trentmkelly/LessWrong-43k huggingface dataset:
In my culture: the responsibilities of open source maintainers
by philh — 2020-04-13T13:40:01.174Z

If you maintain an open source project, what responsibilities do you have towards your users? Some recent drama (that I won't link to) reminded me that there are large differences in how people answer that question.

(In the drama in question, this wasn't the only thing at issue. But it was a relevant question.)

I thought I'd take a stab at describing my best guess as to how we answer it in my culture: in the culture that exists only in my mind, but that I think (and hope) many copies of me would implement, if we had that opportunity. (That culture doesn't attempt to exclude people who aren't copies of me, so it does need to be robust to attack. In my culture, we do not just assume in defiance of all evidence that everyone is friendly and trustworthy.)

Some of this will probably seem obvious to many readers, like "in my culture, murder is considered bad". Probably different bits to different readers. I'm interested in discovering which bits seem obvious to almost everyone, and which bits are controversial.

A lot of it follows from how I think about responsibility in general. But if you start to think "extending this response to this other situation, you'd get this, and that's a terrible idea"… in my culture, we don't immediately assume from this that I'm endorsing a terrible idea. Instead we check. Maybe I disagree that that's how it extends. Maybe I hadn't thought about this, and you can change my mind about the initial response. Maybe I just straightforwardly endorse a terrible idea: in that case, it'll be much easier to push back once you've gotten me to admit to it.

I do not intend, in this essay, to discuss whether any particular person or group is living up to the standards I outline here. I may do that in future. But how likely that is, and what that follow-up looks like, depends on whether the responses to this essay suggest a lot of people agree with my culture.

I think there are at least three important limitations to this essay. One is that I've never been a maintainer of an open source project that had users other than myself (that I knew of), though I've made small contributions to a few. As such, I don't really know what the experience is like or how my culture deals with its challenges, much like I don't really know how my culture deals with the challenges of living in Antarctica. I can make educated guesses, but that's all they are. I'm not going to...
College advice for people who are exactly like me
by benkuhn — 2020-04-13T01:30:02.265Z

It’s college decision season! To celebrate, I’ve been thinking about what I would have told myself in 2011 when I was deciding where to attend. Here’s a stitching-together of several emails I sent to friends who asked for college advice, plus a few additional thoughts.

As a caveat: I really do mean that this advice is for people who are exactly like me! So please don’t take my word for things—lots won’t apply to you because we’re different. As with all advice: think about it, see what resonates with you, consider reversing it, and generally take it as one of many data points.

Keep the goal in mind

At the end of college you should probably have a good idea about what to do next.

Here’s the default way to figure that plan out: (1) don’t think about it for three years; (2) get really stressed during your senior year because you have no idea what you want to do; (3) notice that all your friends are interviewing for jobs in consulting, finance, and MicroFaceGoogAzon; (4) accept a job from consulting, finance, or MicroFaceGoogAzon.

This process… could stand to be improved. Plan ahead and don’t make a “safe” decision based on social defaults or fear of not having a job. The most important question to answer is “what kind of jobs exist that (a) I would enjoy and (b) I have a reasonable shot at getting?”

Yes, it is scary and stressful to stare this question (especially b!) in the face, but it should be a lot scarier to waste years doing something safe instead of something awesome.

That isn’t to say you need to know with any amount of certainty what you want to do. (That’s unreasonable and it will change anyway.) Just that it’s good if your choices during college are informed by some broad sense of what they’re setting you up for later. “Plans are useless; planning is indispensable.”

Social environment >> classes

The other useful thing to get out of college is a bunch of awesome, smart and competent friends. I’d recommend optimizing for that, probably more than educational quality or status.

Sub-points:

On “density of people who are great at STEM,” my impression is that research universities score a lot better than small liberal arts colleges. I didn’t apply to Caltech, but by some (lagging) metrics it has an even higher density of accomplished people than other top schools.

Control over who you live with/near is a huge selling point, since it mostly determines yo

...
Narrative Direction and Rebellion
by  — 2020-04-13T01:07:12.454Z

[Cross-posted from Grand, Unified, Crazy.]

This is the fourth post in what has been a kind of accidental sequence on life narratives from before I started posting things on LessWrong. Previously: Narrative Dissonance, Where the Narrative Stops, and Narrative Distress and Reinvention.

In Where the Narrative Stops I briefly mentioned the hippie revolution as a rebellion against the standard narrative of the time. This idea combined in my brain a while ago with a few other ideas that had been floating around, and now I’m finally getting around to writing about it. So let’s talk about narrative rebellions.

I’ve previously defined narratives as roughly “the stories we tell about ourselves and others that help us make sense of the world”. As explored previously in the series, these stories provide us with two things critical for our lives and happiness: a sense of purposeful direction, and a set of default templates for making decisions. So what happens when an individual or a demographic group chooses to rebel against the narrative of the day? It depends.

Rebellions are naturally framed in the negative: you rebel against something. With a little work you can manage to frame them positively, as in “fighting for a cause”, but the negative framing comes more naturally because it’s more reflective of reality. While some rebellions are kicked off by a positive vision, the vast majority are reactionary; the current system doesn’t work, so let’s destroy it. Even when there is a nominally positive vision (as in the Russian Revolution, which could be framed as a “positive” rebellion towards communism) there is usually also a negative aspect intermingled (the existing Russian army was already ready to mutiny against Russia’s participation in the First World War) and it can be difficult to disentangle the different causes.

In this way, narrative and socio-cultural rebellions are not that different from militaristic and geo-political ones. You can sometimes attach a positive framing, but the negative framing is both default, and usually dominant.

We’ll come back to that. For the moment let’s take a quick side-trip to Stephen Covey’s Principle-centered Leadership. One of the metaphors he uses in that book (which I didn’t actually include in my post about ...
How should we model complex systems?
by ClimateDoc — 2020-04-12T21:23:30.893Z

By "complex", I mean a system for which it would be too computationally costly to model it from first principles e.g. the economy, the climate (my field, by the way). Suppose our goal is to predict a system's future behaviour with minimum possible error given by some metric (e.g. minimise the mean square error or maximise the likelihood). This seems like something we would want to do in an optimal way, and also something a superintelligence should have a strategy to do, so I thought I'd ask here if anyone has worked on this problem.

I've read quite a bit about how we can optimally try to deduce the truth e.g. apply Bayes' theorem with a prior set following Ockham's razor (c.f. Solomonoff induction). However, this seems difficult to me to apply to modelling complex systems, even as an idealisation, because:

Since we cannot afford to model the true equations, every member of the set of models available to us is false, so the likelihood and posterior probability for each will typically evaluate to zero given enough observed data. So if we want to use Bayes' theorem, the probabilities should not mean the probability of each model being true. But it's not clear to me what they should mean - perhaps the probability that each model will give the prediction with the lowest error? But then it's not clear how to do updating, if the normal likelihoods will typically be zero.

It doesn't seem clear that Ockham's razor will be a good guide to giving our models prior probabilities. Its use seems to be motivated by it working well for deducing fundamental laws of nature. However, for modelling complex systems it seems more reasonable to me to give more weight to models that incorporate what we understand to be the important processes - and past observations can't necessarily help us tell what processes are important to include, because different processes may become important in future (c.f. biological feedbacks that may kick in as the climate warms). This could perhaps be done by having a strategy for deriving approximate affordable models from the fundamental laws - but is it possible to say anything about how an agent should do this?

I've not found anything about rational strategies to approximately model complex systems rather than derive true models. Thank you very much for any thoughts and resources you can share.
Leaving Things For Others
by jefftk — 2020-04-12T11:50:02.324Z

With covid I've seen many calls to leave things for others:

Don't
buy WIC-eligible items, leave them for people who can only buy
those specific items.

Don't
use grocery delivery services, leave the slots for people who
can't go to stores.

Don't
buy masks, leave them for healthcare workers.

Don't
buy groceries at the beginning of the month, leave them for people
using EBT, which renews on the first.

Let's set aside for the moment whether these are accurate, [1] does
this kind of thing work? These are all cases where at the current
price there are many more people who want the thing than people who
can supply the thing. In normal times the price would rise until
these balanced, but in emergencies our society has chosen not to allow
that. Let's say you're considering buying store-brand kidney beans, a
WIC-eligible item, the last one on the shelf. If you choose not
to buy it, what happens?

One possibility is that it stays on the shelf until someone who can
only buy the store brand of kidney beans comes along, and they're able to buy
it. Another is that someone who doesn't know to look for the WIC
symbol comes along, and they buy it instead.

There are about 7M
people covered by WIC in the US, out of a population of 330M, so 1 in
50 shoppers is a decent estimate for what fraction care about the WIC
status of items. This means that whether leaving it for someone else
is likely to work depends enormously on whether you expect most of
society to be going along with it. If it's just you and a few other
scrupulous people, probably someone else who isn't on WIC buys the
kidney beans, while if nearly everyone is doing this then it probably works.

The thing is, though, getting everyone on board with one of these,
distributing the message widely so that everyone hears it, explaining
the details of why it matters so people agree and go along with it, is
really hard! And pretty much all the time, instead of putting out
calls for individuals to leave things for others it makes more sense
for sellers to apply restrictions. For example, UK supermarkets have
been trying to restrict
delivery to vulnerable people and US supermarkets have been
designating hours for vulnerable
people and essential workers.

Avoiding things in the hope that someone who especially needs them
will be able to get them instead usually won't work, and isn't...
The History of Color in Chinese
by lsusr — 2020-04-12T04:05:49.885Z
Link: https://www.lsusr.com/blog/history-of-color-in-chinese.html

The linked post is better because you can see the colors.

Older Chinese characters tend to be simpler. You can use this to measure the relative ages of ancient concepts.

For example, the Chinese characters for colors are:

白 white

黑 black

红 red

橙 orange

黄 yellow

绿 green

蓝 blue

紫 purple

The simplest characters are written with a single radical. The oldest radicals are all Kangxi radicals. White 白, black 黑 and yellow 黄 are all written with a single Kangxi radical. The other colors contain at least two radicals.

This fits almost perfectly with Lazarus Geiger’s evolutionary progression[1] which goes black and white ➝ red ➝ yellow ➝ green ➝ blue. The only color out-of-place is red 红.

Wait a minute...I can't recall ever encountering the character "红" in ancient Chinese poetry.

That's because "红" is a relatively new character for the word "red". The traditional five colors 青黄赤白黑 use the Kangxi radical "赤" for red instead of "红". Mystery solved!

If we use the ancient character "赤" for red instead of the modern character "红" then the first four colors in Geiger's sequence are all simple Kangxi radicals and the rest are all composite characters with at least two radicals.

Blue-Green

It gets better. Did you notice how we've only used four of the five colors so far? Take a closer look at the five colors 青黄赤白黑. They include qīng 青, the color of nature, spanning blue and green. It is the 5th and 6th color in Geiger's progression. Qīng 青 is written with a single Kangxi radical.

If all you have are black, white, red and yellow, and then creating a single new color for blue and green is not unusual.

Bastian also argued that Tagalog speakers in the Philippines had not even distinguished between green and blue until the arrival of the Spanish colonizers, because the Tagalog words for “green” and “blue” were clearly recent borrowings from Spanish verde and azul. And he claimed that the language of the Teda tribe in Chad still did not distinguish green from blue at all

― Through the Looking Glass: Why the World Looks Different in Other Languages by Guy Deutscher

Today, qīng 青 is used in more poetic contexts while green 绿 and blue 蓝 are used in less poetic contexts.

For more information about Lazarus Geiger's work, see Chapter 4 of Through the Looking Glass: Why the World Looks Different in Other Languages by Guy Deutscher. ↩︎
Review of "Lifecycle Investing"
by JessRiedel — 2020-04-12T03:12:58.874Z

Crossposted from my blog

Summary

In this post I review the 2010 book “Lifecycle Investing” by Ian Ayres and Barry Nalebuff. (Amazon link here; no commission received.) They argue that a large subset of investors should adopt a (currently) unconventional strategy: One’s future retirement contributions should effectively be treated as bonds in one’s retirement portfolio that cannot be efficiently sold; therefore, early in life one should balance these low-volatility assets by gaining exposure to volatile high-return equities that will generically exceed 100% of one’s liquid retirement assets, necessitating some form of borrowing.

“Lifecycle Investing” was recommended to me by a friend who said the book “is extremely worth reading…like learning about index funds for the first time…Like worth paying >1% of your lifetime income to read if that was needed to get access to the ideas…potentially a lot more”. Ayres and Nalebuff lived up to this recommendation. Eventually, I expect the basic ideas, which are simple, to become so widespread and obvious that it will be hard to remember that it required an insight.

In part, what makes the main argument so compelling is that (as shown in the next section), it is closely related to an elegant explanation for something we all knew to be true — you should increase the bond-stock ratio of your portfolio as you get older — yet previously had bad justifications for. It also gives new actionable, non-obvious, and potentially very important advice (buy equities on margin when young) that is appropriately tempered by real-world frictions. And, most importantly, it means I personally feel less bad about already being nearly 100% in stocks when I picked up the book.

My main concerns, which are shared by other reviewers and which are only partially addressed by the authors, are:

Future income streams might be more like stocks than bonds for the large majority of people.

Implementing a safe buy-and-hold leveraged strategy in the real world could be even more of a headache, and incur more hidden costs, than the authors suppose.

If interest rates go up enough and expected stock returns go down enough, the whole thing could be rendered moot.

By far the best review of this book I’ve found after a bit of Googling is the one by Fredrick Vars, a law professor at the University of Alabama: [PDF]. Read that. I wrote most...
Seemingly Popular Covid-19 Model is Obvious Nonsense
by Zvi — 2020-04-11T23:10:00.594Z

Previous Covid-19 thoughts: On R0, Taking Initial Viral Load Seriously

Epistemic Status: Something Is Wrong On The Internet. Which should almost always be ignored even when you are an expert, and I am nothing of the kind. Thus, despite this seeming like a necessary exception, I expect to regret writing this.

People are taking the projection of 60,000 American deaths from Covid-19 as if it were a real prediction. This number is being used to make policy, to deny states medical equipment and to make plans that spend trillions of dollars and when to plan to reopen entire economies.

Ignoring this in the hopes it will go away does not seem reasonable.

My suspicions that this was necessary were more than confirmed when, failing to realize just how obvious the nonsense in question was and thinking I needed to justify labeling it nonsense, I wrote a reference post called The One Mistake Rule.

The second comment on that post was to argue that we should indeed use exactly the model that motivated me to write the post. The comment is here in full:

>> If a model gives a definitely wrong answer anywhere, it is useless everywhere.

Except if it needs to be used right now to make important decisions and it’s the best model we have. See: https://covid19.healthdata.org/united-states-of-america

We could plausibly think this is the best model we have? Oh my are we screwed.

The Baseline Scenario That Makes No Sense

There seems to be a developing consensus on many fronts, for now, that the model linked above represents our reality. The model says it is ‘designed to be a planning tool’ and that is exactly what is happening here.

What is this model doing? Time to look at the pdf.

Here’s the money quote that describes the core of what they are actually doing.

A covariate of days with expected exponential growth in the cumulative death rate was created using information on the number of days after the death rate exceeded 0.31 per million to the day when 4 different social distancing measures were mandated by local and national government:

School closures, non-essential business closures including bars and restaurants, stay-at-home recommendations, and travel restrictions including public transport closures. Days with 1 measure were counted as 0.67 equivalents, days with 2 measures as 0.334 equivalents and with 3 or 4 measures as 0. For states that have not yet implemented all of the

...
Law school taught me nothing
by ketchupduck — 2020-04-11T14:30:04.254Z

This is a list of all the topics a standard Civil Procedure class in law school covers, with the concepts I remember highlighted in blue and those I don't in red.

There is a lot more red than blue, which is shocking because:

I graduated from law school less than a year ago
I paid slightly more than $6,000 for my Civil Procedure class
I received an A in the class

This post is an exploration of possible hypotheses for why this happened.

Epistemic status: spinning hypotheses from personal experience, so take lightly.

User error

In a world where even surgeons sometimes leave their tools inside their patient's body, the ghost of user error haunts us all. Maybe I am the problem, and my skills, situation, or environment are not suited for remembering basic facts that I paid $6,000 to learn. There are a few ways in which I could be failing at this task.

First, maybe I am not good at school. I reject this hypothesis out-of-hand, mostly because I have a fragile ego, but also partly because it's not true. I scored in the 99th percentile on the LSAT, attended a top-3 law school in the US, and graduated with honors. All these points don't insulate me from being bad at school, but the vast majority of law school attendees are even worse at school (not learning) than me. You know that scene in Taken where Liam Neeson says "I have a very particular set of skills..."? That's me with school; I am good at school. If I can't retain this knowledge, the entire system might as well commit its tax-advantaged resources to rolling boulders up hills for all the good they're doing.

Second, maybe I have framed the problem incorrectly. It's true that retaining material from a Civil Procedure class is not a standard validated benchmark. However, I promise you that I'll receive similar results on any benchmark you devise for 90% of the law school classes I took; I only picked Civil Procedure for this exercise because it is alphabetically first in the list of required law school classes I took. My problem is not that I don't remember Civil Procedure; my problem is that I remember almost nothing.

Third, maybe I didn't study properly. This, unlike the previous two, is a plausible hypothesis. I did work my way through about ~70% of the study tactics the professor recommended, including attending class regularly, completing all the...
Are there any active play-money prediction markets online?
by kickboxer — 2020-04-11T12:16:10.251Z

Prediction markets that trade in play-money rather than real money seem like a great idea to me: they avoid all the legal issues of real prediction markets, they don't seem to be much less accurate, and it might be fun to participate in them. But I can't find any active ones online.

The only ones I can see are ideosphere.com and alphacast.cultivateforecasts.com, but there's almost no activity in either. Metaculus is very active but isn't a prediction market. I've also heard of a few like NewsFutures which have completely died. Are there any active ones that I'm not aware of? If not, why do all play-money prediction markets die?