cashed out (potentially 0 dollars). Until you sell you don’t have any taxable income. I have again donated all the crypto I cashed out. But this time I have donated a much smaller percentage of my bankroll.

I am also quite terrified of the singularity. It has not been easy for me to deal with the ‘singularity is near’ arguments I hear in the rationality and EA communities.

Of course, I think my involvement with EA has been positive for the world. In addition to donations, I gave some money to some poorer friends. They certainly appreciated it. But effective altruism has not been an easy road.

• I like this framing a lot—not seeing EA (or any kind of moral imperative) as a sacrifice but something that can be additive/​fulfilling is crucial, I think.

However, I want to add a cautionary note against only focusing on the positives of spreading/​joining the EA community. I don’t think you intended to suggest that at all, but in my experience EA can exacerbate perfectionist tendencies in a way that is deleterious to mental health, and being aware of that might be important in ensuring that spreading EA leads to fulfillment. I think this can be mitigated by emphasizing the social aspect and encouraging people to view EA as a community instead of purely a framework/​standard. Fortunately your point lends itself well to this, since spreading word of EA to one’s friends is inherently social!

• Cool idea! Some thoughts I have:

• A different thing you could do, instead of trading models, is compromise by assuming that there’s a 50% chance that your model is right and a 50% chance that your peer’s model is right. Then you can do utility calculations under this uncertainty. Note that this would have the same effect as the one you desire in your motivating example: Alice would scrub surfaces and Bob would wear a mask.

• This would however make utility calculations twice as difficult as compared just using your own model, since you’d need to compute the expected utility under each model. But note that this amount of computational intensity is already assumed by the premise that it makes sense for Alice and Bob to trade models. In order for Alice and Bob to reach this conclusion, each needs to compute their utility under each action in each of their models.

• I would say that this is more epistemically sound than switching models with your peer, since it’s reasonably well-motivated by the notion that you are epistemic peers and could have ended up in a world where you had had the information your peer has and vice versa.

• But the fundamental issue you’re getting at here is that reaching an agreement can be hard, and we’d like to make good/​informed decisions anyway. This motivates the question: how can you effectively improve your decision making without paying the cost required by trying to reach an agreement?

• One answer is that you can share partial information with your peer. For instance, maybe Alice and Bob decide that they will simply tell each other their best guess about the percentage of COVID transmission that is airborne and leave it at that (without trying to resolve subsequent disagreement). This is enough to, in most circumstances, cause each of them to update a lot (and thus be much better informed in expectation) without requiring a huge amount of communication.

• Which is better: acting as if each model is 50% to be correct, or sharing limited information and then updating? I think the answer depends on (1) how well you can conceptualize your peer’s model, (2) how hard updating is, and (3) whether you’ll want to make similar decisions in the future but without communicating. The sort of case when the first approach is better is when both Alice and Bob have simple-to-describe models and will want to make good COVID-related decisions in the future without consulting each other. The sort of case when the second approach is better is when Alice and Bob have difficult-to-describe models, but have pretty good heuristics about how to update their probabilities based on the other’s probabilities.

I started making a formal model of the “sharing partial information” approach and came up with an example of where it makes sense for Alice and Bob to swap behaviors upon sharing partial information. But ultimately this wasn’t super interesting because the underlying behavior was that they were updating on the partial information. So while there are some really interesting questions of the form “How can you improve your expected outcome the most while talking to the other person as little as possible”, ultimately you’re getting at something different (if I understand correctly) -- that adopting a different model might be easier than updating your own. I’d love to see a formal approach to this (and may think some more about it later!)

• Under what circumstances is it potentially cost-effective to move money within low-impact causes?

This is preliminary and most likely somehow wrong. I’d love for someone to have a look at my math and tell me if (how?) I’m on the absolute wrong track here.

Start from the assumption that there is some amount of charitable funding that is resolutely non-cause-neutral. It is dedicated to some cause area Y and cannot be budged. I’ll assume for these purposes that DALYs saved per dollar is distributed log-normally within Cause Y:

I want to know how impactful it might, in general terms, be to shift money from the median funding opportunity in Cause Y to the 90th percentile opportunity. So I want the difference between the value of spending a dollar at those two points on the impact distribution.

The log-normal distribution has the following quantile function:

So the value to be gained by moving from p = 0.5 to p = 0.9 is given by

This simplifies down to

Or

Not a pretty formula, but it’s easy enough to see two things which were pretty intuitive before this exercise. First, you can squeeze out more DALYs from moving money in causes where the mean DALYs per dollar across all funding opportunities is higher, and, for a given average, moving money is higher-value where there’s more variation across funding opportunities (roughly, since variance is proportional to but not precisely given by sigma). Pretty obvious so far.

Okay, what about making this money-moving exercise cost-competitive with a direct investment in an effective cause, with a benchmark of $100/​DALY? For that, and for a given investment amount$x, and a value c such that an expenditure of $c causes the money in cause Y to shift from the median opportunity to the 90th-percentile one, we’d need to satisfy the following condition: Moving things around a bit... Which, given reasonable assumptions about the values of c and x, holds true trivially for larger means and variances across cause Y. The catch, of course, is that means and variances of DALYs per dollar in a cause area are practically never large, let alone in a low-impact cause area. Still, the implication is that (a) if you can exert inexpensive enough leverage over the funding flows within some cause Y and/​or (b) if funding opportunities within cause Y are sufficiently variable, cost-effectiveness is at least theoretically possible. So just taking an example: Our benchmark is$100 per DALY, or 0.01 DALYs per dollar, so let’s just suppose we have a low-impact Cause Y that is between three and six orders of magnitude less effective than that, with a 95% CI of [0.00000001,0.00001], or one for which you can preserve a DALY for between $100,000 and$100 million, depending on the opportunity. That gives mu = −14.97 and sigma = 1.76. Plugging those numbers into the above, we get approximately...

...suggesting, I think, that if you can get roughly 4000:1 leverage when it comes to spending money to move money, it can be cost-effective to influence funding patterns within this low-impact cause area.

There are obviously a lot of caveats here (does a true 90th percentile opportunity exist for any Cause Y?), but this is where my thinking is at right now, which is why this is in my shortform and not anywhere else.

• 15% does not sounds too bad.

15% seems to me like very bad odds for a multi-year training program, especially given it doesn’t count people who start a PhD program and then drop out.

• Interesting, thank you for sharing.

Do you have a take on how accurate the national average estimates are? In particular, I’d be interested in whether they were determined using a different methodology, and so perhaps one that will be biased toward “underreporting”. Where as at first glance your methodology might seem to be biased toward “overreporting” (though idk to what extent you may have “corrected” for non-reponse bias, which would be one source of “overreporting”).

• I’m not fully satisfied with the label I’m currently using for this topic/​effort and this sequence. Here are some alternatives that I considered or that other people suggested:

• Scaling the EA research pipeline

• Scalably training and making use of research talent

• Unlocking EA-aligned research and researchers (more, better, and more efficiently)

• Scaling the EA research engine

• Amplifying EA-aligned researchers

• Revving up the EA research engine

• Priming the pump of EA research

• Engineering the EA research ecosystem

(That’s in roughly descending order of how much I like them. And of course I currently prefer the label I’m actually using at the moment.)

• Oh, I am going to start advise undergrads on career choices soon. Many of them will want to go to graduate schools. So I would like to give them some cautions. Please let me know when your article comes out. Good luck with publishing it!

• WOOOHOOO!! Way to go!

• I think this is a great idea and personally I think it’s relevant enough for the forum

• Besides just extrapolating trends in cost of production/​prices, I think the main things to track would be feed conversion ratios and the possibility of feeding animals more waste products or otherwise cheaper inputs, since feed is often the main cost of production. Some FCRs are already < 2 and close to 1, e.g. it takes less than 2kg of input to get 1kg of animal product (this could be measured in just weight, calories, protein weight, etc..), e.g. for chickens, some fishes and some insects.

I keep hearing that animal protein comes from the protein in what animals eat (but I think there are some exceptions, at least), so this would put a lower bound of 1 on FCR in protein terms, and there wouldn’t be much further to go for animals close to that.

I think a lower bound of around 1 for weight of feed to weight of animal product also makes sense, maybe especially if you ignore water in and out.

So, I think chicken meat prices could roughly at most halve again, based on these theoretical limits, and it’s probably much harder to keep pushing. Companies are also adopting less efficient breeds to meet welfare standards like the Better Chicken Commitment, since these breeds have really poor welfare due to their accelerated growth.

This might be on Lewis Bollard’s radar, since he has written about the cost of production, prices and more general trends in animal agriculture.

• From talking to the people helping the Swasti fundraiser in question, they seem to be working on data and coordination, but appear to be working separately on procurement and distribution! In any case, our point was more that Swasti and Swasth are separate organizations (and the similar name seems like a coincidence).

• Thanks for adding the rec! It looks like they are working together, actually. From Swasti’s updates page: “The campaign is in association with Swasti.org which in-turn is working with the Swasth Alliance & ACT to procure oxygen concentrators for the most in-distress areas in the country.” It sounds like you’ve been in touch with Swasti directly, have you heard differently?