trentmkelly/LessWrong-43k huggingface dataset:
Book Review Review (end of the bounty program)
by Ruby — 2021-10-15T03:23:04.300Z

This post calls an official end to the LessWrong Sep/Oct 2021 Book Review Bounty Program!! A huge thank you to everyone who submitted! You've produced some superb content.

I launched the program with the goals of getting more great content on the site, encouraging people to practice useful skills, getting people to learn about many aspects of the world, and just surfacing some talented writers/thinkers. I think the program succeeded.

36 book reviews were posted in the last month. I have paid out $500 to nine of them so far, and expect to pay out several more once: (a) I've finished reading them all, (b) the authors have messaged me to request payment.

If you want to collect the bounty for your book review, please message me via Intercom or email (ruby@lesswrong.com)

In terms of encouraging people to learn about many aspects of the world, the submitted book reviews spanned parenting, history, philosophy, immigration policy, magic, the microbiome, operational excellence, mathematical logic, moral psychology, and yet more topics.
These are some of my personal favorites that I've read so far, in no particular order:

[Book review] Gödel, Escher, Bach: an in-depth explainer
Ordinary People and Extraordinary Evil: A Report on the Beguilings of Evil
[Book Review] "The Vital Question" by Nick Lane
Book Review: Rise and Fall of the Great Powers
Book Review: How To Talk So Little Kids Will Listen
A review of Steven Pinker's new book on rationality
Insights from Modern Principles of Economics
Book review: The Checklist Manifesto
Book Review: Churchill and Orwell

You can see all of the reviews in the Book Reviews tag. Make sure to click "load more"

As far as surfacing new talent goes, quite a few contributors were making their first post on LessWrong. Kudos to Sam Marks and TheRealSlimHippo, authors of two of my favorites listed above, who are new to posting. Great first contributions.

One review author told me that he was initially too shy to write anything on LessWrong, but that the $500 incentive was actually enough to get him to do it. He sent me this image:

Reflections

The bounty program demonstrated to me that we can incentivize the creation of good content with bounties, perhaps (in this case) making it easier for people to spend the 10-30 hours required to produce a good review. I plan for LessWrong to experiment more with such programs.

If I have any reservations about this program...
Interpreting Diagnostic Tests
by Tornus — 2021-10-15T02:58:37.139Z

Most people—including most physicians—don’t understand how to correctly interpret diagnostic medical tests.

Here’s a concrete example: the BinaxNOW home COVID test has 68% sensitivity (chance of giving a positive result if you have COVID) and 99% specificity (chance of giving a negative result if you don’t have COVID). So does that mean positive test results are 68% accurate and negative results are 99% accurate? Unfortunately, it isn’t that simple.

My COVID risk level means that for me, a positive test only has a 6% chance of being accurate but a negative test has a 99.97% chance of being accurate. Your odds might be completely different, depending on your risk level.

In this post, I’ll explain how it all works and show you how to understand test results. (Spoiler: aside from some medical terminology, this is just an application of Bayes’ Theorem).

Update: mayleaf has an excellent piece on interpreting COVID test results using Bayes factors, which I highly recommend.

Sensitivity and specificity

Let’s start with the easy part. Sensitivity and selectivity measure the intrinsic accuracy of a test regardless of who’s taking it.

Sensitivity is how often a test gives a positive result when testing someone who is actually positive. BinaxNOW has a sensitivity of 68%, so if 100 people with COVID take a BinaxNOW test, 68 of them will test positive.

Specificity is how often a test gives a negative result when testing someone who is actually negative. BinaxNOW has a specificity of 99%, so if 100 people who do not have COVID take a BinaxNOW test, 99 of them will test negative.

Why does your risk level matter?

Let’s do a thought experiment:

If 100 people who have COVID take BinaxNOW tests, they will get 68 positive results and 32 negative results. All the positives are correct (0% false positive rate) and all the negatives are incorrect (100% false negative rate).

If 100 people who don't have COVID take BinaxNOW tests, they will get 1 incorrect positive result (100% false positive rate) and 99 correct negative results (0% false negative rate).

The same test has completely different false positive and false negative rates, depending on how likely it is that the person taking it has COVID. So how do I calculate the test’s accuracy based on my risk level?

First, some terminology

The numbers I want are:

Positive predictive value (PPV): how accurate is a positive test result? If I get...
Book Review: A Pattern Language by Christopher Alexander
by lincolnquirk — 2021-10-15T01:11:24.327Z

This is an ambitious, opinionated book about how to live.

Ambitious, because its scope is enormous -- how far apart cities should be placed ("2. the distribution of towns"), zoning ("3. city country fingers"), street maps ("23. parallel roads"), recreation ("31. promenade"), beauty ("62. high places), interior architecture ("133. staircase as a stage"), interior design ("200. open shelves"), building ("214. root foundations"), and decoration ("253. things from your life").

Opinionated, because it has specific prescriptions for all these 253 things. Some are fairly wacky: "3. city country fingers" recommends interlacing urban and countryside in "fingers", so that nobody in the city is ever more than a ten-minute walk from the countryside. Some are incredibly specific: "22. nine percent parking" sets an upper limit on land area used by parking, and has dire warnings about "the fabric of society is threatened" if exceeding that even in a small area.

A Pattern Language is meticulously organized and numbered from big to small -- high to low abstraction level. Each prescription has an epistemic status:

In the patterns marked with two asterisks, we believe that we have succeeded in stating a true invariant: in short, that the solution we have stated summarizes a property common to all possible ways of solving the problem. [...]

In the patterns marked with one asterisk, we believe that we have made some progress towards identifying such an invariant: but that with careful work it will certainly be possible to improve on the solution. In these cases, we believe it would be wise for you to treat the pattern with a certain amount of disrespect. [...]

Finally, in the patterns without an asterisk, we are certain that we have not succeeded in defining a true invariant... In these cases we have still stated a solution, in order to be concrete.

We even see a form of wiki-style hyperlinking -- each pattern references certain other patterns as being particularly related. These hyperlinks are grouped into going upwards or downwards on the abstraction scale:

Notice that the other patterns mentioned by name at the beginning and at the end, of the pattern you are reading, are also possible candidates for your language. The ones at the beginning will tend to be "larger" than your project. Don't include them, unless you have the power to help create these patterns, at least in a smal

...
Paying people to vote for good policies.
by Matteodimaio — 2021-10-15T01:00:22.734Z

Confidence level/s: very speculative, low

The problem

It seems that one of the key optimization problems in democratic decision making is that we often have little incentive to vote for policies that we truly think will increase the public utility.

As per Jess Whittlestone at Vox:

While we have strong social incentives to defend our groups' political beliefs, we have very little incentive to form accurate political beliefs. Most people acknowledge that political issues are incredibly important for society as a whole, but as individuals we’re not necessarily rewarded for how rational and truth-seeking we are about politics.

Consider the contrast between our beliefs about politics and our beliefs about the physical environment immediately around us. If I believe that the pavement ahead of me is clear and I’m wrong — there’s actually a lamppost in my path, say — I’m going to quickly suffer as a result of my false belief. This gives me a clear incentive to form accurate beliefs about the physical environment immediately around me.

The same logic doesn’t apply in politics. If I believe that immigration to my country from other countries is harmful, that false belief doesn’t harm me directly. Even if I vote in an election based on that belief, the chances of my belief actually affecting the outcome of the election are so slim that a poorly chosen vote is unlikely to hurt me personally.

The solution

A possibly workable idea to improve the utility-maximizing effectiveness of our politics is based on the premise that we might better at deciding as a demos whether a policy was good or not in retrospect than we are at deciding whether a policy will be good in the future.

Thus, imagine a political economy in which every five to ten years after a government institutes a policy/set of policies, a decision oracle (everyone in the country) votes on whether they were good or not. If they are found “good” with a high majority—say 70%—everyone who voted for them gets paid; if they are indecisive (i.e. 50-50) no one gets paid. If they are found “bad”, everyone who voted against them gets paid. We could fund this with a zero-sum prediction market, in which citizens could optionally bet on whether the policy would eventually become to be considered successful. Or, we could set up the system as a way to distribute “social dividends/UBI”, which everyone seems to agree are soon...
Framing Practicum: Semistable Equilibrium
by DirectedEvolution — 2021-10-14T23:31:51.515Z

Thanks to John Wentworth for conceiving and executing the concept of a framing practicum, as well as much of the format and language of this post!

This is a

framing practicum

post. We’ll talk about what a semistable equilibrium is, how to recognize semistable equilibria in the wild, and what questions to ask when you find it. Then, we’ll have a challenge to apply the idea.

Today’s challenge: come up with 3 examples of semistable equilibria which do not resemble any you’ve seen before. They don’t need to be good, they don’t need to be useful, they just need to be novel (to you).

Expected time: ~15-30 minutes at most, including the Bonus Exercise.

What’s Semistable Equilibrium?

In Rebel Without a Cause, Jim Stark and Buzz Gunderson are racing their cars at top speed toward a cliff, with the gas pedal strapped down. The first to jump out of their car is chicken. Buzz's leather jacket gets caught on the door handle. He's unable to jump free, and plunges over the cliff to his death.

The cliff is a semistable equilibrium. The idea of the "chickie run" is that the cliff causes the racers to decelerate, so that their velocity approaches zero as they near the cliff's edge. That's how it works out for Jim. On the other side of the cliff, however, Buzz's velocity increases again as he hurtles toward the ground.

In general, a semistable equilibrium will approach an equilibrium point if it starts on one side, but will move away from the equilibrium point if it starts on the other side.

What To Look For

A semistable equilibrium needs a threshold that attracts and slows things down if approached from one side, but repels or launches things away if they're on the other side. If there's a point "point of no return," that may be suggestive of a semistable equilibrium. There's a zone in which disturbances lead to a return to rest, and a second zone just beyond leading to ongoing activity. It's possible to have multiple equilibria in the system. All that's required is that there is a point that attracts things from one zone, but repels things in another adjacent zone.

Whether or not it is common to find the system at the equilibrium point will heavily depend on the direction and relative magnitude of disturbing forces.

Useful Questions To Ask

Unlike with a stable equilibrium, the effect of nudges in a semistable equilibrium depend heavily on how close we are to the equilibrium point....
Classical symbol grounding and causal graphs
by Stuart_Armstrong — 2021-10-14T18:04:32.452Z

I've been using the term symbol-grounding in a slightly different way to how people often use it. I've been using it to mean:

"This agent's symbol is reliable enough, across a wide variety of environments, for me to use it as evidence for the referent of that symbol."

What do I mean by "referent of that symbol"? I mean the actual object or concept that the symbol is referring to[1] ("referring", hence referent).

Whereas classical symbol grounding is closer to:

"This agent understands what this symbol means and refers to."

My definition is empirical, and implies the classical definition (though the reverse is not true): if an entity uses a symbol reliably enough for me to know that it tracks the real thing, then I can generally assume they understand what they're talking about.

Here I'll look at the connection between the definition I've been using and the classical one, and see how we could get empirical evidence for the classical one as well - what would it take to convince us that this agent actually understands what a symbol means?

Sufficient, not necessary

Let's consider a knowledgeable meteorologist, with a full suite of equipment, motivated to figure out the temperature of a room. According to my definition, if we could detect the variable corresponding to "temperature" in their head, this variable would be well grounded to the actual temperature[2]; the same would be true for the classical definition: the meteorologist understands what temperature is.

We can contrast that with a digital thermometer, lying in the room. The readout of that thermometer is not grounded, according to either definition.

Let's put this into a causal graph:

We have the true temperature, the thermometer readout, and the meteorologist's verdict. These three nodes are highly correlated under standard situations. Why then do I feel that the meteorologist's verdict was well grounded while the thermometer's readout was not?

Because the meteorologist could adapt to interfering variables in a way that the thermometer could not. Suppose the thermometer was put in the sun or in a fridge. Or if its readout lost power and started flashing 99.99. Then the thermometer could not adapt or correct its estimate. But the meteorologist could; if they found their own thermometer was corrupted, they could move it around, fix it, mentally correct it, or just ignore it, and get a more accurate estimat...
Consciousness, Free Will and Scientific Objectivity in Perspective-Based Reasoning
by dadadarren — 2021-10-14T18:00:10.681Z
Link: https://www.sleepingbeautyproblem.com/about-consciousness-free-will-and-scientific-objectivity/

I have been avoiding this subject since it is too metaphysical for my taste. My interest was in specific problems such as anthropic paradoxes. However, my solution to them does have clear dispositions on these topics. So I will lay it out here.

Fundamental Perspectives

I argued that our rationality is not able to think about things as they are, by themselves. Instead, we would inevitably take a certain perspective or viewpoint when reasoning. Each of us has a natural perspective due to first-person subjective experiences. “I am this particular person, living in this particular time ” is inherently clear to each. It is the primary fact that has none, nor needs any, logical explanation.

Though we cannot think without a perspective, we are capable of putting ourselves into others’ shoes. In another word, we can imagine thinking from different perspectives. All viewpoints are parallel to each other, none is inherently logically superior.

But people often regard a god’s eye view as something else, something that transcends the limit of perspectives. It’s treated as absolute thinking, a fundamental conception that objective reasoning ought to be conducted from. Why we have this intuition would be discussed later. I think anthropic paradoxes are caused by it: trying to conduct the argument from a god’s eye view yet unavoidably also use “I” or “now” from the first-person perspective.

Consciousness and Free Will

The above has some metaphysical commitments attached. Consciousness, though a rather mysterious concept, is undeniably a first-person experience. E.g. from my perspective I only know that I am conscious, whether others are conscious like I do or just some mindless NPCs can never be verified. It is also instantiated by subjective experience so it is irreducible just like perspectives.

Perspective-based reasoning presupposes free will. For thinking from someone’s perspective to be meaningful at all it is a necessary presumption. It should be noted like consciousness, free will is also a first-person concept. Meaning when thinking from a particular perspective, the only consciousness and free will in reasoning would be that of the self. Someone/something else’s consciousness is only presupposed when reasoning from their perspective (still due to the self).

Scientific Objectivity

Since we are not able to reason about things by themselves as they ar...
Book Analysis: New Thrawn Trilogy
by ozymandias — 2021-10-14T17:49:14.646Z

[Spoiler note: I have used only examples from relatively early on in the new Thrawn Trilogy and ones which I believe won’t spoil your enjoyment of the story; however, if you’d like to go in with absolutely no knowledge, this will tell you some things about the trilogy.]

I’ve recently read the first two books of Timothy Zahn’s new Thrawn trilogy and it’s given me some thoughts about writing competent characters.

The unique thing about Thrawn as a character is that he comes off as genuinely competent. The first book of the new Thrawn trilogy makes this particularly vivid because, instead of having to pair Thrawn with characters with canonical non-competent personalities, Zahn was free to invent the equally competent Eli Vanto, Arindha Pryce, and Nightswan.

In most books, characters who are supposed to be geniuses go one of three ways. First, they’re chessmasters with incredibly tangled Rube Goldberg Machine plans: “of course, I knew all along that overhearing that conversation would cause the protagonist’s wife to conclude she’s cheating on her and divorce her, motivating the protagonist to seek out the Stone of Plot, thus moving my plans one step closer to fruition!” Second, they’re scientific or magical geniuses with fake science or magic: “of course! If you reverse the polarity of the subspace photon launcher it will babble the techno and that will lower the shields, letting us in!” Third, they’re very good improvisers, which is a genuine kind of intelligence, but also one that is kind of easy for writers. The character doesn’t have to have clever plans, they just have to react cleverly to what’s happening—and the audience will cut them some slack if their solution isn’t amazing. After all, they did think of it in a few seconds.

Thrawn takes a fourth approach.

In his first scene, Thrawn is fighting some Imperial troops which are camped in the forest. He figures out that their shield system must let through small forest animals, or they’d be constantly dealing with false alarms. So he tapes bombs to squirrels and blows up the Imperial camp.

Now, I have no idea if this is a legitimately clever military tactic, or if it makes sense that Thrawn is the first person to think of it. I’m not a tactician. Maybe the Imperial troops should already have had a way of dealing with squirrel bombs. But I can understand Thrawn’s plan. There are no hidden steps of “oh, he’s just a...
Is anchoring a reliable cognitive bias?
by justindomke — 2021-10-14T15:15:42.641Z
Link: https://justindomke.wordpress.com/2021/10/14/is-anchoring-a-reliable-cognitive-bias/

Say you get a large group of people and split them into three groups. The first group gets this question:

What is the length of the Mississippi river?

The second group gets these questions:

Is the Mississippi river greater or less than 70 miles long?

What is the length of the Mississippi river?

The third group gets these:

Is the Mississippi river greater or less than 2000 miles long?

What is the length of the Mississippi river?

When Jacowitz and Kahneman tried this in 1995, they found that the groups had median estimates of 800 miles, 300 miles, and 1500 miles, respectively.

In theory at least, the initial greater / less questions provide no information, and so shouldn't change the final estimates people give. But they do change them, a lot.

This effect is known as anchoring. It's widely discussed, but the replication crisis has made me paranoid cautious about trusting anything that hasn't been replicated. So I tried to find anchoring studies with replications.

Anchoring for estimation

A seminal paper from 1995 is Measures of Anchoring in Estimation Tasks by Jacowitz and Kahneman. They created a list of 15 quantities to estimate and took 156 U.C. Berkeley students who were taking an intro to psychology course. They had three questionnaires, one—for calibration—simply asked them to estimate everything. The other two questionnaires first asked if each quantity was more or less than a certain anchor and then asked for an estimate of the quantity.

The results were that anchoring had a massive effect:

Here, the left column shows the median among people who weren't given an anchor, the next two columns show what numbers were used as low/high anchors, and the last two columns show the median responses among people given low/high anchors.

But does it replicate?

The Many Labs project is an effort from a team of psychologists around the world. In their first paper (Klein et al., 2014) they tried to replicate 13 different well-known effects, including anchoring. They had a large sample of 5000 people from around the world and gave them four estimation problems.

There was one small difference from the original study. Rather than being asked if the population of Chicago was greater or less than the anchor, participants were simply informed of the truth.

And what are the results? You have to dig to find the raw data, but I was able to put it together from https://osf.io/tjx3f/ and https:/...