I am a generalist quantitative researcher. I am open to volunteering and paid work. I welcome suggestions for posts. You can give me feedback here (anonymously or not).
Vasco Grilo🔸
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Do you think pain A having a higher intensity than B implies that averting an infinitesimal duration of pain A with an infinitesimal probability is better than averting an astronomically long time of pain B with certainty? If not, what is required for you to believe this besides A having a higher pain intensity than B?
Consider someone holding their hand in hot water for 1 min. If you think there are only 5 pain intensities, what would be the range of temperature for each pain intensity? If the ith pain intensity starts at T_(i − 1), and ends at T_i, what would change so much from temperature T_i_before = T_i − 0.001 ºC to T_i_after = T_i + 0.001 ºC that makes you prioritise averting pain at T_i_after infinitely more than pain at T_i_before? Do you believe empirical studies of people’s preferences would find a few temperatures (4 if you believe in 5 pain intensities) with this property, where people would prefer averting any time in pain at temperature T_i_after over an arbitrarily long time in pain at temperature T_i_before?
Hi Vince.
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This just links to their logo. Here is ACE’s review.
I suspect you would prefer averting A for any value of N (equal to 1 or higher) in principle, but that you believe N cannot take a high value in practice.
I understand I got this right. So, if N could be 1 M, I think you would prefer averting i) 1 year of pain of intensity level 1 M with probability 10^-100 over ii) 10^100 years of pain of intensity level 999,999 with probability 1.
While mathematical models can theoretically increase N to infinity, the subjective reality of suffering is biologically capped because there is a physiological ceiling of the nervous system.
N is supposed to be the number of different pain intensities. One cannot determine the maximum pain intensity M based on N alone. In theory, N can be arbitrarily large for any M. The mean difference Delta = M/N between consecutive pain intensity levels would just tend to 0 as N increases to infinity. For a constant difference between the pain intensity of consecutive intensity levels, the pain intensity of level i would be Delta*i.
The premise of “99.99999999% of X” assumes that pain exists on a perfectly smooth, linear scale that can be infinitely divided.
I agree pain intensities cannot be arbitrarily close. However, consider N = 100. Would you prefer averting, for example, i) 0.1 s of pain of intensity level 100 with probability 10^-100 over ii) 10^100 years of pain of intensity level 99 with probability 1. The expected pain of ii) is 3.12*10^208 (= 10^100*99*1/(0.1/60^2/24/365.25*100*10^-100)) times that of i).
It also seems something worth tracking to me.
Thanks for the post, Lauren. Here is a discussion about the post.
Here is Lauren’s post about this.
Thanks for elaborating. Imagine pain has N levels of intensity (with N equal to 1 or higher). Consider the following pains:
A. Duration of 1 year, intensity level N (the highest), and probability of 10^-100.
B. Duration of 10^100 years, intensity level N − 1 (the 2nd highest), and probability of 1.
The expected pain of B is 10^100*(N − 1)*1/(1*N*10^-100) = 10^200*(1 − 1/N) times that of A. I would prefer averting B for N equal to 2 or higher. Even for N = 2, the expected pain of B is 5*10^199 times that of A.
For which values of N (if any) would you prefer averting B over A? I understand you would prefer averting A at least for N = 5. I suspect you would prefer averting A for any value of N (equal to 1 or higher) in principle, but that you believe N cannot take a high value in practice. If so, why?
Hi David. I would be curious to know your thoughts on my reply to titotal. In the post, “more pain” is supposed to mean “more pain, and all else equal”. If the event that leads to additional pain comes with benefits, it could overall increase welfare. In contrast, a “speck of dust in the eye” is supposed to represent something which decreases welfare very little (considering all effects).
That said, our confidence in our own position is not high. So, we’d be willing to fund things to challenge our own views: If we had sufficient funding from folks interested in the question, Arthropoda would fund a grant round specifically on soil invertebrate sentience and relevant natural history studies (especially in ways that attempt to capture the likely enormous range of differences between species in this group). Currently, much of our grant-making funds are restricted (at least informally) to farmed insects and shrimp, so it’s not an option.
Mal and Bob, what would you fund with 50 k$ of unrestricted funding under expectational total hedonistic utilitarianism? How about under your own moral views? Why? I understand you are open to funding research on soil animals, but I wonder whether you would prefer funding more research on farmed invertebrates.
Got it. Consider these 2 pains:
A. Duration of 1 s, intensity of X, and probability of 10^-100.
B. Duration of 10^100 years, intensity of 99.99999999 % of X, and probability of 1.
Would you prefer averting A over B? In other words, would you prefer A) an infinitesimal chance of decreasing 1 s of very intense pain over B) certainly decreasing 10^100 years of an infinitesimaly less intense pain?
Thanks for the thoughts, Elif.
I agree annoying and excruciating pain have very different properties. However, it does not follow that an arbitrarily short time in excruciating pain is much worse than an arbitrarily long time in annoying pain? Liquid water and ice have different properties, but, for example, their mass and temperature can still be quantitatively compared. I do not think analogies with physics illustrate that some pain intensities cannot be quantitatively compared.
Would you prefer 10 years in annoying pain over a probability of 10^-100 of 0.1 s in excruciating pain? If so, what do you think about the questions I asked here?
Hi Elif. Thanks for the comment.
But one might simply reject these frameworks in favor of a person-affecting view, which I find far more intuitive.
Aggregation across existing individuals still matters in person-affecting views.
If no single observer in the universe experiences a ‘catastrophe,’ can we truly say a catastrophe has occurred?
It depends on how catastrophe is defined. I think most people would consider a catastrophe all life on Earth dying painlessly, even though no one would experience anything in the process.
I don’t know about you but I would never take that gamble.
Would you take the gamble if it involved 1 min of excruciating pain? If not, would you want the painless death of people who have a probability of experiencing more than 1 min of excruciating pain in their real future higher than 1 in 1 trillion, 10^-12, to eliminate the risk of them experiencing excruciating pain? I think the probability of a random person experiencing more than 1 min of excruciating pain in their remaining life is much higher than 10^-12. The probability of dying in a road injury in high income countries in 2023 was 8.61*10^-5. So a probablity of 0.1 % of experiencing more than 1 min of excruciating pain in a road injury death would result in an annual probability of a random person in high income countries experiencing more than 1 min of excruciating pain of at least 8.61*10^-8 (= 8.61*10^-5*0.001), 86.1 k (= 8.61*10^-8/10^-12) times as large as 1 in 1 trillion. “At least” because there are other events besides road injury deaths which could lead to more than 1 min of excruciating pain.
I’m not sure what it means for a candidate to be “more cost-effective”, but I assume it equates to “better at their job” (assuming a fixed salary).
Yes. I meant more impactful per unit cost (accounting for the direct impact of the job, and its time and financial costs).
My impression, from spending a lot of time in EA orgs and working on several hiring rounds, is that the difference between “best candidate” and “second-best candidate” is often well over 10% (though, to be fair, orgs may be wrong about which candidate is best).
The expected difference will tend to be smaller than the observed difference (the best candidates will tend to regress more towards the mean). I do not know how much this matters, and the extent to which organisations try to account for it, but I guess you are right that the most cost-effective candidate are often more than 10 % more cost-effective than the 2nd most cost-effective. Donating more 10 % of the gross salary to an organisation 10 to 100 times as cost-effective at the margin as A would still be 2 (= 0.1*10/0.5) to 20 (= 0.1*100/0.5) times as impactful as joining A for an alternative hire 50 % as impactful who would get the same salary.
Your model seems to assume that a donor will support the most cost-effective intervention in an area. But if less cost-effective interventions have a lot more jobs, doesn’t that imply they also have more funding, and thus that the average donor is more likely to support them?
Great point. I would just say “random funding” instead of “average donor”. I see now that the vast majority of people considering joining A would not find the comparison I made just above relevant. If they thought there was another organisation 10 to 100 times as cost-effective at the margin as A, they would most likely only consider joining organisations significantly more cost-effective at the margin than A.
The implicit assumption I’m reading from your model is that someone’s impact within a job is worth exactly what they are paid.
Yes, I am making this assumption. I think it roughly applies to new jobs. My thinking is that funders should fund organisations until they are indifferent between funding them more or not. If a funder thought that an organisation spending 100 k$/year more on a new job would be worth 1 M$/year more to the funder, they would be leaving 900 k$/year (= 1*10^6 − 100*10^3) of impact on the table, in the sense that giving 100 k$/year more to the organisation would be as impactful as the funder having 900 k$/year more to spend.
In my experience, EA orgs tend to describe themselves as much more limited by talent than funding.
I wonder whether this depends on the audience. Organisations have an incentive to highlight talent constraints in hiring efforts (to get more applicants), and funding constraints in fundraising (to get more funding).
I am not sure what organisations mean when they say they are limited by funding or talent. Organisations are always constrained by both funding and talent to some extent. Additional funding can be used to retain and acquire talent via higher salaries and greater spending on hiring, including on field-building efforts like fellowships. I believe it would be better for organisations to say how much they value the best candidates over the 2nd best candidates (for roles they are hiring for) in terms of additional funding instead of just saying they are funding or talent constrained.
I think someone who changes their career is more likely to start donating than vice-versa.
I agree. In addition, I think people who change to more impactful jobs will tend to donate to more cost-effective organisations.
If you think most people make far more impact by donating than working, and that it’s much easier to convince people to donate than change careers, this factor won’t change much; you’d still prefer lots of new donors to a small number of new workers who also donate. But I still thought it was worth noting.
Makes sense. I do not know whether marginal funding should mostly go towards adocating for cost-effective donations, including via earning to give, or careers.
I appreciate the impetus to think about the question!
Likewise.
Here is for example an article on sauna that I have read:
https://pdf.sciencedirectassets.com/271341/1-s2.0-S0531556521X00095/1-s2.0-S05315565210029[…]lY3QuY29t&ua=0a0b560605045c52&rr=9aad34daa873be38&cc=dk
For what I understand, is that there is no extremely hard evidence for benefits of sauna/icebathing, but there is a lot of research that points in that direction.Someone I know sent me this. Here is my reply on 13 December 2025 covering the study, which I think is in the spirit of Seth’s post.
Thanks for sharing, [name]! To clarify, I was mostly sceptical about the ice bathing (I looked into this a bit because my father does 10 ºC bathing sometimes, and was claiming it has lots of benefits). I have now looked into the study above (“Sauna use as a lifestyle practice to extend healthspan”). I think the evidence presented there is very weak. In terms of data from interventional studies, which involve comparing a group which receives a treatment (like more sauna) with a control group, and are the type of study which offers the strongest evidence, they mention the following (I searched the paragraphs with “interv”):
“Findings from a small intervention study in rodents demonstrated that local heat application during an immobilization period decreased muscle atrophy by 37% compared to a sham treatment”. Very weak evidence because it is about rodents.
“A randomized controlled trial that examined the effects of sauna use in 24 patients with ischemic heart disease with chronic total coronary artery occlusion who had not responded to non-surgical procedures and had failed or were not candidates for percutaneous coronary intervention demonstrated that 15 waon sessions given over a 3-week period improved the patients’ vascular endothelial function as measured by flow-mediated dilation of the brachial artery. No significant improvements were observed in the control group that received standard medical care (Sobajima et al., 2013)”. Very weak evidence because:
The sample size is tiny (24 people in the treatment group).
The treated people are very special people (so sauna being beneficial for them does not imply it has benefits for a random healthy person).
They did not measure mortality (I do not know the extent to which this correlates well with “flow-mediated dilation of the brachial artery”).
“A small intervention study investigated the effects of repeat sauna use on endurance and other physiological effects in 6 male distance runners. The findings showed that one 30-minute sauna session twice a week for 3 weeks post-workout increased the time that it took for the study participants to run until exhaustion by 32% compared to their baseline”. Very weak evidence because:
The sample size really is super small (6 people)!
The treated people are not random people (they are distance runners).
They did not measure mortality (I do not know the extent to which this correlates with “time that it took for the study participants to run until exhaustion”).
“Another randomized controlled trial found that endurance training in a sauna suit led to improved performance and respiratory measures, including VO2max. The authors speculated that the improved performance time for the sauna suit group was due to improved VO2max and increased capacity for thermoregulation. For example, they noted that sweat rate during a heated 5 km time trial increased in the post-intervention group but not the control group (Van de Velde et al. 2017)”.
I am confused. The summary of this stufy does not even mention “sauna”.
In any case, I guess the same points about the study above apply. A “sauna suit” most likely implies a very small sample size, and they did not measure mortality.
“A small intervention study in humans found that daily heat treatments applied locally to muscle during 10 days of immobilization prevented the loss of mitochondrial function, increased HSP levels, and attenuated skeletal muscle atrophy by 37% compared to a sham treatment group (Hafen et al., 2019)”. Very weak evidence because:
By small, they mean “23 healthy volunteers”.
They did not study realistic conditions. “This study investigated the effects of daily heat therapy on human skeletal muscle subjected to 10 days of immobilization”. The question is whether sauna helps random healthy people longterm, not whether they temporarily benefit people who were spending 10 days without moving.
This is not about sauna. It is about local heating. “daily 2-h heat treatment using pulsed shortwave diathermy”.
They did not measure mortality.
Hi James.
NPS of 63 (mean recommendation: 8.86/10).
In case anyone is wondering, NPS stands for net promoter score, and is equal “fraction of people giving a score of 9 or 10 (promoters)”—“fraction of people giving a score of 0 to 6 (detractors)”.
Hi Ozy.
By looking at this picture, you can see that we’re pretty certain that humans are conscious and very certain that ELIZA is not conscious. We’re uncertain about whether chickens and 2024 LLMs are conscious.
These conclusions only hold for a prior probability of consciousness of 1⁄6. I think this prior is very arbitrary. So I believe the results for the (posterior) probability of consciousness are also very arbitrary.
We are pretty certain about how uncertain we are about whether 2024 LLMs are conscious (we think there’s about a 10% chance they’re conscious). But we’re not only uncertain about whether chickens are conscious, we’re very uncertain about how uncertain we are about whether chickens are conscious.
The model underestimates the uncertainty of the distributions of the probability of consciousness. The weights of the stances are set to point estimates. However, Figure 7 of the report shows the 13 experts surveyed were very uncertain about the weights. I would set the weights of the stances to very wide distributions to represent the very high model uncertainty.
Hi Aaron. Nice post.
In the golden age of public giving, the EA community spent most of its time thinking about where to give. Now, it’s more about finding the right job. That’s appropriate; the average person can make a much bigger impact by working than giving.
Do you mean random people in the whole population can have a much greater impact through work than giving? I find this hard to believe. Benjamin Todd thinks “it’s defensible to say that the best of all interventions in an area are about 10 times more effective than [as effective as] the mean, and perhaps as much as 100 times”, which is in agreement with the cost-effectiveness of interventions following a heavy-tailed distribution. If so, jobs were uniformly distributed across interventions, and a person in a random job within an area were 10 % more cost-effective than the 2nd best candidate for their job, them donating 10 % more of their gross salary to the best interventions in the area could have 10 (= 0.1*10/0.1) to 100 (= 0.1*100/0.1) times as much impact through donations as through work. In reality, I assume there are more jobs in less cost-effective interventions, as the best interventions only account for a small fraction of the overall funding. Based on Ben’s numbers, if there are 10 times as many people in jobs as cost-effective as a random one as in the most-effective jobs, a person in a random job within an area who is 10 % more cost-effective than the 2nd best candidate for their job, and donates 10 % more of their gross salary to the best interventions in the area is 100 (= 10*10) to 1.00 k (= 100*10) times as impactful as a person in the same job not donating.
Here’s a list of examples.
Nitpick. The link links to your post instead of the appendix.
Hi titotal. A “speck of dust in the eye” is supposed to represent something which decreases welfare very little (considering all effects, including decreasing boredom), thus being very slightly bad, and worth avoiding (all else equal). So one can interpret premise 1 (“mild pain is bad”) as “mildly decreasing welfare is bad”. I believe the arguments in the post work for an arbitrarily small decrease in welfare. Do you agree?
I replied here.