In my experience of being an Australian, “fag” is not a common term of endearment I’ve encountered, except in the sense that general insults are used as terms of endearment (like “shit-for-brains” etc).
DanielFilan
Karma: 1,370
Consider not donating under $100 to political candidates
MATS AI Safety Strategy Curriculum v2
[Question] Favourite vegan luggage?
I guess I should add why I’d like such a BOTEC: I’m broadly skeptical that anti-abortion interventions will turn out to be competitive with animal welfare interventions on a cost-effectiveness basis under the assumption of foetal moral patienthood, given that my impression is that animal welfare has a vastly wider scale (perhaps with exceptions like choosing to not have an abortion or voting for your polity to criminalize abortion).
FWIW I would have appreciated a BOTEC for the cost-effectiveness of various anti-abortion interventions (under the assumption of the wrongness of abortion). You gesture that it’s possible to affect the number of abortions via policy, but this is obviously a pretty limited analysis. Absent this sort of BOTEC, this reads as a case for policy-makers to restrict abortion, rather than an effective altruist case for pro-life/anti-abortion advocacy, as your title promises.
Aum intended to kill thousands of people with sarin gas, and produced enough to do so. But they a) were not able to get the gas to a sufficiently high level of purity, and b) had issues with dispersal. In the 1995 Tokyo subway attack, they ended up killing 13 people, far less than the thousands that they intended.
IIRC b) was largely a matter of the people getting nervous and not deploying it in the intended way, rather than a matter of a lack of metis.
Thanks!
No chance there’s a ~10 word summary of the executive summary? I’m interested but pretty sleep-deprived/jetlagged and found it hard to interpret the table.
(1) Sorry, I was copy-pasting the formula in the spreadsheet, but missed the extra 0.1 factor you added at the end. First of all, I still think the factor of 50 you’re shaving off due to diminishing marginal returns seems quite extreme, given the lack of articulable justification for why it should be so big. I guess the extra factor of 10 is because diet cola exists? I’m not sure why you’re adding that—as I mentioned, the questions you asked people already mentioned that people could substitute to diet sodas. Quoting from the instructions to participants of cell E1 of your survey spreadsheet, “Note that this does not include artificially-sweetened beverages (i.e. Coke Zero, Diet Pepsi, Sprite Zero etc) – you would still be able to drink those.”.
(2) Makes sense. Regarding composition, we could look at polling of soda taxes to get a sense for this. One thing to note is that politicians typically don’t implement these taxes (hence you looking into lobbying for them), suggesting that they’re not very popular. Of the two polls I could find, it seemed like either there were no significant differences between groups, or that Republicans were more opposed to soda taxes than Democrats. Given that the vast majority of American EAs are Democrats, this suggests that the poll could be underrating the disutility of reduced sugar consumption.
(3) Note that many of these factors go both ways—people could be motivated to appear self-abnegating and healthy, think sugary drinks are less healthy than they are (anecdotally, I’ve asked two people how much diabetes would be reduced if sugary drinks didn’t exist, and they both overestimated relative to your cited number), or not think about how sugary drinks are actually OK. It’s indeed plausible that people value their future selves less than they ought to according to standard utilitarianism, but I don’t think it’s a priori clear that they do so by a massive factor.
Beyond potential a prior scepticism as to whether such a significant number of people not dying or suffering ill health from diabetes really is less valuable than loss of freedom to drink sugar drinks
I guess I want to add something here about why one would have the opposite prior: by and large, a decent model of people is that when they make decisions, they roughly weigh costs and benefits to themselves (or follow a policy that they adopted when weighing costs and benefits). Diabetes is mostly a cost to oneself. At least in the US, people are broadly aware that drinking sugary drinks makes you a bit less healthy, via an increased chance of obesity, diabetes, heart disease etc. But people drink them anyway because they’re tasty—that is, in their judgement, the value of being able to drink them is higher than the health risk.
It seems like you have a strong prior that people are wrong about this, and that they significantly underweight the health impacts of soft drinks, such that limiting their intake by 20% is worth it to reduce their risk of getting diabetes by 1%. This isn’t impossible—it could be that people don’t know that sugary drinks are unhealthy (of course, people could also overestimate how unhealthy they are), or that socialized healthcare means that there are massive externalities to diabetes cases—but I didn’t see any arguments to that effect in your executive summary.
Then, using the y=x^0.1 formula, we take (1-d)^0.1 to find that 98% of “freedom of choice” still remains, and correspondingly, there was a 2% reduction.
The number in the sheet is a 0.2% reduction, not a 2% reduction. [EDIT: my bad, it’s a 2% reduction, there’s just another factor of 10 reduction that I mistakenly lumped into that]
I still disagree with your belief that the accuracy of the iterated questions format was lower than the accuracy of the fraction of income format—both questions had standard deviations that were approximately the same multiple of their means.
I think your original strategy of aggregating across the population using the arithmetic mean made sense, and don’t understand what the justification is supposed to be for replacing it with a geometric mean [1]. Concretely, imagine a decision that affects two friends lives, making one 50% worse, and the other 0.005% worse. Presumably you wouldn’t take the geometric mean and say “this basically makes both your lives 0.5% worse, which is not very much”. Instead you might conclude that your friends are different in some way. Similarly, it seems like probably some people like sugary drinks and others don’t, causing significant variation in how much they care about sugary drinks being banned.
As DMR said, that curve seems kind of weird to me—it seems like an unjustified assumption is being used to cut a BOTEC by a factor of 50, which strikes me as suspicious. The real curve is presumably not linear (because otherwise people would buy more sugary drinks on the margin), but intuitively I feel like a factor of 5 adjustment makes way more sense than a factor of 50.
By my analysis of your sheet, if you use a factor of 5 rather than 50 for the decreasing marginal utility, and use the arithmetic mean rather than the geometric mean to aggregate across participants, you get the disutility of freedom as 500% higher than the gains. If you also weight both estimation methods equally, it goes up to 1,100% - which is bigger enough than my BOTEC that I worry you might be making some errors in the opposite direction?
[1] Consider that this analysis is done in the genre of a utilitarian calculation, which usually uses the arithmetic mean of welfare rather than the geometric mean, as is used implicitly in the disease reduction component.
also noticed that there are a number of issues – probability of success, and also substitution with diet coke – which should be factored in
Just reread this—surely these don’t need to be factored in? Probability of success affects the numerator and the denominator equally, and your poll respondents probably already knew about diet coke.
(1) Fair enough re sample (altho it obviously limits how much of a conclusion you can draw). Re: the different variances, I basically dispute that the cash value method has a meaningfully lower variance than the hypothetical sequence method, because the relevant error is relevant to the mean. That said, this factor of 3 is the smallest issue I have with your calculation.
(2) Could you show the calculations (perhaps in a simplified format, like I did)? The tax vs ban seems like it should affect the value of freedom similarly to how it affects the disease burden reduction, and it’s very unclear to me what you’re actually doing to adjust for diminishing marginal returns to freedom—and I’m skeptical that the heavy discount relative to the BOTEC model is justified.
(3) Fair enough! Easy to make this sort of slip-up in such a big survey [EDIT: by “such a big survey” I meant “such a big report”—surveys were just on the mind]
(4) Yeah, the per annum issue was just a problem with my BOTEC, I have no particular reason to think you got it wrong (other than the extreme divergence from the BOTEC).
(5) For what it’s worth I don’t share your skepticism that taxing sugary drinks could be net negative. At any rate: I’d guess the factors you first thought of are probably the most important (deferring to your process of creating the initial report), and I suppose that there are probably further effects that go either way—especially since, as you noted, cost effectiveness analyses tend to look less promising as more effort is put into them.
Sounds like you roughly agree with me − 8.1 / 3.5 = 230%, which is close to 167%. Difference is I use the 5% reduction number for proportion of burden due to sugary drinks, getting 90 mil / 20 = 4.5 mil, 8.1 / 4.5 = 180%, and the rest is error built into these calcs.
Wait: your survey numbers are for DALYs lost per year. So if the disease burden is 90 million DALYs per year, banning sugary drinks gives a benefit of 0.0006 DALYs per person-year, compared to 0.001 DALYs lost per person-year, meaning that the loss of freedom reduces the benefit by 167% (or 500% if you believe this comment). So now I’m really curious how your adjustments are bringing that down so much.
FWIW I’m also suspicious of the 0.001 DALYs per person number.
AFAICT, the way you get it is by combining two methods: method 1 is to ask people a chain of questions like “as a fraction of death, how bad is life imprisonment”, “as a fraction of life imprisonment, how bad is not being able to eat tasty stuff”, “as a fraction of not being able to eat tasty stuff, how bad is not being able to have sugary drinks”, multiply their answers to get how bad losing sugary drinks is as a fraction of dying, and then multiply by the fraction 64⁄74 (for remaining life years? this was opaque to me), to get a DALY loss of 0.016 +/- 0.009 [1]. You then do method 2: ask people how much of their annual income they’d give up to get a 1-year exemption from a ban on drinking sugary drinks, take the binary logarithm of 1 + that fraction, and multiply by 2 to get DALY loss. This gives you a loss of 0.0012 +/- 0.0009 [1]. You then average each respondent’s result from each method to get a per-respondent DALY loss estimate, before aggregating that accross respondents. Because the standard deviation of responses from method 1 is 10 times higher than that of method 2 [2], you weight method 2 10x higher in the per-respondent average, meaning that the overall loss is basically just that of method 2.
But I don’t think you’re right to conclude that method 2 is more accurate than method 1: it’s just that method 2 gives ~10x smaller results for whatever reason, so it makes sense that its error is also ~10x smaller. If you look at the spread in responses as a fraction of the mean response, methods 1 and 2 are pretty close (if anything, it looks like method 1 is a bit more precise). If you instead weighted the methods equally, you would get 3x the per-person DALY loss [3], and if I’m right in the parent comment, that would net out to a 15% reduction in the value of the program.
(also more fundamentally, the fact that the methods give 10x different values suggests that they plausibly are just measuring different things, and we should be unsure which (if either) is actually measuring the disvalue of the loss of freedom to drink sugary drinks)
[1] My error here is the standard error of the mean of each result: basically, how much we’d expect our calculated mean to vary if we resampled. It’s equal to the empirical standard deviation divided by the square root of the number of samples (which is 4).
[2] You also list one benefit of method 1 and one benefit of method 2, which I’m assuming cancel out in your considerations.
[3] Sanity check: the mean of the first method is 10x bigger than the second method, previously we were ~ignoring the first method, now we’re taking the geometric mean, and the geometric mean of 1 and 10 is 3 (because 3^2 is about 10), so this looks right.
Since nobody else has responded, my best guess would be “conditional probability table”.