Does that make it more likely or less likely that his death was related to his diet?

His blog is back-- it's had occasional down time for a while. The archive copy was down, though.

Probably a good idea to save anything you think is especially important.

His approach would be expected to optimize for common situations, which may not be the same as optimizing for rare situations. I've been working on a theory that health is not a single thing.

For all I know, he had some intrinsic cardio-vascular problems, and his self-experimentation led to him living longer than he otherwise would have.

> foldr1 (*) $ map (1-) [0.006337,0.006837,0.007347,0.007905,0.008508,0.009116,0.009723,
                         0.010354,0.011046,0.011835,0.012728,0.013743,0.014885,0.016182,
                         0.017612,0.019138,0.020752,0.022497,0.024488,0.026747,0.029212,
                         0.031885,0.034832,0.038217,0.042059,0.046261,0.050826,0.055865,
                         0.061620]
0.5065374918662645

A reference to this example of why to use the Oxford comma.

Can you please give an example of where the possible subjectivity of probabilities matter? I mean this in earnest.

Full disclosure: I have papers using B (on structure learning using BIC, which is an approximation to a posterior of a graphical model), and using F (on estimation of causal effects). I have no horse in this race.

Bayes rule is the answer to that problem that provides the promise of a solution.

See, this is precisely the kind of stuff that makes me shudder, that regularly appears on LW, in an endless stream. While Scott Alexander is busy bible thumping data analysts on his blog, people here say stuff like this.

Bayes rule doesn't provide shit. Bayes rule just says that p(A | B) p(B) = p(B | A) p(A).

Here's what you actually need to make use of info in this study:

(a) Read the study.

(b) See if they are actually making a causal claim.

(c) See if they are using experimental or observational data.

(d) Experimental? Do we believe the setup? Are we in a similar cohort? What about experimental design issues? Observational? Do they know what they are doing, re: causality-from-observational-data? Is their model that permits this airtight (usually it is not, see Scott's post on "adjusting for confounders". Generally to really believe that adjusting for confounders is reasonable you need a case where you know all confounders are recorded by definition of the study, for instance if doctors prescribe medicine based only on recorded info in the patient file).

(e) etc etc etc

I mean what exactly did you expert, a free lunch? Getting causal info and using it is hard.

p.s. If you skeptical about statistics papers that adjust for confounders, you should also be skeptical about missing data papers that assume MAR (missing at random). It is literally the same assumption.

We live at a time where up to 70% of scientific research can't be replicated. Frequentism might not be to blame for all of that, but it does play it's part. There are issues such an the Bem paper about porno-precognition where frequentist techniques did suggest that porno-precognition is real but analysing Bems data with Bayesian methods suggested it's not.

It seems to me that there's a bigger risk from Bayesian methods. They're more sensitive to small effect sizes (doing a frequentist meta-analysis you'd count a study that got a p=0.1 result as evidence against, doing a bayesian one it might be evidence for). If the prior isn't swamped then it's important and we don't have good best practices for choosing priors; if the prior is swamped then the bayesianism isn't terribly relevant. And simply having more statistical tools available and giving researchers more choices makes it easier for bias to creep in.

Bayes' theorem is true (duh) and I'd accept that there are situations where bayesian analysis is more effective than frequentist, but I think it would do more harm than good in formal science.

To all your points about the overloading of "Bayesian", fair enough. I guess I just don't see why that overloading is necessary.

We lack a good framework for doing this. Bayes rule is the answer to that problem that provides the promise of a solution. The solution to wait a few years and then read a meta review is unsatisfying.

Sure Bayes rule provides a formalization of updating beliefs based on evidence, but you can still be dead wrong. In particular, setting a prior on any given issue isn't enough. You have to be prepared to update for evidence of the form "I am really bad at setting priors". And really, priors are just a (possibly arbitrary) way of digesting existing evidence. Sometimes they can be very useful (avoiding privileging the hypothesis) but sometimes they are quite arbitrary.

There are issues such an the Bem paper about porno-precognition where frequentist techniques did suggest that porno-precognition is real but analysing Bems data with Bayesian methods suggested it's not.

According to the Slate Star Codex article Bem's results stand up to bayesian analysis quite well (that is, it has a strong Bayes factor). The only exception he mentioned was "I begin with a very low prior for psi phenomena, and a higher prior for the individual experiments and meta-analysis being subtly corrupt"; but there's nothing especially helpful about this in actually fixing the experimental design and meta-analysis.

Part of LW is that it's a place to discuss how an AGI could be structured. As such we care about the philosophic level of how you come to know that something is true. As such there an interest into going as basic as possible when looking at epistemology.

How you get from AGI to epistemology eludes me. As long as the AGI can accurately model its interactions with the environment, that's really all it needs (or can hope) to do.

That sentence is quite easy to say but it effectively means there no such thing as pure absolute objective truth. If you use tools A you get truth X and if you use tools B you get truth Y. Neither X or Y are "more true". That's not an appealing conclusion to many people.

One of them is more useful for prediction and inference. They can guide you towards observing mechanisms useful for future hypothesis generation. That's all you can hope for. Especially in the case of "are low-salt diets healthy". A "Yes" or "No" to that question will never be truthful, because "health" and "for what segments of the population" and "in conjunction with what other lifestyle factors" are left underspecified. And you'll never get rid of the kernel of doubt that the low-sodium lobby has been the silent force behind all the anti-salt research this whole time.

The best you can do is provide enough evidence that anyone who points out your hypothesis is not truth can be reasonably called a pedant or conspiracy theorist, but not 100% guaranteed wrong.

As you might see, I am a fan of the idea of Dissolving epistemology.

The term "holy war" or "religious war" is often used to describe debates where people advocate for a side with an intensity disproportionate to the stakes, (e.g. the proper pronunciation of "gif", vi vs. emacs, surrogate vs. natural primary keys in the RDBM). That's how I read the OP, and it's fitting in context.

To me it looks like a tension between a method that's theoretically better

It's because Bayesian methods really do claim to be more than just a set of tools. They are supposed to be universally applicable.

[etc.]

Ugh. Here is a good heuristic:

"Not in stats or machine learning? Stop talking about this."

Can you point to examples of these "holy wars"? I haven't encountered something I'd describe like that, so I don't know if we've been seeing different things, or just interpreting it differently.

Various bits of Jaynes's "Confidence intervals vs Bayesian intervals" seem holy war-ish to me. Perhaps the juiciest bit (from pages 197-198, or pages 23-24 of the PDF):

I first presented this result to a recent convention of reliability and quality control statisticians working in the computer and aerospace industries; and at this point the meeting was thrown into an uproar, about a dozen people trying to shout me down at once. They told me, "This is complete nonsense. A method as firmly established and thoroughly worked over as confidence intervals can't possibly do such a thing. You are maligning a very great man; Neyman would never have advocated a method that breaks down on such a simple problem. If you can't do your arithmetic right, you have no business running around giving talks like this".

After partial calm was restored, I went a second time, very slowly and carefully, through the numerical work [...] with all of them leering at me, eager to see who would be the first to catch my mistake [...] In the end they had to concede that my result was correct after all.

To make a long story short, my talk was extended to four hours (all afternoon), and their reaction finally changed to: "My God – why didn't somebody tell me about these things before? My professors and textbooks never said anything about this. Now I have to go back home and recheck everything I've done for years."

This incident makes an interesting commentary on the kind of indoctrination that teachers of orthodox statistics have been giving their students for two generations now.

Small changes to genotype don't imply small changes to phenotype.

Evolution is slow. It takes generations. Depending on the selection pressure these may be quite few. Assume sexual drive were the only determining factor for reproductive fitness (which probably is a good approximation for some animals) and you introduce a 95% successful 'contraception' (e.g. a genetic modification to avoid reproduction - this has been done for mosquitoes) and guess how many generations it takes to work around it. Now humans use 95% reliable contraceptives - but their usage is regulated by complex processes so no simple analysis suffices (just think of the misinterpretaion of the baby-bust/pill-gap).

Additionally we don't have to limit us to genetic evolution. We could also consider memetic evolution - the one invoked somewhat imprecisely in point 4. Memes evolve faster. It could happen that meme-complexes joining birth-control and anti-science out-breed progress within few generations.

Sure after 500 years we'd likely have the technological means - if anyone is still interested in technology then. And for some 500 may be a more likely date than 50.

I am not sure if you are aware, but the OTs have been a monthly rather than weekly event for years until a few months ago.

At any rate, I am generally not convinced that a further reduction is needed, as it will 1. It might make people even more confused as to when to post them 2. It is generally becomes more 'spammy 'and 3. The benefits of a further reduction are questionable.

This does not explain the current thread, as Nancy did not post anything in it initially (but seems to be a big part of it).

That's probably what I'm thinking of. Thank you to you and bramflakes!

To the extent that privilege claims are about ignorance, I think they're likely to have a point. To the extent that they're a claim that some people are guaranteed to be wrong, they're ad hominem.

There's the difference between logical fallacy and Bayesian fallacy. Most logical fallacies got evolved into human thinking because they often enough in fact constituted Bayesian evidence. e.g. authorities on a subject often know what the hell they're talking about.

It says: "don't hate the player, hate the game."

I'm not sure what the average LW reader knows.

I consider it likely that there are LW readers (both left and right) who don't know there's opposition to the privilege model from the left.

I agree, though to be fair the author himself seems to use malicious and fallacious to describe a privilege framework.

First, I am arguing that no one’s participation in public discourse should be denigrated by appeal to essential features of their identity. If we, as leftists, want to be unashamedly critical of discourse—as we should be—we should do so with reference to structures of power, such as heterosexual hegemony, rather than with reference to essential identities, such as the ‘straightness’ of particular individuals.

...

Second, I am arguing that to situate ideology in identity can not only be malicious, but also fallacious. If a self-identified queer person were to have written “How Gay Pride Backfires”, the privilege framework would collapse as an explanans, as it would no longer be able to appeal to the heterosexual privilege of the author to explain the danger of the argument. Importantly, however, in this alternative scenario, the queerness of the author would not render the article any less ideological and detrimental to the interests of sexual minorities.

Any of the above.

I see how that would work. Thanks.

That was pretty much my intuition too, once I actually thought about it.

For context, this is on my mind because I'm graduating from college in a year. I'm still figuring out my values and their relative importances. (I'm going to make a post about it later, if I'm still unsure after talking it over with myself and my friends.) I've heard that the Southwest has a lower cost of living than the rest of the country, and some areas have nice weather, but beyond that I don't know a whole lot about what it's like living there. (I'm on study abroad in Sydney now, and noticing that I go outside more when in an interesting neighborhood with nice weather.)

At the moment, all the cities near the top of my mental list are in the northeast US. I'm pretty sure that's at least partly because that's the only region I've stayed in for more than a couple weeks at a time, and I'm slightly homesick at the moment. Probably what I should do is do a bit of research now and write down what I'm thinking, and then come back to it once I've come back to my hometown and had some time to get bored of it, and maybe again during my last semester of college. It's probably not worth trying to evaluate places out of driving distance of Pennsylvania until I stop being homesick, now that I think of it that way.

EA is about things that are relatively easy to measure, and causing political change is hard to measure.

The Swiss folks may have done that. But I think the major organizations, like GiveWell, Giving What We Can, and 80,000 Hours, are focused on apolitical causes like global health, if you judge from their lists of recommended charities.

Also I don't think there's any getting around taking a position on mainstream political issues to optimally benefit society. Statistically your income is more influenced by which society you happen to be born in than anything you do. If you believe Acemoglu and Robinson, it's the institutions that matter for economic growth.

At the Community Weekend in Berlin Jonas spoke about EA movement building and how one is

Huh?

I think that largely depends on your skill set.

It might. (Thank you for giving a data point.) I find myself drawn toward the earning to give route since then you can use your salary to kinda measure impact. You could measure too with seeking political office although that's not my cup of tea. But with political activism I don't really see how.

That the effects of abortion are economically nertral[sic] seems like an extrodinary[sic] claim.

Not really. If I recall, legalizing abortion has almost no effect on the birth rate, accessible contraceptives are somewhat higher, but none come close to changing cultural norms.

roughly 50 million abortions have taken place in America since Roe v. Wade.

50 million mostly legal abortions, even if the figure is correct, does not translate to 50 million more adults, of course. It is not even clear whether the overall effect is increase or decrease in population.

I suggested this division of causes because, first, people who earn to give may join the upper or at least upper middle class. It seems harder to advocate for things like socialism when your peer group is rich. Your opinions aren't going to earn you praise or friends and friends and connections are really important for making money. It's also hard to devote time and energy to maintaining odd views when you're focused on a career that isn't directly involved with acting on those opinions. You're losing some potential synergy. It also possible that, second, the usefulness of cash donations varies with whether the cause has support among the rich or poor, although this might work the other way in that I would expect causes that favor the poor to need money more.

But with a topic like abortion this all seems unclear--although opinions on abortion do correlate some with income, I don't think that correlation is a strong as with outright economic redistribution. What do you think?

If you want to suggest a more clearly neutral topic than abortion I would be interested to hear it.

At CFAR workshop all participants were real people. But they still missed an existing opportunity to abuse the system: there were rewards for winning, but no punishment for losing. So two people could agree to transfer a lot of bits from one to another, and split the price afterwards.

Not quite true. I ran the markets, and I did threaten to fearsomely glare at people who were abusing the system. (And my glare is very fearsome).

You're right. Gaming the system is feasible, though I believe it is very low-value.

What exactly would the point of gaming a prediction thread be? The only point-keeping would be informal, so if you're making a bunch of points off of idiotic puppets bets it's still visible as because you were up against an idiotic bet. It'd be like lying on the group diary, almost.

Do note, there was actually a HUGE punishment for losing. You could get into the negative pretty easily by being stupidly overconfident. The scoring was 100 × log2(Your probability of outcome/Previous Bet probability of outcome).. For example: if you updated a 50% house bet to 99% being correct would give you 98.55 "bits", while being wrong would give you -564.39

But they still missed an existing opportunity to abuse the system: there were rewards for winning, but no punishment for losing. So two people could agree to transfer a lot of bits from one to another, and split the price afterwards.

I raised this possibility, but an instructor said they'd use human judgment to stop us from doing that.

(My actual idea was along the lines of "if two of us decide that we aren't going to come to agreement on a market, we can just repeatedly alternate our bets, and each expect that we're getting arbitrarily many points from this". The instructor said something like, they'd just ignore all but the final bets if they thought we were doing that.)

100 × log2(Your probability of outcome/Previous Bet probability of outcome).. For example: if you updated a 50% house bet to 99% being correct would give you 98.55 "bits", while being wrong would give you -564.39

It's posted a couple of posts up. I had given no credence to the idea that it could be a CFAR secret.

• It's a reasonably sized city (giving me theatre, foreign films, lectures, etc)
• Plus public transportation (I don't know how to drive)
• Short trip on Amtrak to see my family in NY
• I am a politics junkie
• So are a lot of my friends, so the plurality of people I care about most all live here
• As a result, I get to do group movie nights, DnD, parliamentary debates, babysitting with them

Also, I'd bet the kind of person I ultimately want to marry is most likely to want to live in DC, too.

Well, I've sent out a lot of (mostly writing) applications, and not gotten bites on anything good. I've been interviewing here and tipped them off I had an offer, and am waiting to hear back. I've done public policy analysis before and could do it again.

Definitely have written too much Snowden/Manning coverage to get a security clearanced job in DC, so places like Booz Allen are right out. ;)

Along similar lines, Jon Elster was so taken by that literary motif that he used it in his Ulysses and the Sirens: Studies in Rationality and Irrationality, as well as his later Ulysses Unbound: Studies in Rationality, Precommitment, and Constraints.

I must have mixed it up with some other statement.

One sense of "burden of proof" seems to be a game-rule for a (non-Bayesian) adversarial debate game. It is intended to exclude arguments from ignorance, which if permitted would stall the game. The players are adversaries, not co-investigators. The player making a novel claim bears the burden of proof — rather than a person criticizing that claim — so that the players actually have to bring points to bear. Consider:

A: God loves frogs. They are, above all other animals, sacred to him.
B: I don't believe it.
A: But you can't prove that frogs aren't sacred!
B: Well of course not, it never occurred to me to consider as a possibility.

At this point the game would be stalled at zero points.

The burden-of-proof rule forbids A's last move. Since A started the game by making a positive claim — the special status of frogs — A has to provide some evidence for this claim. B can then rebut this evidence, and A can present new evidence, and then we have a game going:

A: God loves frogs. They are, above all other animals, sacred to him.
B: I don't believe it.
A: Well, the God Book says that God loves frogs.
B: But the God Book also says that chickens are a kind of flea, and modern taxonomy shows that's wrong. So the God Book isn't good evidence.
A: I found a frog once that had the word "God" encoded in the spots on its back in Morse code.
B: But the spots on frogs' backs are probably pretty random. How many frogs did you have to check?
A: Umm ... a few thousand. It was a sacred duty!
B: But it would be a lot more convincing if all frogs had that pattern, wouldn't it?
A: Well ... Frogs are sacred in Homestuck, which is the most financially successful webcomic of all time. Surely that's a sign of God's favor.
B: They're sacred to Prospitians, yes, but Dersites think they're blasphemous. Besides, if financial success was a sign of God's favor, we should all be worshiping Berkshire Hathaway, not frogs.

According to the rules of the game, B doesn't have to establish that God hates frogs. B just has to knock down each one of A's arguments. Then, since A has failed to establish any evidence that holds up, B is (so far) winning the game.

If someone is (or seems like they might be) privileging the hypothesis, it seems reasonable to say that the burden of proof is on them, not just as a social norm but also as a question of epistemology.

In other words, if there are a hundred boxes where the diamond could be and I claim that it's in box number 27, then it's reasonable that I ought to provide some evidence for this claim, rather than requiring the other person to come up with a hypothesis for why my claim would be false. There are an infinite number of false hypotheses, and if we try to test them all rather than focusing on the most promising ones, we'll never get anywhere.

a hypothesis which has a very low prior probability

Its probability is different in estimates of the people who disagree, and its best alternative will find the status of having "low probability" in estimate of different people. Just "low probability" doesn't make the situation asymmetric.

if you want to support the low probability hypothesis, then you spend the resources.

You should spend the resources when there is high value of information, otherwise do something else. Improving someone else's beliefs may have high value for them.

if you want to support the low probability hypothesis, then you spend the resources

If you want to [test] a low [value of information] hypothesis, you change your mind and stop wanting that. What happens is that people disagree on the probabilities.

I'd forgotten that-- I'd just noticed that we were running past the end date on the previous thread.

Uhm, we weren't.