Dan_Keys

Karma: 638

Dan_Keys 27 Jan 2026 6:13 UTC
5 points
1 ∶ 0
in reply to: Thomas Kwa🔹’s comment on: A sliding scale for donation percentage
The appendix of the ebook (pdf, p. 218 & 221) suggests structuring it like tax brackets, giving (as a percentage of adjusted gross income):
1% of the first $81,000, 5% of the next $59,000, 10% of the next $180,000, 15% of the next $160,000, 20% of the next $1,520,000, 25% of the next $9,000,000, 33.3% of the next $42,000,000, and 50% of the remainder

Dan_Keys 3 Sep 2025 6:20 UTC
14 points
4 ∶ 0
in reply to: Davidmanheim’s comment on: I bet superforecaster David Manheim 2 k$ that the unemployment rate in the United States in 2027 will be lower than 8 %
I would rather see people make bets that they think are very profitable (relative to the size of the bet).
There’s this idea that betting on your beliefs is epistemically virtuous, which sometimes leads people to be so eager to bet that they make bets at odds that are roughly neutral EV for them. But I think the social epistemic advantages of betting mostly depend on both parties trying to make bets where they think they have a significant EV edge, so sacrificing your EV to get some sort of bet made is also sacrificing the epistemic spillover benefits of the bet.

Dan_Keys 25 Aug 2025 20:44 UTC
4 points
0 ∶ 0
in reply to: Hugh P’s comment on: Hugh P’s Quick takes
Wouldn’t a person’s “welfare footprint” also include, e.g., all the cases where they brightened someone’s life a little bit by having a pleasant interaction with them? The purpose (“different animal products have vastly different welfare harms”) seems fairly narrow but the term suggests something much broader.

Dan_Keys 7 Aug 2025 2:34 UTC
9 points
4 ∶ 0
on: Alcohol is so bad for society that you should probably stop drinking
I expect the causal effect to be pretty weak—if some light drinkers become nondrinkers, I guess that would lead to some reduction in the amount of heavy drinking, but not very much.
There are larger social influences from “the 5 closest people in your life [being] heavy drinkers” or from social rituals that actively pressure people into heavy drinking, but if the recommendation is for light drinkers to become nondrinkers that doesn’t directly touch either of those causal pathways.

Dan_Keys 10 Jul 2025 1:19 UTC
23 points
9 ∶ 4
in reply to: Raemon’s comment on: Don’t Eat Honey
I basically disagree with this take on the discussion.
Most clearly: this post did generate a lot of pushback. It has more disagree votes than agree votes, the top comment by karma argues against some of its claims and is heavily upvoted and agree-voted, and it led to multiple response posts including one that reaches the opposite conclusion and got more karma & agree votes than this one.
Focusing on the post itself: I think that the post does a decent job of laying out the reasoning for its claims, and contains insights that are relevant and not widely considered/discussed, especially for readers who already (e.g.) think that fish matter to a non-negligible extent and are willing to avoid eating salmon because of that. It takes a lot more bee-days to make a serving of honey than salmon-days to make a serving of salmon, and when Rethink Priorities looked into moral weights they found as about as strong a case for bee sentience/welfare as they did for carp or salmon. So the claim that one should avoid eating honey for basically the reasons given in the post seems like a plausible hypothesis, especially conditional on the views that lead many EAs to avoid eating factory farmed chicken or salmon, and it seems good that people are thinking through that claim.
I don’t buy this post’s conclusion, for reasons that are pretty well covered in existing responses. And I agree that there are some epistemic problems with the way it lays out its arguments, such as using a kilogram-to-kilogram comparison instead of serving-to-serving, incorrectly claiming that RP found that bees display “every” behavioral proxy of consciousness, and its choices about when to label estimates as being “conservative”. And it’s not good that these all point in the same direction, of overstating the case for avoiding honey.
So we at least have a fair amount of agreement about the post itself. But turning it into an exhibit of a general EA-vegan-specific problem feels like a stretch, especially given the response it has received. And even just considering the post by itself, there are posts which have gotten a positive reception on LW that I’ve found to be worse-argued than this. (Though perhaps that could be flipped around to say that there are some bad epistemic patterns in both intellectual subcultures.)

Dan_Keys 29 Jun 2025 1:24 UTC
2 points
0 ∶ 0
in reply to: Toby Tremlett🔹’s comment on: Interstellar travel will probably doom the long-term future
I’m unsure how to interpret “will probably doom”. 2 possible readings:
1. A highly technologically advanced civilization that tries to get really big will probably wind up wiping itself out due to the dynamics in this post. More than half of all highly technologically advanced civilizations that grow really big go extinct due to drastically increasing their attack surface to existential threats.
2. The following claim is probably true: a highly technologically advanced civilization that tries to get really big will almost certainly wind up wiping itself out due to the dynamics in this post. Almost every very large, highly technologically advanced civilization that grows big has a doom-level event spawn in a pocket of the civilization and spread to the rest of it.
The 2nd reading is big if true—it implies that the EV of the future arising from our civilization is much lower than it would otherwise seem, and that civilizations might be better off staying small—but I disagree with it.
For the first one I’m on the agree side, but it doesn’t change the overall story very much.

Dan_Keys 29 Jun 2025 1:18 UTC
7 points
0 ∶ 0
in reply to: Jelle’s comment on: Interstellar travel will probably doom the long-term future
Causality moves at c, so if we have probes moving away from each other at nearly 2c, that suggests extinction risk could be permanently reduced to zero.
This isn’t right. Near-speed-of-light movement in opposite directions doesn’t add up to above speed of light relative movement. e.g., Two probes each moving away from a common starting point at 0.7c have a speed relative to each other of about 0.94c, not 1.4c, so they stay in each other’s lightcone.
(That’s standard special relativity. I asked o3 how that changes with cosmic expansion and it claims that, given our current understanding of cosmic expansion, they will leave each other’s lightcone after about 20 billion years.)

Dan_Keys 12 Mar 2025 21:30 UTC
4 points
3 ∶ 0
in reply to: Toby Tremlett🔹’s comment on: Announcing: Existential Choices Debate Week (March 17-24)
How about ‘On the margin, work on reducing the chance of our extinction is the work that most increases the value of the future’?
As I see it, the main issue with the framing in this post is that the work to reduce the chances of extinction might be the exact same work as the work to increase EV conditional on survival. In particular, preventing AI takeover might be the most valuable work for both. In which case the question would be asking to compare the overall marginal value of those takeover-prevention actions with the overall marginal value of those same actions.
(At first glance it’s an interesting coincidence for the same actions to help the most with both, but on reflection it’s not that unusual for these to align. Being in a serious car crash is really bad, both because you might die and because it could make your life much worse if you survive. Similarly with serious illness. Or, for nations/cities/tribes throughout history, losing a war where you’re conquered could lead to the conquerors killing you or doing other bad things to you. Avoiding something bad that might be fatal can be very valuable both for avoiding death and for the value conditional on survival.)

Dan_Keys 13 Feb 2025 2:48 UTC
5 points
2 ∶ 0
on: The standard case for delaying AI appears to rest on non-utilitarian assumptions
Nate Soares’ take here was that an AI takeover would most likely lead to an “unconscious meh” scenario, where “The outcome is worse than the “Pretty Good” scenario, but isn’t worse than an empty universe-shard” and “there’s little or no conscious experience in our universe-shard’s future. E.g., our universe-shard is tiled with tiny molecular squiggles (a.k.a. “molecular paperclips”).” Whereas humanity boosted by ASI would probably lead to a better outcome.
That was also the most common view in the polls in the comments there.

Dan_Keys 13 Nov 2024 5:35 UTC
4 points
2 ∶ 0
on: “Direct cash looks 3-4x more cost-effective in a new GiveWell assessment”
Wouldn’t the economic spillover effects depend on macroeconomic conditions? Government stimulus is more useful when there is more slack in the economy and more inflationary when there’s a tight labor market. I’d expect cash transfers to be similar.
I don’t know the conditions in the specific places studied, but in a lot of places there was significant slack in the economy from the Great Recession until Covid, and the labor markets are now tighter. So studies conducted in the 2010s might overestimate the present-day net benefits of economic spillovers.

Dan_Keys 19 Aug 2024 20:19 UTC
1 point
0 ∶ 0
on: Does “Ultimate Neartermism” via Eternal Inflation dominate Longtermism in expectation?
This sounds like one of those puzzles of infinities. If you take the limits in one way then it seems like one infinity is bigger than another, but if you take the limits a different way then the other infinity seems bigger.
A toy version: say that things begin with 1 bubble universe at time 0 and proceed in time steps, and at time step k, 10^k new bubble universes begin. Each bubble universe lasts for 2 time steps and then disappears. This continues indefinitely.
Option A: each bubble universe has a value of 1 in the first time step of its existence and a value of 5 in its second time step. (Then it disappears, or forever after has value 0.)
Option B: each bubble universe has a value of 3 in the first time step of its existence and a value of 1 in its second time step. (Then it disappears, or forever after has value 0.)
This has the same basic structure as the setup in the post, though with much smaller numbers.
We could try summing across all bubble universes at each time step, and then taking the limit as the total number of time steps increases without bound. Option B is 3x as good in the zeroth time step (value of 3 vs. 1), 2.125x as good through the next time step (value of 34 vs. 16), about 2.072x as good through the next time step (value of 344 vs. 166), and in the limit as the number of time steps increases without bound it is 2.0666… times as good (31/15). That is how this post sets up its comparison of infinities (with larger numbers so the ratio would be much more lopsided).
Instead, we could try summing within each bubble universe across all of its time steps, and then sum across all complete bubble universes. Each bubble universe has a total value of 6 in Option A vs. 4 in Option B, so Option A is 1.5x as good for each of them. Option A is 1.5x as good for the first bubble universe that appears (6 vs. 4), and for the first 11 bubble universes it is 1.5x as good (66 vs. 44), and for the first 111 bubble universes it is 1.5x as good (666 vs. 444), and if you take the limit as the number of bubble universes increases without bound it is 1.5x as good. This matches the standard longtermist argument (which has larger numbers so the ratio would be more lopsided).

Dan_Keys 10 Jun 2024 20:13 UTC
9 points
2 ∶ 1
on: Reducing the nearterm risk of human extinction is not astronomically cost-effective?
I disagree. One way of looking at it:
Imagine many, many civilizations that are roughly as technologically advanced as present-day human civilization.
Claim 1: Some of them will wind up having astronomical value (at least according to their own values)
Claim 2: Of those civilizations that do wind up having astronomical value, some will have gone through near misses, or high-risk periods, when they could have gone extinct if things had worked out slightly differently
Claim 3: Of those civilizations that do go extinct, some would have had wound up having astronomical value if they had survived that one extinction event. These are civilizations much like the ones in claim 2, but who got hit instead of getting a near miss.
Claim 4: Given claims 1-3, and that the “some” civilizations described in claims 1-3 are not vanishingly rare (enough to balance out the very high value), the expected value of averting a random extinction event for a technologically advanced civilization is astronomically high.
Then to apply this to humanity, we need something like:
Claim 5: We don’t have sufficient information to exclude present-day humanity from being one of the civilizations from claim 1 which winds up having astronomical value (or at least humanity conditional on successfully navigating the transition to superintelligent AI and surviving the next century in control of its own destiny)

Dan_Keys 23 Apr 2024 0:50 UTC
2 points
0 ∶ 0
on: Extinction risk and longtermism: a broader critique of Thorstad
I’ve raised related points here, and also here with followup, about how exponential decay with a fixed decay rate is not a good model to use for estimating long-term survival probability.

Dan_Keys 26 Feb 2024 5:33 UTC
30 points
5 ∶ 1
on: UPDATE: Critical Failures in the World Happiness Report’s Model of National Satisfaction
Does your model without log(GNI per capita) basically just include a proxy for log(GNI per capita), by including other predictor variables that, in combination, are highly predictive of log(GNI per capita)?
With a pool of 1058 potential predictor variables, many of which have some relationship to economic development or material standards of living, it wouldn’t be surprising if you could build a model to predict log(GNI per capita) with a very good fit. If that is possible with this pool of variables, and if log(GNI per capita) is linearly predictive of life satisfaction, then if you build a model predicting life satisfaction which can’t include log(GNI per capita), it can instead account for that variance by including the variables that predict log(GNI per capita).
And if you transform log(GNI per capita) into a form whose relationship with life satisfaction is sufficiently non-linear, and build a model which can only account for the linear portion of the relationship between that transformed variable and life satisfaction, then within that linear model those proxy variables might do a much better job than transformed log(GNI per capita) of accounting for the variance in life satisfaction.

Dan_Keys 24 Jan 2024 21:17 UTC
4 points
1 ∶ 0
in reply to: Otto’s comment on: Is fear productive when communicating AI x-risk? [Study results]
It looks like the 3 articles are in the appendix of the dissertation, on pages 65 (fear, Study A), 72 (hope, Study B), and 73 (mixed, Study C).

Dan_Keys 17 Nov 2023 19:11 UTC
5 points
0 ∶ 0
on: What social science research do you want to see reanalyzed?
The effect of health insurance on health, such as the old RAND study, the Oregon Medicaid expansion, the India study from a couple years ago, or whatever else is out there.
Robin Hanson likes to cite these studies as showing that more medicine doesn’t improve health, but I’m skeptical of the inference from ‘not statistically significant’ to ‘no effect’ (I’m in the comments there as “Unnamed”). I would like to see them re-analyzed based on effect size (e.g. a probability distribution or confidence interval for DALY per $).

Dan_Keys 10 Nov 2023 22:48 UTC
4 points
1 ∶ 0
in reply to: Vasco Grilo🔸’s comment on: Rethink Priorities’ Cross-Cause Cost-Effectiveness Model: Introduction and Overview
I’d guess that this is because an x-risk intervention might have on the order of a ¹⁄_100,000 chance of averting extinction. So if you run 150k simulations, you might get 0 or 1 or 2 or 3 simulations in which the intervention does anything. Then there’s another part of the model for estimating the value of averting extinction, but you’re only taking 0 or 1 or 2 or 3 draws that matter from that part of the model because in the vast majority of the 150k simulations that part of the model is just multiplied by zero.
And if the intervention sometimes increases extinction risk instead of reducing it, then the few draws where the intervention matters will include some where its effect is very negative rather than very positive.
One way around this is to factor the model, and do 150k Monte Carlo simulations for the ‘value of avoiding extinction’ part of the model only. The part of the model that deals with how the intervention affects the probability of extinction could be solved analytically, or solved with a separate set of simulations, and then combined analytically with the simulated distribution of value of avoiding extinction. Or perhaps there’s some other way of factoring the model, e.g. factoring out the cases where the intervention has no effect and then running simulations on the effect of the intervention conditional on it having an effect.

Dan_Keys 4 Nov 2023 17:46 UTC
58 points
3 ∶ 0
on: Clean Water—the incredible 30% mortality reducer we can’t explain
I believe the paper you’re referring to is “Water Treatment And Child Mortality: A Meta-Analysis And Cost-effectiveness Analysis” by Kremer, Luby, Maertens, Tan, & Więcek (2023).
The abstract of this version of the paper (which I found online) says:
We estimated a mean cross-study reduction in the odds of all-cause under-5 mortality of about 30% (Peto odds ratio, OR, 0.72; 95% CI 0.55 to 0.92; Bayes OR 0.70; 95% CrI 0.49 to 0.93). The results were qualitatively similar under alternative modeling and data inclusion choices. Taking into account heterogeneity across studies, the expected reduction in a new implementation is 25%.
That’s a point estimate of a 25-30% reduction in mortality (across 3 methods of estimating that number), with a confidence/credible interval that has a lower bound of a 7-8% reduction in mortality. So, it’s a fairly noisy estimate, due to some combination of the noisiness of individual studies and the heterogeneity across different studies.
That interval for the reduction in mortality just barely overlaps with your number that “Sub-saharan Africa diarrhoea causes 5-10% of child mortality.” (The overlap might be larger if that rate was higher than 5-10% in the years & locations where the studies were conducted.)
So it could be that the clean water interventions prevent most children’s deaths from diarrhoea and few other deaths, if the mortality reduction is near the bottom of the range that Kremer & colleagues estimate. Or they might prevent a decent chunk of other deaths, but not nearly as many as your Part 4 chart & list suggest, if the true mortality reduction is something like 15%.
There is generally also a possibility of a meta-analysis giving inflated results, due to factors like publication bias affecting which studies they include or other methodological issues in the original studies, which could mean that the true effect is smaller than the lower bound of their interval. I don’t know how likely that is in this case.
Here’s a more detailed look at their meta-analysis results:

Dan_Keys 26 Oct 2023 23:53 UTC
37 points
2 ∶ 0
on: UK Prime Minister Rishi Sunak’s Speech on AI
More from Existential Risk Observatory (@XRobservatory) on Twitter:
It was a landmark speech by @RishiSunak: the first real recognition of existential risk by a world leader. But even better are the press questions at the end:
@itvnews: “If the risks are as big as you say, shouldn’t we at the very least slow down AI development, at least long enough to understand and control the risks.”
@SkyNews: “Is it fair to say we know enough already to call for a moratorium on artificial general intelligence? Would you back a moratorium on AGI?”
Sky again: “Given the harms and the risk you pointed out in this report, and some of those are profound, surely there must be some red lines we can draw at this point. Which ones are yours?”
@TheSun: “You say we shouldn’t be losing sleep over this stuff. If not, why not?”
@theipaper: “You haven’t really talked about whether your government is actually going to regulate. Will there be an AI Bill or similar on the The King’s Speech?”
iNews again: “On the details of that regulation: does the government remain commited to this idea of responsible scaling, whereby you sort of test models after they’re being developed, or is it time to start thinking about how you intervene to stop the most dangerous models being developed at all?”
Who would have thought one year ago? The public debate about AI xrisk so far outdoes everyone’s expectations. Next step: convincing answers.
https://www.youtube.com/watch?v=hSup6mgNhzQ

Dan_Keys 23 Oct 2023 21:11 UTC
14 points
3 ∶ 0
in reply to: Dan_Keys’s comment on: How bad would human extinction be?
One way to build risk decay into a model is to assume that the risk is unknown within some range, and to update on survival.
A very simple version of this is to assume an unknown constant per-century extinction risk, and to start with a uniform distribution on the size of that risk. Then the probability of going extinct in the first century is ¹⁄₂ (by symmetry), and the probability of going extinct in the second century conditional on surviving the first is smaller than that (since the higher-risk worlds have disproportionately already gone extinct) - with these assumptions it is exactly ¹⁄₃. In fact these very simple assumptions match Laplace’s law of succession, and so the probability of going extinct in the nth century conditional on surviving the first n-1 is 1/(n+1), and the unconditional probability of surviving at least n centuries is also 1/(n+1).
More realistic versions could put more thought into the prior, instead of just picking something that’s mathematically convenient.
What links here?
- Dan_Keys's comment on Extinction risk and longtermism: a broader critique of Thorstad by Matthew Rendall (23 Apr 2024 0:50 UTC; 2 points)