I was joking ;)
But the distinction between prophylaxis and treatment I think is useful because even if “it doesn’t work” as one or both, it could work for the other and still be helpful.
Evil is when you “know that some things are damaging to someone else, gain no tangible value from doing them (or even expect that their life would be worse off!), know it is not a virtuous act, and do the harmful acts anyway without expecting future good to come from it.”The first failure point is “gain no tangible value.” Imagine any prototypically evil character, maybe a person who is bullying once, maybe a chronic bully, maybe Dr. Evil, maybe Satan. Each of these gains some subjective value from their actions, if not “tangible” value. Either “tangible” is critical here, in which case you have way too narrow a definition of value, or it’s not, in which case it is clear that these people are selfish and pretty legible.What makes them evil is that their value system is so out of whack that they are evil (please just live with the circularity for now, I’m not trying to propose that as a formal definition). So the person who is bullying once and then learning it doesn’t fulfill them that much...they may have done a bad, or even evil, thing, but they aren’t evil! Same of the chronic bully—if they had a bad home life and are coping poorly, their innate value system may still be programmable to avoid evil acts. Dr. Evil is much closer to chronic evil right up until Goldmember (I can’t believe I’m really going with this), when we find out he is a victim of circumstance, which anyone seemingly can be with enough compassion. Satan, well yeah, he’s evil.No one likes or endorses bullying, but you need a definition of evil that has validity, and yours is debatable. But even if accepted, it hardly encompasses a lot of people. You could actually stand to loosen the definition of evil, but you quickly run into selfishness. Construct definition is step one here. And it’ll probably carry value judgments (see: virtue).
Your discussion would suggest that disulfiram might not work at curing alcoholism but could be a useful prophylactic. Lace the drinking water with it and people will avoid alcohol or stop earlier! What could go wrong?
Aleatory and epistemic uncertainty often get wrapped up together so these estimates are not always proper probabilities nor measuresof confidence. You’re separating them, good for you!
There once was a time when people who were obsessed with knowledge (and the appropriate action flowing therefrom) were called scientists. Now they are just adherents to scientism, and the rest of us have to pick up terms to describe our taking the mantle. Where the “rationalist” community seems to be is at the intersection of metacognition and “rational” (whatever that means :P ). Neither describes the movement entirely on its own, but with their powers combined...Interesting post, thanks.
Basic standard microeconomics (supply and demand) is a pretty strong model, so you’re doing great! What you’re missing is formalizing the value or disvalue being pursued or created by the system.Right up until “If you do literally nothing at all,” the discussion was about prices and quantity, but then suddenly we care about aesthetics and infrastructure. Did you know that people would also pay for that, too? This might lead to things like some neighborhoods being more valuable than others and accordingly commanding higher prices for otherwise similar accommodations.If a lot of people want to move to the city because the opportunity is so vast and they aren’t as concerned about aesthetics, developers would develop accordingly. If instead many of these people are pickier, well, developers would be too. This sounds bad because that means we can’t guarantee other people live according to our preferences, but it’s actually good because it’s demand and supply meeting up. Where things go awry is when these market exchanges create externalities that should be internalized by the market participants. If bare wires a strewn across the streets and children are being electrocuted every day, maybe we need a government to enforce some basic regulatory code to take care of that (because in this hypothetical, I guess the neighborhood is populated by selfish singles and the children come from elsewhere to play in these oh so attractive streets, so the problem won’t get fixed otherwise).Yes, inventing a generic government can cover the really bad results (if they occur) from this market arrangement. The risk with this is that people may then seek to enforce their preferences through this government rather than letting the market handle it. That might be fine. Or it might be inefficient, maybe even unjust. “I think apartment complexes should have at least a one-car garage or two parking spaces per unit; I’m also super benevolent so developers can mix and match” leads to an absurd result when the would-be tenants just grin and bear it despite their preference for taking the available public transit or use their bikes. It just becomes a value-suck, raising prices and/or lowering supply, achieving one (foolish) objective to the neglect of the many (important) others.I come from a mountain town—space is scarce. The government decided it would be more efficient, kinda neat in town, and better for tax revenue to implement onerous housing regulations but exempt mixed-use (residential on top, commercial on bottom, and you know it, parking in the back) from some (not all) of those regs. We got a lot more mixed use. The housing filled up since we had a shortage already. The commercial did not, wasting resources and space. This also cratered commercial rent prices, but the new building owners don’t seem to cry about it. Turns out the developers were building residential space; commercial rent would just be gravy since the commercial space was just to get the desired regulatory structure applied. That’s how valuable the residential space was.I can tell you what experts aren’t disagreeing on.
Oh yeah, definitely agree!
The two “direct” causal links are the only ones we would really call “causal” regarding A and B.But I am a big fan of “correlation implies causation.” It might not be between A and B specifically, but it means we’ve been able to detect something happening.Sometimes even non-effects, when theory is strong enough, can indicate causation (though then the usual course of action is to control one of the paths to get an effect that you can talk about and publish). For example, you are about to eat an allergen, which you know causes side effects for you with p=1. You take Benadryl beforehand and have no side effects. There is no “effect” there (post state = pre state), but you can feel pretty sure Benadryl had a suppressing action on the allergen’s effects (and then you would follow-up with experiments where you ate the allergen without Benadryl or took the Benadryl without eating the allergen to see the positive and negative effects separately).
OP’s claim is that intelligence is positively skewed. Counter-points are “most brains are slightly worse” (Donald Hobson) and “you oversample the high-intelligence people, so your claim is biased because of availability” (Ericf).Both of these counter-points agree with, rather than disagree with, lsusr’s point. Most brains are slightly worse implies positive skew and to the extent that lsusr oversamples high-intelligence people, they are underestimating how positively skewed intelligence is yet still conclude it is positively skewed (caveat: as Donald Hobson says, the measurement approach can be really important here, but for the sake of argument let’s say lsusr is talking about latent intelligence, and our measures just need to catch up with the theory).Ericf also makes another interesting point- “variation in low intelligence is less identifiable than variation in high intelligence,” 160 vs. 130 IQ people will act differently, but 40 vs. 70 IQ people won’t so much, or at least the IQ test is better at delineating on the high end than low end. I am no expert on the measurement of intelligence, but this point probably shouldn’t just be taken at face value- for example, individuals with Down’s syndrome consistently have IQs less than 70 and getting below 70 is rare, as expected since IQ is designed to be Gaussian. But the implication of that is that as rare (and therefore difficult to dig into) as low IQs are, high IQs are...equally rare (and therefore difficult to dig into).I agree that OP’s claim should also be subjected to scrutiny -simply saying intelligence is positively skewed doesn’t make it so- but I also don’t find the present set of counter-points either that contradictory or that convincing either. Just my two cents.
FWIW, number sense is definitely a thing in psychology.
Escaped/circulated earlier than officially reported.
FDA Dr. Peter Marks’s reply either indicates his own misunderstanding or that something is wrong with the FDA report! In Table 15 of the FDA’s Moderna report, they report efficacy “in Participants Who Only Received One Dose” (emphasis added and the N’s are correctly not the full trial’s N). 80% (95% CI: 55%, 93%) is a nice round number to tell people, but also we assess two-dose efficacy only after 14 days anyway, so the truly comparable number is 92% (95% CI: 69%, 99%). Now if there are other reasons we shouldn’t trust those numbers, I’d love to see them. They caveat it with it’s not necessarily 80+% effective forever since they only observed single-dosers for a median of 28 days, and the N is definitely lower but still 1000 per group (which is why the confidence intervals are wide). But that gives us pretty high confidence that 14 days after the first dose, the vaccine is effective enough to warrant JABS IN ARMS!
It’s between-subjects, these aren’t real probabilities for individuals. But from a Bayesian standpoint it gives you useful base rates with which to assess risk.
One way to “rewire” your brain is to wire in a quick check- how does selection/stratification/conditioning matter here?But perhaps most important is to think causally. Sure, you can open up associations, but, theoretically, do they make sense? Why would obesity, conditional on having cardiovascular disease, reduce mortality? Addressing why rather than leaping to a bivariate causal conclusion is important. This is why scientists look for mechanisms and mechanism-implicating boundary conditions.
I’m having trouble with it too and I think Zvi misinterpreted it as well- the far right column is the VE.
Indeed, these aren’t controlled experiments at all, but sometimes they are also not policy-sneaking. Sometimes they are just using the phrase “experimenting with” in place of “trying out” to frame policy-implementation. At that point, the decision has already been made to try (not necessarily to assess whether trying is a good idea, it’s already been endorsed as such), and presumably the conditions for going back to the original version are: 1) It leads to obviously-bad results on the criteria “management” was looking at to motivate the change in the first place or 2) It leads to complaints among the underlings.The degree of skepticism, then, really just depends on your prior for whether the change will be effective, just like anything else. Whether there should have been more robust discussion depends either on the polarity of those priors (imagine a boardroom where someone raises the change and no one really objects vs. one where another person suggests forming an exploratory committee to discuss it further), or on whether you believe more people should have been included in the discussion (“you changed the bull pen without asking any of the bulls?!”). It has little to do with the fact that it was labeled an experiment, since again, it’s likely being used as business-speak rather than as a premeditated ploy. I would love to have data on that though- do people who specifically refer to experimentation when they could just use a simpler word tend to use it innocuously or in a sneaky way?
^Not always true, but true often enough that it definitely bears mentioning.
If you invest in an asset that you expect to have a 0% real return and therefore hand you an after-tax real loss, and then you complain the tax system is handing you an after-tax real loss, there’s something wrong there- is it with the tax system?
To the contrary, I think the criticism of post 2 is very on point. But Zvi and I are looking at two different parts: Zvi’s looking at the logic/begging the question part, and I’m looking at the critique. In thought experiments, we can take imagined exogenous changes to be exogenous even though in the real world they’d be endogenous (i.e., we can take them as events rather than outcomes). Later, we can relax that assumption; the endogeneity problem is important for understanding whether the conclusions extend to the real world, but it is not important for understanding what the conclusions are within the thought experiment. So I agree with Zvi that the logic isn’t really an issue here.
However, I do believe this is a bad example (/weak post, Sorry Elizabeth) precisely for the reason AllAmericanBreakfast pointed out- it frames basic economics knowledge as a new insight. Admittedly, the EconLog post that was linked to doesn’t discuss comparative advantage either, but that’s because it’s really just about the “flight to safety” in 2008 where capital has to go somewhere, so it goes to the safest haven- even if that place is on fire, at least it’s not on fire next to a ticking time bomb. But, if you really want to talk about the “benefit not from absolute skill or value at a thing, but by being better at it than anyone else” then you can just consult microeconomics 101 (literally) and read up on absolute vs. comparative advantage. And then a better example of it is what you would find in the textbook (ha, probably Mankiw’s) of English cloth vs. Portuguese wine, which clearly illustrates the concepts.
Or, maybe Elizabeth really wasn’t referring to comparative advantage and more specifically to “when a superlative is applied in a context and the context is later lost.” This might seemingly apply better to the USD (we think of it as a safe haven because we used to think of it as a safe haven), but again the USD is not an apt example here because the context isn’t lost, it just changed (e.g., suppose the USD scores a 10⁄10 at being a currency and things change and now it’s a terrible 3⁄10 but it’s still better than all the rest). The Tallest Pygmy derives its tension from that fact that you think you’ve found someone “tall” but it’s just among the pygmies you’re sampling. The Tallest Pygmy, then, is best understood as getting stuck in a valley at a local, but not global, minimum (gradient descent). Or peaking at a local, but not global, maximum. Sometimes you are fine with local maxima, but if you are optimizing for global maxima, then obviously this creates a problem. May as well go with a classic example instead, which clearly illustrates sampling bias (statistics).
You see this in the academic literature as well where people refer to concepts as “effects.” I think it is a good idea to be skeptical of those findings- not that they are fake, just that more clarity could be gained from understanding the core concept that generates the effect. Elizabeth’s example is not great for comparative advantage, nor for gradient descent/sampling bias. The USD in 2008 is a “lesser of two evils effect,” or really not an effect at all- if you have a choice between 10%, 9%, and 8% returns at equal risk, you choose 10%; if a regime change occurs that makes you choose between 5%, 4.5%, and 4%, you choose 5%. It’s worse than before, but it’s the best around.
LessWrong is a great community to be in, but AllAmericanBreakfast is correct that many posts stumble upon “new” insights that are really just symptomatic of not having done enough research, particularly when it comes to economics. And that’s okay in this forum, we’re all trying to figure this stuff out!
It can *survive* on surfaces for a long enough period of time for it to seem possible. But I think viral load is important and it might not practically be a serious vector for most people and most surfaces, particularly those that are commonly disinfected. It’s amazing how our knowledge went from symptomatic/surface? to pre-symptomatic/aerosol? transmission.
Also a PhD here—read, read, read. You need to know what’s been done to see what the gaps are and how your project would fit in. You will also build up that intuition.
Sure, it’s also helpful to be able to bounce ideas around your network, but the less well-formed the idea is, the more likely it is to go to friends who aren’t just going to shoot you down or for it to get the benefit of the doubt as “early-stage.” You need to get the idea formed to the point where someone can feel comfortable pointing out issues, which will take independent research. You also see that here at LW, where ideas/points are usually more than a paragraph long.