I operate by Crocker’s rules.
niplav
Karma: 2,751
I only flossed on the right side of my mouth since 2023-07-08, and today asked the dentist to guess which side I’d flossed on. She guessed left.
Ah, but there is some non-empirical cognitive work done here that is really relevant, namely the choice of what equivalence class to put Bernie Bankman into when trying to forecast. In the dialogue, the empiricists use the equivalence class of Bankman in the past, while you propose using the equivalence class of all people that have offered apparently-very-lucrative deals.
And this choice is in general non-trivial, and requires abstractions and/or theory. (And the dismissal of this choice as trivial is my biggest gripe with folk-frequentism—what counts as a sample, and what doesn’t?)
Further evidence that I should write a factpost investigating whether attention spans have been declining.
I think the words “optimism” and “pessimism” are really confusing, because they conflate the probability, utility and steam of things:
You can be “optimistic” if you believe a good event is likely (or a bad one unlikely), you can be optimistic because you believe a future event (maybe even unlikely) is good, or you have a plan or idea or stance for which you have a high recursive self-trust/recursive reflectively stable prediction that you will engage in it.
So you could be “pessimistic” in the sense that extinction due to AI is unlikely (say, <1%) but you find it super bad and you currently don’t have anything concrete that you can latch onto to decrease it.
Or (in the case of e.g. MIRI) you might have (“indefinitely optimistic”?) steam for reducing AI risk, find it moderately to extremely likely, and think it’s going to be super bad.
Or you might think that extinction would be super bad, and believe it’s unlikely (as Belrose and Pope do) and have steam for both AI and AI alignment.
But the terms are apparently confusing to many people, and I think using these terminologies can “leak” optimism or pessimism from one category into another, and can lead to worse decisions and incorrect beliefs.
Oh nice, another post I don’t need to write anymore :-D
Some disjointed thoughts on this I had:
Feedback loops can be characterized along at least three axes:
Speed: How quickly you get feedback from actions you take. Archery has a very fast feedback loop: You shoot an arrow and one or two seconds later you see what the outcome is.
Noise: How noisy the feedback is. High-frequency trading has fast feedback loops, but they have a lot of noise, and finding the signal is the difficult part.
Richness: How much information you’re getting. Dating is one example: Online dating has extremely poor feedback loops: only a couple of bits (did the other person respond, what did they respond) per interaction, while talking & flirting with people in person has extremely rich feedback (the entire visual+acustic+sensory field (plus perhaps smell? Don’t know much about human pheromones))—probably kilobytes per minimal motor-action, and megabytes per second.
Fast & low-noise & rich feedback loops are the best, and improving the feedback loop in any of those dimensions is super valuable.
As an example, forecasting has meh feedback loops: they can be very slow (days at least, but more likely months or years (!)), the feedback is kind of poor (only a few bits per forecast), but at least there’s not that much noise (you forecast what the question says, but maybe this is why forecasters really don’t like questions resolving on technicalities—the closest thing to noise).
But one can improve the richness of the forecasting feedback loop by writing out ones reasoning, so one can update on the entire chain of thought once the resolution comes. Similarly, programming has much better feedback loops than mathematics, which is why I’d recommend that someone learn programming before math (in general learn things with fast & rich feedback loops earlier and slow & poor ones later).
Also, feedback loops feel to me like they’re in the neighbourhood of both flow & addiction? Maybe flow is a feedback loop with a constant or increasing gradient, while addiction is a feedback loop with a decreasing gradient (leading into a local & shallow minimum).
When I started reading the Sequences, I started doing forecasting on Metaculus within 3 months (while still reading them). I think being grounded at that time in actually having to do reasoning with probabilities & receiving feedback in the span of weeks made the experience of reading the Sequences much more lasting to me. I also think that the lack of focus on any rationality verification made it significantly harder to develop an art of rationality. If you have a metric you have something to grind on, even if you abandon it later.
Heuristics for choosing/writing good textbooks (see also here):
Has exercises
Exercises are interspersed in the text, not in large chunks (better at the end of sections, not just at the end of chapters)
Solutions are available but difficult to access (in a separate book, or on the web), this reduces the urge to look the solution up if one is stuck
Of varying difficulty (I like the approach Concrete Mathematics takes: everything from trivial applications to research questions)
I like it when difficulty is indicated, but it’s also okay when it’s said clearly in the beginning that very difficult exercises that are not marked are mystery boxes
Takes many angles
Has figures and illustrations. I don’t think I’ve encountered a textbook with too many yet.
Has many examples. I’m not sure yet about the advantage of recurring examples. Same point about amount as with figures.
Includes code, if possible. It’s cool if you tell me the equations for computing the likelihood ratio of a hypothesis & dataset, but it’s even cooler if you give me some sample code that I can use and extend along with it.
Uses typography
You can use boldface and italics and underlining for reading comprehension, example here.
Use section headings and paragraphs liberally.
Artificial Intelligence: A Modern Approach has one-three word side-notes describing the content of each paragraph. This is very good.
Distinguish definitions, proofs, examples, case-studies, code, formulas &c.
Dependencies
Define terms before they are used. (This is not a joke. Population Genetics uses the term “substitution” on p. 32 without defining it, and exercise 12-1 from Naive Set Theory depends on the axiom of regularity, but the book doesn’t define it.)
If the book has pre-requisites beyond what a high-schooler knows, a good textbook lists those pre-requisites and textbooks that teach them.
Indicators
Multiple editions are an indicator for quality.
Ditto for multiple authors.
A conversational and whimsy style can be nice, but shouldn’t be overdone.
Hot take: I get very little value from proofs in math textbooks, and consider them usually unnecessary (unless they teach a new proof method). I like the Infinite Napkin for its approach.
Wishlist
Flashcard sets that come together with textbooks. Please.
3blue1brown style videos that accompany the book. From Zero to Geo is a great step in that direction.
Laptop chargers are also an object for which it’s trivial to own multiple, at a low cost and high (potential) advantage.
Other examples: Take caffeine once a week (and nicotine (not cigarettes!) once a month) instead of never or daily. Leave social situations when they’re not fun or useful anymore. Do small cost-benefit analyses when they make sense[1].
See also: Solved Problems Repository, Boring Advice Repository.
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I’ve done two already this year: One to decide whether to leave a bootcamp, and another to decide which gym to select. (The second one misfired: I made a mistake in my calculation, taking only the way there as a cost and not the way back to public transport, which led me to choose the wrong one (by <100€ of cost over the time I go there)). I should’ve done the math (done ✓), then burned the math and gone with my gut (not done ✗).)
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I pledge to match the bounty with $500.
This seems very similar to the distinction between Steam and probability.
Sorry @RomanHauksson! Reversed the downvote. I didn’t realize this was a joke 🤦
Hello everybody!
I have done some commenting & posting around here, but I think a proper introduction is never bad.
I was Marxist for a few years, then I fell out of it, discovered SSC and thereby LW three years ago, started reading the Sequences and the Codex (yes, you now name them together). I very much enjoy the discussions around here, and the fact that LW got resurrected.
I sometimes write things for my personal website about forecasting, obscure programming languages and [REDACTED]. I think I might start cross-posting a bit more (the two last posts on my profile are such cross-posts).
I endorse spending my time reading, meditating, and [REDACTED], but my motivational system often decides to waste time on the internet instead.
I like this post! It doesn’t feel like a fully fleshed out partitioning, but its great that it was done at all.
Object Level and Basics Theory seem entangled with Feedback Loop Theory.
Subtype of Reading Theory: Math Theory, if you understand the relevant mathematics deeply enough that constitutes rationality.
I weak-down voted your comment, mainly because it has an underlying movement that is very should-y (beware other-optimizing!), based on at best medium information about the writer. I believe you probably wouldn’t have made a similar comment about someone spending two months learning the piano?
I would like it to be the case that Lesswrong stays a place where overly math-heavy video game discussion is socially allowed, and not where everything that doesn’t reduce x-risk is frowned upon. Let people play during the apocalypse, they should enjoy their remaining time.
There are further shards of a track record strewn across the internet:
The bets registry shows two bets lost by Eliezer Yudkowsky, none won
The public figure profile on Metaculus has no resolved predictions yet
To combat the negativity bias that internet comments have (you only comment if something is wrong/bad/broken), I’ll state that I find the current design intuitive, aesthetically pleasing, useful and on the whole a big step up from the past voting norms on the site, to the point that I don’t have any ideas how that particular piece of lesswrong could be improved.
And judging from Alcor and the Cryonics Institute logs he was not cryopreserved :-/
Generally, I’m super in favour of forecasting & prediction market type platforms.
I’d like to hear from you why you think another of platform is a good idea, since on the hobbyist side of things I know of at least 4 projects (foretold, predictionbook, good judgement open and metaculus) and two major prediction markets (predictit and augur).
Is there a reason why you believe that another such platform is a good idea, as opposed to additional work going into existing platforms?
I spent around 1½ months volunteering in a very rural Nepalese village in 2017, and some things in this post reminded me starkly of that. (Others were quite different, always in the “better” direction). For context, Nepal is one of the poorest Asian countries (150th out of 192 in the list of all countries by GPD per capita).
Things that were similar:
Cruelty towards animals. While I did not observe cruelty to the same extent as in the post, villagers were remarkably unconcerned about animal well-being, and would e.g. throw stones at the basically stray dogs in the village for fun when drunk. I was sleeping in the same bed as an educated man from Kathmandu, and one evening at cat was lying on his side of the bed, causing him to hit the cat so hard I feared it had suffered fatal injuries (I saw the cat later and it seemed fine). Fights between animals were a curiosity. I think this is not too surprising: People were used to slaughtering animals such as chickens with their own hands. I also saw children playing with young animals, so it wasn’t as clear cut as in the post above.
Hard work by pregnant women. I was told that one woman who was working with us digging out the foundation of the house we were building had a few months prior worked up to the day before going into labor, and then picked up the shovel/pickaxe the day after giving birth. She had apparently also carried 60kg bags with stones on her back while pregnant.
Cooking with smoke inside the house. In one house, the woman was cooking while the smoke was escaping only through the windows and the door, resulting in me (who was sleeping on the veranda) having headaches when I woke up. I can’t imagine how bad the air was inside. On one occasion a kid started burning plastic waste, without much regard for the fumes, and wasn’t stopped by the parent nearby.
Alcohol consumption. I don’t think it was as extreme as in the text above, but in the village I stayed in, there were no weekends, but instead holidays every ~4 days, during which the main source of entertainment was getting drunk. This resulted in cruelty towards animals, but not towards each other (afaik).
General poverty. I guess the villagers were maybe twice as rich as the peasants described in the post. I found it quite stark how the family I was staying with were sleeping mainly without blankets, warmed only by sleeping next to each other, even at temperatures near zero degrees. There was access to electricity, which was (exclusively?) used to power the light in the hut we were sleeping in.
Work ethic. This one is an ambiguous case: The people in the village were (of course) much more proficient at hard physical work than I was, to a sometimes embarrassing degree. But there was no regular schedule, and work was interrupted by long irregular breaks.
Things that were different:
Cruelty towards each other. Everyone was quite nice to each other, as far as I could tell. (This might’ve been confounded by the fact that people were especially nice to me, since I was a rich foreigner). I didn’t hear of cases of infanticide, rape, murder or even accidental death (apart from the earthquake a few years earlier).
Respect for property rights. There was no or very little stealing going on, at least from what I could observe. (One exception being some young kids stealing chocolate I’d brought for them and wanted to give out incrementally, but I’d expect a bunch of western 7-year olds to do the same thing).
Children drinking alcohol. I didn’t see this happening.
Random samples are valuable, even if small, the first data point carries the highest amount of information. Public opinion matters to some degree, I believe it matters a lot, and Twitter is a widely used platform, so it is decently representative of the public opinion on something (at least more representative than Lesswrong).
This post would be strongly improved by 3 examples of decisions you made differently due to this heuristic.