I don't think I have any argument that it's unlikely aliens are screwing with us—I just feel it is, personally.
I definitely don't assume our sensors are good enough to detect aliens. I'm specifically arguing we aren't detecting alien aircraft, not that alien aircraft aren't here. That sound like a silly distinction, but I'd genuinely give much higher probability to "there are totally undetected alien aircraft on earth" than "we are detecting glimpses of alien aircraft on earth."
Regarding your last point, I totally agree those things wouldn't explain the we...
I know that the mainstream view on Lesswrong is that we aren't observing alien aircraft, so I doubt many here will disagree with the conclusion. But I wonder if people here agree with this particular argument for that conclusion. Basically, I claim that:
As a side note: I personally feel that P[observat...
I get very little value from proofs in math textbooks, and consider them usually unnecessary (unless they teach a new proof method).
I think the problem is that proofs are typically optimized for "give most convincing possible evidence that the claim is really true to a skeptical reader who wants to check every possible weak point". This is not what most readers (especially new readers) want on a first pass, which is "give maximum possible into why this claim is true for to a reader who is happy to trust the author if the details don't give extra intuition." At a glance, infinite Napkin seems to be optimizing much more for the latter.
If you're worried about computational complexity, that's OK. It's not something that I mentioned because (surprisingly enough...) this isn't something that any of the doctors discussed. If you like, let's call that a "valid cost" just like the medical risks and financial/time costs of doing tests. The central issue is if it's valid to worry about information causing harmful downstream medical decisions.
I might not have described the original debate very clearly. My claim was that if Monty chose "leftmost non-car door" you still get the car 2/3 of the time by always switching and 1/3 by never switching. Your conditional probabilities look correct to me. The only thing you might be "missing" is that (A) occurs 2/3 of the time and (B) occurs only 1/3 of the time. So if you always switch your chance of getting the car is still (chance of A)*(prob of car given A) + (chance of B)*(prob of car given B)=(2/3)*(1/2) + (1/3)*(1) = (2/3).
One difference (outside the...
Just to be clear, when talking about how people behave in forums, I mean more "general purpose" places like Reddit. In particular, I was not thinking about Less Wrong where in my experience, people have always bent over backwards to be reasonable!
I have two thoughts related to this:
First, there's a dual problem: Given a piece of writing that's along the Pareto frontier, how do you make it easy for readers who might have a utility function aligned with the piece to find it.
Related to this, for many people and many pieces of writing, a large part of the utility they get is from comments. I think this leads to dynamics where a piece where the writing that's less optimal can get popular and then get to a point on the frontier that's hard to beat.
I loved this book. The most surprising thing to me was the answer that people who were there in the heyday give when asked what made Bell Labs so successful: They always say it was the problem, i.e. having an entire organization oriented towards the goal of "make communication reliable and practical between any two places on earth". When Shannon left the Labs for MIT, people who were there immediately predicted he wouldn't do anything of the same significance because he'd lose that "compass". Shannon was obviously a genius, and he did much more after than most people ever accomplish, but still nothing as significant as what he did when at at the Labs.
I thought this was fantastic, very thought-provoking. One possibly easy thing that I think would be great would be links to a few posts that you think have used this strategy with success.
Thanks, I clarified the noise issue. Regarding factor analysis, could you check if I understand everything correctly? Here's what I think is the situation:
We can write a factor analysis model (with a single factor) as
where:
It always holds (assuming and are independent) that
In the simplest variant of factor analysis (in the current post) we use in which cas...
Thanks for pointing out those papers, which I agree can get at issues that simple correlations can't. Still, to avoid scope-creep, I've taken the less courageous approach of (1) mentioning that the "breadth" of the effects of genes is an active research topic and (2) editing the original paragraph you linked to to be more modest, talking about "does the above data imply" rather than "is it true that". (I'd rather avoid directly addressing 3 and 4 since I think that doing those claims justice would require more work than I can put in here.) Anyway, thanks again for your comments, it's useful for me to think of this spectrum of different "notions of g".
Thanks, very clear! I guess the position I want to take is just that the data in the post gives reasonable evidence for g being at least the convenient summary statistic in 2 (and doesn't preclude 3 or 4).
What I was really trying to get at in the original quote is that some people seem to consider this to be the canonical position on g:
There are lots of articles that (while not explicitly stating the abo...
Can I check if I understand your point correctly? I suggested we know that g has many causes since so many genes are relevant and thus f you opened up a brain, you wouldn't be able to "find" g in any particular place. It's the product of a whole bunch of different genes, each of which is just coding for some protein, and they all interact in complex ways. If I understand you correctly, you're pointing out that there could be a sort of "causal bottleneck" of sorts. For example, maybe all the different genes have complex effects, but all that really matters ...
I used python/matplotlib. The basic idea is to create a 3d plot like so:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
Then you can add dots with something like this:
ax.scatter(X,Y,Z,alpha=.5,s=20,color='navy',marker='o',linewidth=0)
Then you save it to a movie with something like this:
def update(i, fig, ax):
ax.view_init(elev=20., azim=i)
return fig, ax
frames = np.arange(0, 360, 1)
anim = FuncAnimation(fig, update, frames=frames, repeat=True, fargs=(fig, ax))
writer = 'ffmpeg'
anim.save(fname, dpi=80, writer=writer, fps=30)
... Thanks for the reply. I certainly agree that "factor analysis" often doesn't make that assumption, though it was my impression that it's commonly made in this context. I suppose the degree of misleading-ness here depends on how often people assume isotropic noise when looking at this kind of data?
In any case, I'll try to think about how to clarify this without getting too technical. (I actually had some more details about this at one point but was persuaded to remove them for the sake of being more accessible.)
if a trait is 80% heritable and you want to guess whether or not Bob has that trait then you'll be 80% more accurate if you know whether or not Bob's parents have the trait than if you didn't have that information.
I think this is more or less correct for narrow-sense heritability (most commonly used when breeding animals) but not quite right for broad-sense heritability (most commonly used with humans). If you're talking about broad-sense heritability, the problem is that you'd need to know not just if the parents have the trait, but also which genes Bo...
On the other hand, there is some non-applied scientific value in heritability. For example, though religiosity is heritable, the specific religion people join appears to be almost totally un-heritable. I think it's OK to read this in the straightforward way, i.e. as "genes don't predispose us to be Christian / Muslim / Shinto / whatever". I don't have any particular application for that fact, but it's certainly interesting.
Similarly, schizophrenia has sky-high heritability (like 80%) meaning that current environments don't have a huge impact on where schizophrenia appears. That's also interesting even if not immediately useful.
My view is that people should basically talk about heritability less and interventions more. In most practical circumstances, what we're interested in is how much potential we have to change a trait. For example, you might want to reduce youth obesity. If that's your goal, I don't think heritability helps you much. High heritability doesn't mean that there aren't any interventions that can change obesity-- it just means that the current environments that people are already exposed to don't create much variance. Similarly, low heritability means the enviro...
In principle, I guess you could also think about low-tech solutions. For example, people who want to opt out of alcohol might have some slowly dissolving tattoo / dye placed somewhere on their hand or something. This would eliminate the need for any extra ID checks, but has the big disadvantage it would be visible most of the time.
Thanks. Are you able to determine what the typical daily dose is for implanted disulfiram in Eastern Europe? People who take oral disulfiram typically need something like 0.25g / day to have a significant physiological effect. However, most of the evidence I've been able to find (e.g. this paper) suggest that the total amount of disulfiram in implants is around 1g. If that's dispensed over a year, you're getting like 1% of the dosage that's active orally. On top of that, the evidence seems pretty strong that bioavailability from implants is lower than from...
Very interesting! Do you know how much disulfiram the implant gives out per day? There's a bunch of papers on implants, but there's usually concerns about (a) that the dosage might be much smaller than the typical oral dosage and/or (b) that there's poor absorption.
I specified (right before the first graph) that I was using the US standard of 14g. (I know the paper uses 10g. There's no conflict because I use their raw data which is in g, not drinks.)
I wasn't (intentionally?) being ironic. I guess that for underage drinking we have the advantage that you can sort of guess how old someone looks, but still... good point.
I've politely contacted them several times via several different channels just asking for clarifications and what the "missing coefficients" are in the last model. Total stonewall- they won't even acknowledge my contacts. Some people more connected to the education community also apparently did that as a result of my post, with the same result.
You could model the two as being totally orthogonal:
In practice, I think the dividing lines are more blurry. Also, the two tend to come up together because people who are attracted to the thinking in one of these tend to be attracted to the other as well.
You definitely need a number of data at least exponential in the number of parameters, since the number of "bins" is exponential. (It's not so simple as to say that exponential is enough because it depends on the distributional overlap. If there are cases where one group never hits a given bin, then even an infinite amount of data doesn't save you.)
I see what you're saying, but I was thinking of a case where there is zero probability of having overlap among all features. While that technically restores the property that you can multiply the dataset by arbitrarily large numbers, if feels a little like "cheating" and I agree with your larger point.
I guess Simpson's paradox does always have a right answer in "stratify along all features", it's just that the amount of data you need increases exponentially in the number of relevant features. So I think that in the real world you can multiply the amount of...
I like your concept that the only "safe" way to use utilitarianism is if you don't include new entities (otherwise you run into trouble). But I feel like they have to be included in some cases. E.g. If I knew that getting a puppy would make me slightly happier, but the puppy would be completely miserable, surely that's the wrong thing to do?
(PS thank you for being willing to play along with the unrealistic setup!)
This covers a really impressive range of material -- well done! I just wanted to point out that if someone followed all of this and wanted more, Shannon's 1948 paper is surprisingly readable even today and is probably a nice companion:
http://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf
Well, it would be nice if we happened to live in a universe where we could all agree on an agent-neutral definition of what the best actions to take in each situation are. It seems to be that we don't live in such a universe, and that our ethical intuitions are indeed sort of arbitrarily created by evolution. So I agree we don't need to mathematically justify these things (and maybe it's impossible) but I wish we could!
If I understand your second point, you're suggesting that part of our intuition seems to suggest large populations are better is that larger populations tend to make the average utility higher. I like that! It would be interesting to try to estimate at that human population level average utility would be highest. (In hunter/gatherer or agricultural times probably very low levels. Today probably a lot higher?)
Can you clarify which answer you believe is the correct one in the puppy example? Or, even better, the current utility for the dog in the "yes puppy" example is 5-- for what values you believe it is correct to have or not have the puppy?
My guess is that the problem is I didn't make it clear that this is just the introduction from the link? Sorry, I edited to clarify.
Totally agree that the different failure modes are in reality interrelated and dependent. In fact, one ("necessary despot") is a consequence of trying to counter some of the others. I do feel that there's enough similarity between some of the failure modes at different sites that's it's worth trying to name them. The temporal dimension is also an interesting point. I actually went back and looked at some of the comments on Marginal Revolution posts years ago. They are pretty terrible today, but years ago they were quite good.
In principle, for work done for market, I guess you don't need to explicitly think about free trade. Rather, by everyone pursing their own interests ("how much money can I make doing this"?) they'll eventually end up specializing in their comparative advantage anyway. Though, with finite lifetime, you might want to think about it to short-circuit "eventually".
For stuff not done for market (like dividing up chores), I'd think there's more value in thinking about it explicitly. That's because there's no invisible hand naturally pushing people toward their comparative advantage so you're more likely to end up doing things inefficiently.
That's definitely the central insight! However, experimentally, I found that explanation alone was only useful for people who already understood Monty Hall pretty well. The extra steps (the "10 doors" step and the "Monty promising") seem to lose fewer people.
That being said, my guess is that most lesswrong-ites probably fall into the "already understood Monty Hall" category, so...
You've convinced me! I don't want to defend the claim you quoted, so I'll modify "arguably" into something much weaker.