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Pretending not to see when a rule you've set is being violated can be optimal policy in parenting sometimes (and I bet it generalizes).
Example: suppose you have a toddler and a "rule" that food only stays in the kitchen. The motivation is that each time food is brough into the living room there is a small chance of an accident resulting in a permanent stain. There's cost to enforcing the rule as the toddler will put up a fight. Suppose that one night you feel really tired and the cost feels particularly high. If you enforce the rule, it will be much more painful than it's worth in that moment (meaning, fully discounting future consequences). If you fail to enforce the rule, you undermine your authority which results in your toddler fighting future enforcement (of this and possibly all other rules!) much harder, as he realizes that the rule is in fact negotiable / flexible.
However, you have a third choice, which is to credibly pretend to not see that he's doing it. It's true that this will undermine your perceived competence, as an authority, somewhat. However, it does not undermine the perception that the rule is to be fully enforced if only you noticed the violation. You get to "skip" a particularly costly enforcement, without taking steps back that compromise future enforcement much.
I bet this happens sometimes in classrooms (re: disruptive students) and prisons (re: troublesome prisoners) and regulation (re: companies that operate in legally aggressive ways).
Of course, this stops working and becomes a farce once the pretense is clearly visible. Once your toddler knows that sometimes you pretend not to see things to avoid a fight, the benefit totally goes away. So it must be used judiciously and artfully.
Huh, that went somewhere other than where I was expecting. I thought you were going to say that ignoring letter-of-the-rule violations is fine when they're not spirit-of-the-rule violations, as a way of communicating the actual boundaries.
Perhaps that can work depending on the circumstances. In the specific case of a toddler, at the risk of not giving him enough credit, I think that type of distinction is too nuanced. I suspect that in practice this will simply make him litigate every particular application of any given rule (since it gives him hope that it might work) which raises the cost of enforcement dramatically. Potentially it might also make him more stressed, as I think there's something very mentally soothing / non-taxing about bright line rules.
I think with older kids though, it's obviously a really important learning to understand that the letter of the law and the spirit of the law do not always coincide. There's a bit of a blackpill that comes with that though, once you understand that people can get away with violating the spirit as long as they comply with the letter, or that complying with the spirit (which you can grok more easily) does not always guarantee compliance with the letter, which puts you at risk of getting in trouble.
Infertility rates are rising and nobody seems to quite know why. Below is what feels like a possible (trivial) explanation that I haven't seen mentioned anywhere.
I'm not in this field personally so it's possible this theory is out there, but asking GPT about it doesn't yield the proposed explanation: https://chat.openai.com/share/ab4138f6-978c-445a-9228-674ffa5584ea
Toy model:
Under this model, in the olden days we would find a high proportion of fertile people in the gene pool, but in the modern world we wouldn't. Put differently, the old convention lead to a strong positive correlation between fertility and participation in the gene pool, and the new convention leads to 0 correlation. This removes the selective pressure on fertility, hence we should expect fertility to drop / infertility to rise.
Empirical evidence for this would be something like an analysis of the time series of family size variance and infertility -- is lower variance followed by increased infertility?
To be sure, I'm not an expert on the topic.
Declines in male fertility I think are regarded as real, though I haven't examined the primary sources.
Regarding female fertility, this report from Norway outlines the trend that I vaguely thought was representative of most of the developed world over the last 100 years.
Female fertility is trickier to measure, since female fertility and age are strongly correlated, and women have been having kids later, so it's important (and likely tricky) to disentangle this confounder from the data.
Causality is rare! The usual statement that "correlation does not imply causation" puts them, I think, on deceptively equal footing. It's really more like correlation is almost always not causation absent something strong like an RCT or a robust study set-up.
Over the past few years I'd gradually become increasingly skeptical of claims of causality just by updating on empirical observations, but it just struck me that there's a good first principles reason for this.
For each true cause of some outcome we care to influence, there are many other "measurables" that correlate to the true cause but, by default, have no impact on our outcome of interest. Many of these measures will (weakly) correlate to the outcome though, via their correlation to the true cause. So there's a one-to-many relationship between the true cause and the non-causal correlates. Therefore, if all you know is that something correlates with a particular outcome, you should have a strong prior against that correlation being causal.
My thinking previously was along the lines of p-hacking: if there are many things you can test, some of them will cross a given significance threshold by chance alone. But I'm claiming something more specific than that: any true cause is bound to be correlated to a bunch of stuff, which will therefore probably correlate with our outcome of interest (though more weakly, and not guaranteed since correlation is not necessarily transitive).
The obvious idea of requiring a plausible hypothesis for the causation helps somewhat here, since it rules out some of the non-causal correlates. But it may still leave many of them untouched, especially the more creative our hypothesis formation process is! Another (sensible and obvious, that maybe doesn't even require agreement with the above) heuristic is to distrust small (magnitude) effects, since the true cause is likely to be more strongly correlated with the outcome of interest than any particular correlate of the true cause.
This seems pretty different from Gwern's paper selection trying to answer this topic in How Often Does Correlation=Causality?, where he concludes
Compilation of studies comparing observational results with randomized experimental results on the same intervention, compiled from medicine/economics/psychology, indicating that a large fraction of the time (although probably not a majority) correlation ≠ causality.
Also see his Why Correlation Usually ≠ Causation.
Those are not randomly selected pairs, however. There are 3 major causal patterns: A->B, A<-B, and A<-C->B. Daecaneus is pointing out that for a random pair of correlations of some variables, we do not assign a uniform prior of 33% to each of these. While it may sound crazy to try to argue for some specific prior like 'we should assign 1% to the direct causal patterns of A->B and A<-B, and 99% to the confounding pattern of A<-C->B', this is a lot closer to the truth than thinking that 'a third of the time, A causes B; a third of the time, B causes A; and the other third of the time, it's just some confounder'.
What would be relevant there is "Everything is Correlated". If you look at, say, Meehl's examples of correlations from very large datasets, and ask about causality, I think it becomes clearer. Let's take one of his first examples:
For example, only children are nearly twice as likely to be Presbyterian than Baptist in Minnesota, more than half of the Episcopalians “usually like school” but only 45% of Lutherans do, 55% of Presbyterians feel that their grades reflect their abilities as compared to only 47% of Episcopalians, and Episcopalians are more likely to be male whereas Baptists are more likely to be female.
Like, if you randomly assigned Baptist children to be converted to Presbyterianism, it seems unlikely that their school-liking will suddenly jump because they go somewhere else on Sunday, or that siblings will appear & vanish; it also seems unlikely that if they start liking school (maybe because of a nicer principal), that many of those children would spontaneously convert to Presbyterianism. Similarly, it seems rather unlikely that undergoing sexual-reassignment surgery will make Episcopalian men and Baptist women swap places, and it seems even more unlikely that their religious status caused their gender at conception. In all of these 5 cases, we are pretty sure that we can rule out one of the direct patterns, and that it was probably the third, and we could go through the rest of Meehl's examples. (Indeed, this turns out to be a bad example because we can apply our knowledge that sex must have come many years before any other variable like "has cold hands" or "likes poetry" to rule out one pattern, but even so, we still don't find any 50%s: it's usually pretty obviously direct causation from the temporally earlier variable, or confounding, or both.)
So what I am doing in 'How Often Does Correlation=Causality?' is testing the claim that "yes, of course it would be absurd to take pairs of arbitrary variables and calculate their causal patterns for prior probabilities, because yeah, it would be low, maybe approaching 0 - but that's irrelevant because that's not what you or I are discussing when we discuss things like medicine. We're discussing the good correlations, for interventions which have been filtered through the scientific process. All of the interventions we are discussing are clearly plausible and do not require time travel machines, usually have mechanisms proposed, have survived sophisticated statistical analysis which often controls for covariates or confounders, are regarded as credible by highly sophisticated credentialed experts like doctors or researchers with centuries of experience, and may even have had quasi-randomized or other kinds of experimental evidence; surely we can repose at least, say, 90% credibility, by the time that some drug or surgery or educational program has gotten that far and we're reading about it in our favorite newspaper or blog? Being wrong 1 in 10 times would be painful, but it certainly doesn't justify the sort of corrosive epistemological nihilism you seem to be espousing."
But unfortunately, it seems that the error rate, after everything we humans can collectively do, is still a lot higher than 1 in 10 before the randomized version gets run. (Which implies that the scientific evidence is not very good in terms of providing enough Bayesian evidence to promote the hypothesis from <1% to >90%, or that it's <<1% because causality is that rare.)
Reflecting on the particular ways that perfectionism differs from the optimal policy (as someone who suffers from perfectionism) and looking to come up with simple definitions, I thought of this:
So, perfectionism will be maximally costly in an environment where you have lots of valuable options of new things you could do (breaking from status quo) but you're unsure whether you can come close to the best one, like you might end up choosing something that's half as good as the best you could have done. Optimal policy would say to just give it your best, and that you should be happy since this is an amazingly good problem to have, whereas perfectionism will whisper in your ear how painful it might be to only get half of this very large chunk of potential utility, and wouldn't it be easier if you just waited.
Regularization implements Occam's Razor for machine learning systems.
When we have multiple hypotheses consistent with the same data (an overdetermined problem) Occam's Razor says that the "simplest" one is more likely true.
When an overparameterized LLM is traversing the subspace of parameters that solve the training set seeking the smallest l2-norm say, it's also effectively choosing the "simplest" solution from the solution set, where "simple" is defined as lower parameter norm i.e. more "concisely" expressed.
Unfortunately the entire complexity has just been pushed one level down into the definition of "simple". The L2 norm can't really be what we mean by simple, because simply scaling the weights in a layer by A, and the weights in the next layer by 1/A leaves the output of the network invariant, assuming ReLU activations, yet you can obtain arbitrarily high L2 norms by just choosing A high enough.
Agreed with your example, and I think that just means that L2 norm is not a pure implementation of what we mean by "simple", in that it also induces some other preferences. In other words, it does other work too. Nevertheless, it would point us in the right direction frequently e.g. it will dislike networks whose parameters perform large offsetting operations, akin to mental frameworks or beliefs that require unecessarily and reducible artifice or intermediate steps.
Worth keeping in mind that "simple" is not clearly defined in the general case (forget about machine learning). I'm sure lots has been written about this idea, including here.
I wonder how much of the tremendously rapid progress of computer science in the last decade owes itself to structurally more rapid truth-finding, enabled by:
There are other reasons to expect rapid progress in CS (compared to, say, electrical engineering) but I wonder how much is explained by this replication dynamic.
Very little, because most CS experiments are not in fact replicable (and that's usually only one of several serious methodological problems).
CS does seem somewhat ahead of other fields I've worked in, but I'd attribute that to the mostly-separate open source community rather than academia per se.
To be sure, let's say we're talking about something like "the entirety of published material" rather than the subset of it that comes from academia. This is meant to very much include the open source community.
Very curious, in what way are most CS experiments not replicable? From what I've seen in deep learning, for instance, it's standard practice to include a working github repo along with the paper (I'm sure you know lots more about this than I do). This is not the case in economics, for instance, just to pick a field I'm familiar with.
See e.g. https://mschloegel.me/paper/schloegel2024sokfuzzevals.pdf
Fuzzing is a generally pretty healthy subfield, but even there most peer-reviewed papers in top venues are still are completely useless! Importantly, "a 'working' github repo" is really not enough to ensure that your results are reproducible, let alone ensure external validity.
It feels like (at least in the West) the majority of our ideation about the future is negative, e.g.
Are we at a historically negative point in the balance of "good vs bad ideation about the future" or is this type of collective pessimistic ideation normal?
If the balance towards pessimism is typical, is the promise of salvation in the afterlife in e.g. Christianity a rare example of a powerful and salient positive ideation about our futures (conditioned on some behavior)?
I agree. I feel like this is a very recent change as well. We used to be hopeful about the future, creating sci-fi about utopias rather than writing nightmare scenarios.
The west is becoming less self-affirming over time, and our mental health is generally getting worse. I think it's because of historic guilt, as well as a kind of self-loathing pretending that it's virtue (anti-borders, anti-nationalism, anti-natalism) not to mention the slander of psychological drives which strive for growth and quality (competition, hierarchies, ambition, elitism, discrimination/selection/gatekeeping)
I do not believe that the salvation in the afterlife is the opposite of this, but rather the same. It ultimately talks negatively about life and actual reality, comparing it to some unreachable ideal. It's both pessimistic, as well as a psychological cope which makes it possible to endure this pessimism. The message is something akin to "Endure, and you will be rewarded in the end"
It's a weariness we will have to overcome. I feel like our excessive tendency to problem-solving has caused us to view life as a big collection of problems, rather than something which is merely good but imperfect
From personal observation, kids learn text (say, from a children's book, and from songs) back-to-front. That is, the adult will say all but the last word in the sentence, and the kid will (eventually) learn to chime in to complete the sentence.
This feels correlated to LLMs learning well when tasked with next-token prediction, and those predictions being stronger (less uniform over the vocabulary) when the preceding sequences get longer.
I wonder if there's a connection to having rhyme "live" in the last sound of each line, as opposed to the first.
A lot of memory seems to be linear, possibly because most information in the world is encoded linearly. If I was to tell you the 20th letter of the alphabet, I'd have to go through every letter it in my head. It's a linked-list data structure.
Even many memory techniques, like the mind palace, is ordered, with each item linking to the next.
I don't think this is the same as markov-chains or predicting the next item, but that it has to do with the most common data structure of information being linear.
As for making the first word rhyme instead of the last, that's an interesting thought! I actually have no idea. When I rhyme like that in my head, it sounds wrong, but I couldn't tell you the reason. You may be on to something.
Is meditation provably more effective than "forcing yourself to do nothing"?
Much like sleep is super important for good cognitive (and, of course, physical) functioning, it's plausible that waking periods of not being stimulated (i.e. of boredom) are very useful for unlocking increased cognitive performance. Personally I've found that if I go a long time without allowing myself to be bored, e.g. by listening to podcasts or audiobooks whenever I'm in transition between activities, I'm less energetic, creative, sharp, etc.
The problem is that as a prescription "do nothing for 30 minutes" would be rejected as unappealing by most. So instead of "do nothing" it's couched as "do this other thing" with a focus on breathing and so on. Does any of that stuff actually matter or does the benefit just come from doing nothing?
There are some styles of meditation that are explicitly described as "just sitting" or "doing nothing."
Kind of related Quanta article from a few days ago: https://www.quantamagazine.org/what-your-brain-is-doing-when-youre-not-doing-anything-20240205/
I think what those other things do is help you reach that state more easily and reliably. It's like a ritual that you do before the actual task, to get yourself into the right frame of mind and form a better connection, similar to athletes having pre game rituals.
Also yeah, I think it makes the boredom easier to manage and helps you slowly get into it, rather than being pushed into it without reference.
Probably a lot of other hidden benefits though, because most meditation practices have been optimized for hundreds of years, and are better than others for a reason.
The parallel to athlete pre game rituals is an interesting one, but I guess I'd be interested in seeing the comparison between the following two groups:
group A: is told to meditate the usual way for 30 minutes / day, and does
group B: is told to just sit there for 30 minutes / day, and does
So both of the groups considered are sitting quietly for 30 minutes, but one group is meditating while the other is just sitting there. In this comparison, we'd be explicitly ignoring the benefit from meditation which acts via the channel of just making it more likely you actually sit there quietly for 30 minutes.