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.

(sci-fi take?) If time travel and time loops are possible, would this not be the (general sketch of the) scenario under which it comes into existence:

1. a lab figures out some candidate particles that could be sent back in time, build a detector for them and start scanning for them. suppose the particle has some binary state. if the particle is +1 (-1) the lab buys (shorts) stock futures and exits after 5 minutes

2. the trading strategy will turn out to be very accurate and the profits from the trading strategy will be utilized to fund the research required to build the time machine

3. at some arbitrary point in the future, eventually, the r&d and engineering efforts are successful. once the device is built, the lab starts sending information back in time to tip itself to future moves in stock futures (the very same particles it originally received). this closes the time loop and guarantees temporal consistency

Reasons why this might not happen:

1. time doesn't work like this, or time travel / loops aren't possible
2. civilization doesn't survive long enough to build the device
3. the lab can't commit to using its newfound riches to build the device, breaking the logic and preventing the whole thing from working in the first place

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:

• a family is either fertile or infertile, and fertility is hereditary
• the modal fertile family can have up to 10 kids, the modal infertile family can only have 2 kids
• in the olden days families aimed to have as many kids as they could
• now families aim to have 2 kids each

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?

I often mistakenly behave as if my payoff structure is binary instead of gradual. I think others do too, and this cuts across various areas.

For instance, I might wrap up my day and notice that it's already 11:30pm, though I'd planned to go to sleep an hour earlier, by 10:30pm. My choice is, do I do a couple of me-things like watch that interesting YouTube video I'd marked as "watch later", or do I just go to sleep ASAP? I often do the former and then predictably regret it the next day when I'm too tired to function well. I've reflected on what's going on in my mind (with the ultimate goal of changing my behavior) and I think the simplest explanation is that I behave as if the payoff curve, in this case of length of sleep, is binary rather than gradual. Rational decision-making would prescribe that, especially once you're getting less rest than you need, every additional hour of sleep is worth more rather than less. However, I suspect my instinctive thought process is something like "well, I've already missed my sleep target even if I go to sleep ASAP, so might as well watch a couple of videos and enjoy myself a little since my day tomorrow is already shot."

This is pretty terrible! It's the opposite of what I should be doing!

Maybe something like this is going on when poor people spend a substantial fraction of their income on the lottery (I'm already poor and losing an extra $20 won't change that, but if I win I'll stop being poor, so let me try) or when people who are out of shape choose not to exercise (I'm already pretty unhealthy and one 30-minute workout won't change that, so why waste my time.) or when people who have a setback in their professional career have trouble picking themselves back up (my story is not going to be picture perfect anyway, so why bother.)

It would be good to have some kind of mental reframing to help me avoid this prectictably regrettable behavior.

The more complex the encoding of a system (e.g. of ethics) is, the more likely it is that it's reverse-engineered in some way. Complexity is a marker of someone working backwards to encapsulate messy object-level judgment into principles. Conversely, a system that flows outward from principles to objects will be neatly packed in its meta-level form.

In linear algebra terms, as long as the space of principles has fewer dimensions than the space of objects, we expect principled systems / rules to have a low-rank representation, with a dimensionality approaching that of the space of principles and far below that of the space of objects.

As a corrolary, perhaps we are justified in being more suspicious of complex systems over simple ones, since they come with a higher risk that the systems are "insincere", in the sense that they were deliberately created with the purpose of justifying a particular outcome rather than being genuine and principled.

This rhymes with Occam's razor, and also with some AI safety approaches which planned to explore whether dishonesty is more computationally costly than honesty.

Does this mean that meta-level systems are memetically superior, since their informational payloads are smaller? The success of Abrahamic religions (which mostly compress neatly into 10-12 commandments) might agree with this.

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.

It feels like (at least in the West) the majority of our ideation about the future is negative, e.g.

• popular video games like Fallout
• zombie apocalypse themed tv
• shows like Black Mirror (there's no equivalent White Mirror)

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)?

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?

Simple math suggests that anybody who is selfish should be very supportive of acceleration towards ASI even for high values of p(doom).

Suppose somebody over the age of 50 thinks that p(doom) is on the order of 50%, and that they are totally selfish. It seems rational for them to support acceleration, since absent acceleration they are likely to die some time over the next 40ish years (since it's improbable we'll have life extension tech in time) but if we successfully accelerate to ASI, there's a 1-p(doom) shot at an abundant and happy eternity.

Possibly some form of this extends beyond total selfishness.

Immorality has negative externalities which are diffuse, and hard to count, but quite possibly worse than its direct effects.

Take the example of Alice lying to Bob about something, to her benefit and his detriment. I will call the effects of the lie on Alice and Bob direct, and the effects on everybody else externalities. Concretely, the negative externalities here are that Bob is, on the margin, going to trust others in the future less for having been lied to by Alice than he would if Alice has been truthful. So in all of Bob's future interactions, his truthful counterparties will have to work extra hard to prove that they are truthful, and maybe in some cases there are potentially beneficial deals that simply won't occur due to Bob's suspicions and his trying to avoid being betrayed. 

This extra work that Bob's future counterparties have to put in, as well as the lost value from missed deals, add up to a meaningful cost. This may extend beyond Bob, since everyone else who finds out that Bob was lied to by Alice will update their priors in the same direction as Bob, creating second order costs. What's more, since everyone now thinks their counterparties suspect them of lying (marginally more), the reputational cost of doing so drops (because they already feel like they're considered to be partially liars, so the cost of confirming that is less than if they felt they were seen as totally truthful) and as a result everyone might actually be more likely to lie.

So there's a cost of deteriorating social trust, of p*ssing in the pool of social commons.

One consequence that seems to flow from this, and which I personally find morally counter-intuitive, and don't actually believe, but cannot logically dismiss, is that if you're going to lie you have a moral obligation to not get found out. This way, the damage of your lie is at least limited to its direct effects.

Does belief quantization explain (some amount of) polarization?

Suppose people generally do Bayesian updating on beliefs. It seems plausible that most people (unless trained to do otherwise) subconsciosuly quantize their beliefs -- let's say, for the sake of argument, by rounding to the nearest 1%. In other words, if someone's posterior on a statement is 75.2%, it will be rounded to 75%.

Consider questions that exhibit group-level polarization (e.g. on climate change, or the morality of abortion, or whatnot) and imagine that there is a series of "facts" that are floating around that someone uninformed doesn't know about. 

If one is exposed to facts in a randomly chosen order, then one will arrive at some reasonable posterior after all facts have been processed -- in fact we can use this as a computational definition of the what it would be rational to conclude.

However, suppose that you are exposed to the facts that support the in-group position first (e.g. when coming of age in your own tribe) and the ones that contradict it later (e.g. when you leave the nest.) If your in-group is chronologically your first source of intel, this is plausible. In this case, if you update on sufficiently many supportive facts of the in-group stance, and you quantize, you'll end up with a 100% belief on the in-group stance (or, conversely, a 0% belief on the out-group stance), after which point you will basically be unmoved by any contradictory facts you may later be exposed to (since you're locked into full and unshakeable conviction by quantization).

One way to resist this is to refuse to ever be fully convinced of anything. However, this comes at a cost, since it's cognitively expensive to hold onto very small numbers, and to intuitively update them well.

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.

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:

• the virtual nature of the majority of the experiments, making them easily replicable
• the proliferation of services like github, making it very easy to replicate others' experiments
• (a combination of the points above) the expectation that one would make one's experiments easily available for replication by others

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.

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.