Thanks for the response!

I understand that you are worried about chicken and fish consumption. I have no knowledge about why these charts are the way they are, or why people in the UK consume twice as much chicken as those in Germany. It's also difficult to guess the impact of Veganuary in these trends. Insofar, I find the charts a bit distracting.

What I intended to say with my comment is that Veganuary has clearly visible impacts around me: when I go shopping, when I see ads, when I eat out. This seems to correlate with a general trend of seeing more vegan products, brands, and menu choices. Maybe the general trend that I identified is similarly distracting as your chicken and fish charts... yet it does seem to be something that Veganuary directly works on and influences.

I suspect that you brought up the chicken and fish charts because you worry about shifts in consumption from larger animals to higher numbers of small animals. This is a real possibility, but I would be wary of accusing Veganuary to cause such a shift, without good evidence. I grant that Veganuary tries to appeal to a broad range of people with various reasons for reducing meat consumption, including climate reasons which might cause a shift away from ruminants. But I recall there was a lot of Veganuary content around animal welfare. Personally, Veganuary shifted my views to care more about animals.

Animal welfare seems to be the main participant motivation. Here's a figure from the 2023 survey report:

Taking a step back, it's a little sad that this article feels so hostile towards Veganuary, and shows Veganuary in a bad light primarily because of discounts and back-of-the-envelope numbers that seem quite arbitrary. I see a lot less competition than you do between Veganuary and work on shrimp welfare or cage-free campaigns. On the contrary, people who have participated in Veganuary are likely more receptive for that type of work, and this is a benefit that we won't find in CEAs ;-)

It's great to try and analyze the cost-effectiveness of Veganuary. I'm thankful for this post and also for the responses by @Toni Vernelli and others.

While I appreciate the effort, I find it hard to agree with Vasco's conclusions. There are many discounts in the analysis that feel pretty arbitrary to me. Toni has answered to this much better than I could. I'd just like to share a few personal impressions. These are of course biased, but might explain why I'm suspicious about the many downward adjustments (and lack of upward adjustments) in Vasco's analysis:

• Veganuary is quite prominent where I live. There are numerous new products in supermarkets. I've seen many ads for vegan products in January, not directly by Veganuary but by franchises like Burger King.
• During Veganuary 2020, my wife and I made the decision to become vegan. We had been vegetarians before, and found out during Veganuary that a fully vegan lifestyle was easier than expected. Since then, one of our flatmates transitioned from omnivore to vegan. Another flatmate stayed omnivore but ate mostly vegan during the year that she lived with us. This is an extreme example, but it shows that the 31 emails can affect more than just one person, and for a duration longer than 6 months.

Overall, there seems to be a clear trend in Germany toward more vegan products. Oat milk shelves are larger than cow milk shelves in many retailers nowadays; there are many meat alternatives; vegan products are becoming popular also in other areas such as chocolate and baked goods. It's difficult to isolate the effect that Veganuary has played in all this... but I'd be surprised if it was as small as Vasco estimates.

EA charities can also combine education and global health, like https://healthlearn.org/blog/updated-impact-model

HealthLearn builds a mobile app for health workers (nurses, midwives, doctors, community health workers) in Nigeria und Uganda. Health workers use it to learn clinical best practices. This leads to better outcomes for patients.

I'm personally very excited by this. Health workers in developing countries often have few training resources available. There are several clinical practices that can improve patient outcomes while being easy to implement (such as initiating breastfeeding immediately after birth). These are not as widely used as we would like.

HealthLearn uses technology as a way to faithfully scale the intervention to thousands of health workers. At this point, AI does not play a significant role in the learning process yet. Courses are manually designed. This was important to get started quickly, but also to get approval from government health agencies and professional organizations such as nursing councils.

The impact model that I've linked to above estimates that the approach has been cost-effective so far, and could become better with scale.

(disclaimer: I'm one of the software engineers building the app)

Personally, I'm not using the forum as much as I could and as much as I used to, because it is a time-sink. I'm the kind of person who can easily get lost on the Internet; clicking a link here and opening another tab there, and... look where those two hours went. Because of this, I'm wary of spending too much time here.

I don't know whether my declining forum use is due to changes in my behavior or changes to the forum. Probably it's a combination. On the forum side, the home page feels a bit more cluttered than it used to be. The forum feels slightly more gamified (e.g., emoji reactions).

I don't have concrete suggestions, other than thinking about what would be an ideal time for users to spend on the forum. A time that takes both the forum quality and its user's productivity into account.

OP here :) Thanks for the interesting discussion that the two of you have had!

Lukas_Gloor, I think we agree on most points. Your example of estimating a low probability of medical emergency is great! And I reckon that you are communicating appropriately about it. You're probably telling your doctor something like "we came because we couldn't rule out complication X" and not "we came because X has a probability of 2%" ;-)

You also seem to be well aware of the uncertainty. Your situation does not feel like one where you went to the ER 50 times, were sent home 49 times, and have from this developed a good calibration. It looks more like a situation where you know about danger signs which could be caused by emergencies, and have some rules like "if we see A and B and not C, we need to go to the ER".^[1]

Your situation and my post both involve low probabilities in high-stakes situations. That said, the goal of my post is to remind people that this type of probability is often uncertain, and that they should communicate this with the appropriate humility.

1. That's how I would think about it, at least... it might well be that you're more rational than I, and use probabilities more explicitly. ↩︎

Richard Chappell writes something similar here, better than I could. Thanks Lizka for linking to that post!

Pascalian probabilities are instead (I propose) ones that lack robust epistemic support. They're more or less made up, and could easily be "off" by many, many orders of magnitude. Per Holden Karnofsky's argument in 'Why we can't take explicit expected value estimates literally', Bayesian adjustments would plausibly mandate massively discounting these non-robust initial estimates (roughly in proportion to their claims to massive impact), leading to low adjusted expected value after all.

Maybe I should have titled this post differently, for example "Beware of non-robust probability estimates multiplied by large numbers".

I agree that our different reactions come partly from having different intuitions about the boundaries of a thought experiment. Which factors should one include vs exclude when evaluating answers?

For me, I assumed that the question can't be just about expected values. This seemed too trivial. For simple questions like that, it would be clearer to ask the question directly (e.g., "Are you in favor of high-risk interventions with large expected rewards?") than to use a thought experiment. So I concluded that the thought experiment probably goes a bit further.

If it goes further, there are many factors that might come into play:

• How certain are we of the numbers?
• Are there any negative effects if the intervention fails? These could be direct negative outcomes, but also indirect ones like difficulty to raise funds in the future, reputation loss...
• Are we allocating a small part of a budget, or our total money? Is this a repeated decision or a one-off?

I had no good answers, and no good guesses about the question's intent. Maybe this is clearer for you, given that you mention "the way EA culture has handled thought experiments thus far" in a comment below. I, for one, decided to skip the question :/

This is a great point.

Clearly you are right. That said, the examples that you give are the kind of frequentist probabilities for which one can actually measure rates. This is quite different from the probability given in the survey, which presumably comes from an imperfect Bayesian model with imprecise inputs.

I also don't want to belabor the point... but I'm pretty sure my probability of being stuck by lightning today is far from 0.001%. Given where I live and today's weather, it could be a few orders of magnitude lower. If I use your unadjusted probability (10 micromorts) and am willing to spend $25 to avert a micromort, I would conclude that I should invest $250 in lightning protection today... that seems the kind of wrong conclusion that my post warns about.

I think humility is useful in cases like the present survey question, when a specific low probability, derived from an imperfect model, can change the entire conclusion. There are many computations where the outcome is fairly robust to small absolute estimation errors (e.g., intervention (1) in the question). On the other hand, for computations that depend on a low probability with high sensitivity, we should be extra careful about that probability.

Sorry for having been imprecise in my post -- I wrote the question from memory after having already submitted the survey. I'll change it to "avert".

There is some public information about this here: https://www.givewell.org/charities/amf#Registration

Details vary by country. It's often a process where enumerators go door-to-door and interview the head of household to determine how many people live in a household. There can be some incentives to over-report the number of people, to receive more bednets. However, there is a limit on the number of nets per household (usually 3 or 4), and some of the data is independently verified by a second team of enumerators.