Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is Epistemic Strategies of Selection Theorems, published by Adam Shimi on October 18, 2021 on The AI Alignment Forum.

Introduction: Epistemic Strategies Redux

This post examines the epistemic strategies of John Wentworth’s selection theorem posts.

(If you want to skim this post, just read the Summary subsection that display the different epistemic strategies as design patterns)

I introduced the concept in a recent post, but didn’t define them except as the “ways of producing” knowledge that are used in a piece of research. If we consider a post or paper as a computer program outputting (producing) knowledge about alignment, epistemic strategies are the underlying algorithm or, even more abstractly, the design patterns.

An example of epistemic strategy, common in natural sciences (and beyond), is

Look at the data

Find a good explanation

Predict new things with that explanation

Get new data for checking your prediction

More than just laying out some abstract recipe, analysis serves to understand how each step is done, whether that makes sense, and how each step (and the whole strategy) might fail. Just like a design pattern or an algorithm, it matters tremendously to know when to apply it and when to avoid it as well as subtleties to be aware of.

Laying this underlying structure bare matters in three ways:

It clarifies the research’s purpose and value for newcomers and researchers from other fields, with minimal assumptions of shared approaches.

Just like a programmer switching to a new domain of problems will get up to speed faster and more reliably if they get access to the patterns/algorithms/tricks used in their new domain.

It focuses feedback and criticism on the most important parts of the idea/proposition/argument.

Issues with an algorithm more often focus on the point of it instead of the details,whereas issues with an implementation of that algorithm can be as much about typos, optimization tricks and bad structure than about the actual core (the algorithm.

It builds a library of such strategies for alignment in particular, a cookbook newcomers and senior researchers alike can browse for inspiration or a take on some new post/paper they don’t grok.

Like the glorious Game Programming Patterns who does exactly that for game programming

Now, before starting, I need to point out that the selection theorems posts don’t present research results; they present epistemic strategies (the eponymous theorems). Does that mean my job has already been done? Not exactly: John’s posts do present that epistemic strategy, but not in all the ways I want to stress out. John is also trying to fill in a lot of concrete details and to convince people that selection theorems are a nice thing to research, which I don’t have to do. Instead, you can see this post as distilling the structure of selection theorems and interrogating them further as ways of producing knowledge.

(I use the word “agent” to stay coherent with John, but nothing in the epistemic strategy itself requires agency, and so finding the idea of agents confusing shouldn’t be an issue for reading this post)

Thanks to John Wentworth for feedback on a draft of this post.

Characterizing Selection Theorems

Selection theorems are theorems. Obviously. But what sort of theorems? What are they trying to find about the world?

John summarizes the whole class of results in the following way:

Roughly speaking, a Selection Theorem tells us something about what agent type signatures will be selected for (by e.g. natural selection or ML training or economic profitability) in some broad class of environments.

This gives us three components of a selection theorem: the selection pressure, the class of environments considered and the constraint on the agent (what John calls the “type signatures”). Let’s get into each, looking for what can fill ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is Long Covid Informal Study Results, published by Elizabeth on October 16, 2021 on LessWrong.

Introduction

Yesterday I talked about a potential treatment for Long Covid, and referenced an informal study I’d analyzed that tried to test it, which had seemed promising but was ultimately a let down. That analysis was too long for its own post, so it’s going here instead.

Gez Medinger ran an excellent-for-its-type study of interventions for long covid, with a focus on niacin, the center of the stack I took. I want to emphasize both how very good for its type this study was, and how limited the type is. Surveys of people in support groups who chose their own interventions is not a great way to determine anything. But really rigorous information will take a long time and some of us have to make decisions now, so I thought this was worth looking into.

Medinger does a great analysis in this youtube video. He very proactively owns all the limitations of the study (all of which should be predictable to regular readers of mine) and does what he can to make up for them in the analysis, while owning where that’s not possible. But he delivers the analysis in a video rather than a text post ugh why would you do that (answer: he was a professional filmmaker before he got long covid). I found this deeply hard to follow, so I wanted to play with the data directly. Medinger generously shared the data, at which point this snowballed into a full-blown analysis.

I think Medinger attributes his statistics to a medical doctor, but I couldn’t find it on relisten and I’m not watching that damn video again. My statistical analysis was done by my dad/Ph.D. statistician R. Craig Van Nostrand. His primary work is in industrial statistics but the math all transfers, and the biology-related judgment calls were made by me (for those of you just tuning in, I have a BA in biology and no other relevant credentials or accreditations).

The Study

As best I can determine, Medinger sent a survey to a variety of long covid support groups, asking what interventions people had tried in the last month, when they’d tried them, and how they felt relative to a month ago. Obviously this has a lot of limitations – it will exclude people who got better or worse enough they didn’t engage with support groups, it was in no way blinded, people chose their own interventions, it relied entirely on self-assement, etc.

Differences in Analysis

You can see Medinger’s analysis here. He compared the rate of improvement and decline among groups based on treatments. I instead transformed the improvement bucket to a number and did a multivariate analysis.

Much better (near or at pre-covid)1Significantly better0.5A little better0.1No change0A little worse-0.2Significantly worseCuriously unusedMuch worse-1.2

You may notice that the numerical values of the statements are not symmetric- being “a little worse” is twice as bad as “a little better” is good. This was deliberate, based on my belief that people with chronic illness on average overestimate their improvement over short periods of time. We initially planned on doing a sensitivity analysis to see how this changed the results; in practice the treatment groups had very few people who got worse so this would only affect the no-treatment control, and it was obvious that fiddling with the numbers would not change the overall conclusion.

Also, no one checked “significantly worse”, and when asked Medinger couldn’t remember if it was an option at all. This suggests to me that “Much worse” should have a less bad value and “a little worse” a more bad value. However, we judged this wouldn’t affect the outcome enough to be worth the effort, and ignored it.

We tossed all the data where people had made less than two weeks ago (this was slightly more than half of it), except for the no-change cont...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is Long Covid Informal Study Results, published by Elizabeth on October 16, 2021 on LessWrong.

Introduction

Yesterday I talked about a potential treatment for Long Covid, and referenced an informal study I’d analyzed that tried to test it, which had seemed promising but was ultimately a let down. That analysis was too long for its own post, so it’s going here instead.

Gez Medinger ran an excellent-for-its-type study of interventions for long covid, with a focus on niacin, the center of the stack I took. I want to emphasize both how very good for its type this study was, and how limited the type is. Surveys of people in support groups who chose their own interventions is not a great way to determine anything. But really rigorous information will take a long time and some of us have to make decisions now, so I thought this was worth looking into.

Medinger does a great analysis in this youtube video. He very proactively owns all the limitations of the study (all of which should be predictable to regular readers of mine) and does what he can to make up for them in the analysis, while owning where that’s not possible. But he delivers the analysis in a video rather than a text post ugh why would you do that (answer: he was a professional filmmaker before he got long covid). I found this deeply hard to follow, so I wanted to play with the data directly. Medinger generously shared the data, at which point this snowballed into a full-blown analysis.

I think Medinger attributes his statistics to a medical doctor, but I couldn’t find it on relisten and I’m not watching that damn video again. My statistical analysis was done by my dad/Ph.D. statistician R. Craig Van Nostrand. His primary work is in industrial statistics but the math all transfers, and the biology-related judgment calls were made by me (for those of you just tuning in, I have a BA in biology and no other relevant credentials or accreditations).

The Study

As best I can determine, Medinger sent a survey to a variety of long covid support groups, asking what interventions people had tried in the last month, when they’d tried them, and how they felt relative to a month ago. Obviously this has a lot of limitations – it will exclude people who got better or worse enough they didn’t engage with support groups, it was in no way blinded, people chose their own interventions, it relied entirely on self-assement, etc.

Differences in Analysis

You can see Medinger’s analysis here. He compared the rate of improvement and decline among groups based on treatments. I instead transformed the improvement bucket to a number and did a multivariate analysis.

Much better (near or at pre-covid)1Significantly better0.5A little better0.1No change0A little worse-0.2Significantly worseCuriously unusedMuch worse-1.2

You may notice that the numerical values of the statements are not symmetric- being “a little worse” is twice as bad as “a little better” is good. This was deliberate, based on my belief that people with chronic illness on average overestimate their improvement over short periods of time. We initially planned on doing a sensitivity analysis to see how this changed the results; in practice the treatment groups had very few people who got worse so this would only affect the no-treatment control, and it was obvious that fiddling with the numbers would not change the overall conclusion.

Also, no one checked “significantly worse”, and when asked Medinger couldn’t remember if it was an option at all. This suggests to me that “Much worse” should have a less bad value and “a little worse” a more bad value. However, we judged this wouldn’t affect the outcome enough to be worth the effort, and ignored it.

We tossed all the data where people had made less than two weeks ago (this was slightly more than half of it), except for the no-change cont...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is How much should you update on a COVID test result?, published by mayleaf on October 17, 2021 on LessWrong.

This is a writeup of COVID test accuracies that I put together for my own interest, and shared with friends and housemates to help us reason about COVID risk. Some of these friends suggested that I post this to LessWrong. I am not a statistician or an expert in medical research.

Background

We often hear that some kinds of COVID tests are more accurate than others — PCR tests are more accurate than rapid antigen tests, and rapid antigen tests are more accurate if you have symptoms than if you don't. A test's accuracy is often presented as two separate terms: sensitivity (what proportion of diseased patients the test accurately identifies as diseased) and specificity (what proportion of healthy people the test accurately identifies as healthy). But it's not obvious how to practically interpret those numbers: if you test negative, what does that mean about your odds of having COVID?

This writeup attempts to answer to the question, "how much more (or less) likely am I to have COVID given a positive (or negative) test result?" In particular, this is an attempt to calculate the Bayes factor for different types of COVID test results.

The Bayes factor is a number that tells you how much to update your prior odds of an event (in this case, your initial guess at how likely someone is to have COVID) given some piece of new evidence (in this case, a test result). It's calculated based on the test's sensitivity and specificity. If a test has a Bayes factor of 10x for a positive test result, and you test positive, then you should multiply your initial estimated odds of having COVID by 10x. If the same test has a Bayes factor of 0.3x for a negative test result, and you test negative, then you should update your prior odds of having COVID by 0.3x.

(For an excellent explanation of Bayes factors and the motivation behind using them to interpret medical tests, I highly recommend this 3Blue1Brown video, which inspired this post.)

Using Bayes factors

There's a well-known anecdote where doctors in a statistics seminar were asked how they would interpret a positive cancer test result. They were given the information that the has a sensitivity of 90% (10% false negative rate), a specificity of 91% (9% false positive rate), and that the base rate of cancer for the patient's age and sex is 1%. Famously, nearly half of doctors incorrectly answered that the patient had a 90% probability of having cancer. [1] The actual probability is only 9%, since the base rate of cancer is low in the patient's population. One important lesson from this anecdote is that test results are an update on your priors of having the disease; the same positive test result implies different probabilities of disease depending on the disease's base rate.

Bayes factors help make it easy to make this update. A test's Bayes factor is a single number that, when multiplied by your prior odds, gives you your posterior odds. For a COVID test, you can start with your initial estimate of how likely you are to have COVID (based on the prevalence in your area, or your current number of microCOVIDs) and update from there.

To calculate the Bayes factor for a negative COVID test, you take the probability that you'd test negative in the world where the you do have COVID and divide it by the probability that you'd test negative in the world where the you do not have COVID. Expressed mathematically:

Similarly, the Bayes factor for a positive COVID test is the probability of a positive result in the world where the you do have COVID, divided by the probability of a positive result in the world where the you do not have COVID.

To interpret the test result, express your prior probability of having COVID as an odds, and then multiply thos...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is How much should you update on a COVID test result?, published by mayleaf on October 17, 2021 on LessWrong.

This is a writeup of COVID test accuracies that I put together for my own interest, and shared with friends and housemates to help us reason about COVID risk. Some of these friends suggested that I post this to LessWrong. I am not a statistician or an expert in medical research.

Background

We often hear that some kinds of COVID tests are more accurate than others — PCR tests are more accurate than rapid antigen tests, and rapid antigen tests are more accurate if you have symptoms than if you don't. A test's accuracy is often presented as two separate terms: sensitivity (what proportion of diseased patients the test accurately identifies as diseased) and specificity (what proportion of healthy people the test accurately identifies as healthy). But it's not obvious how to practically interpret those numbers: if you test negative, what does that mean about your odds of having COVID?

This writeup attempts to answer to the question, "how much more (or less) likely am I to have COVID given a positive (or negative) test result?" In particular, this is an attempt to calculate the Bayes factor for different types of COVID test results.

The Bayes factor is a number that tells you how much to update your prior odds of an event (in this case, your initial guess at how likely someone is to have COVID) given some piece of new evidence (in this case, a test result). It's calculated based on the test's sensitivity and specificity. If a test has a Bayes factor of 10x for a positive test result, and you test positive, then you should multiply your initial estimated odds of having COVID by 10x. If the same test has a Bayes factor of 0.3x for a negative test result, and you test negative, then you should update your prior odds of having COVID by 0.3x.

(For an excellent explanation of Bayes factors and the motivation behind using them to interpret medical tests, I highly recommend this 3Blue1Brown video, which inspired this post.)

Using Bayes factors

There's a well-known anecdote where doctors in a statistics seminar were asked how they would interpret a positive cancer test result. They were given the information that the has a sensitivity of 90% (10% false negative rate), a specificity of 91% (9% false positive rate), and that the base rate of cancer for the patient's age and sex is 1%. Famously, nearly half of doctors incorrectly answered that the patient had a 90% probability of having cancer. [1] The actual probability is only 9%, since the base rate of cancer is low in the patient's population. One important lesson from this anecdote is that test results are an update on your priors of having the disease; the same positive test result implies different probabilities of disease depending on the disease's base rate.

Bayes factors help make it easy to make this update. A test's Bayes factor is a single number that, when multiplied by your prior odds, gives you your posterior odds. For a COVID test, you can start with your initial estimate of how likely you are to have COVID (based on the prevalence in your area, or your current number of microCOVIDs) and update from there.

To calculate the Bayes factor for a negative COVID test, you take the probability that you'd test negative in the world where the you do have COVID and divide it by the probability that you'd test negative in the world where the you do not have COVID. Expressed mathematically:

Similarly, the Bayes factor for a positive COVID test is the probability of a positive result in the world where the you do have COVID, divided by the probability of a positive result in the world where the you do not have COVID.

To interpret the test result, express your prior probability of having COVID as an odds, and then multiply thos...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is Suffering-Focused Ethics (SFE) FAQ, published by EdisonY on October 16, 2021 on The Effective Altruism Forum.

This FAQ is meant to introduce suffering-focused ethics to an EA-aligned audience. Section 2 is the most important part of the FAQ, and will help the reader get a good grasp of the conceptual landscape of SFE. The FAQ is designed to allow the reader to skip around and head directly to questions that they are personally interested in, so please don’t feel the need to read the entire FAQ sequentially.

3.1 Why should I believe SFE?

4.11 We do interpersonal trade-offs in policy making all the time. Doesn’t interpersonal SFE rule out such trades?

1. Common Misconceptions

1.1 Is SFE the same as negative utilitarianism (NU)?

No, SFE is a broader category of ethical views that includes NU, see 2.1 for details.

1.2 Does SFE assume that there is more suffering than happiness in most people’s lives?

No, SFE’s core claim is that reducing suffering is more morally important than increasing happiness. This normative claim does not hinge on the empirical quantity of suffering and happiness in most people’s lives.

1.3 Are people who hold suffering-focused views much more concerned about mild suffering than, say, the average ethically concerned person?

This varies between versions of SFE. Most versions of SFE do not attach extreme moral importance to mild suffering. Threshold lexical SFE (see 2.8), for example, is compatible with the claim that mild suffering is morally symmetrical to mild happiness.

1.4 Is SFE motivated by pessimism?

No, there is no clear connection between SFE and pessimism. On the contrary, one can argue that SFE is much less pessimistic than standard versions of consequentialism. This is because, if SFE is true, then most moral progress will be found in the elimination of suffering, a goal that is much more achievable than maximizing the amount of happiness in the universe (which might be close to infinite).

1.5 Isn’t SFE a fringe moral theory?

People often hold that SFE is a fringe moral theory because they conflate SFE with negative utilitarianism (see 1.1). However, many more moderate views in the SFE family are arguably very widely held. See what we call the common sense SFE view here. It is also notable that even negative utilitarianism has had more supporters than it’s often given credit for. For more on philosophers and philosophical traditions that have supported SFE, see here.

1.6 Is prioritarianism a version of SFE?

No. Suppose no one in the world suffers, but some people are happier than others. Prioritarianism implies that we ought to prioritize those who are less happy, while SFE does not entail this implication. See 4.10 for more on the relationship between prioritarianism and SFE.

2. What views fall under Suffering Focused Ethics?

2.1 What is Suffering-focused Ethics (SFE)?

SFE is an umbrella term for ethical views that attach primary or special moral importance to the prevention of suffering. Although a large variety of possible ethical views fall under the category of SFE, they all share at least one common claim, which we may call the happiness/suffering asymmetry thesis:

The happiness/suffering asymmetry thesis: Suffering has higher ethical priority than happiness.

Different SFE theories will have different views on the extent and nature of the asymmetry (for details, please see 2.6-2.11 for this FAQ).

2.2 What is suffering?

Like happiness, suffering is ambiguous between two senses. Understood in the descriptive sense, suffering refers to mental states that are typically associated with pain, the feeling of dissastisfaction, etc. Since this sense of suffering is purely descriptive, there is a further value-oriented question as to whether suffering is morally bad. Alternatively, we can understand suffering in the normative sense. In this ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is Suffering-Focused Ethics (SFE) FAQ, published by EdisonY on October 16, 2021 on The Effective Altruism Forum.

This FAQ is meant to introduce suffering-focused ethics to an EA-aligned audience. Section 2 is the most important part of the FAQ, and will help the reader get a good grasp of the conceptual landscape of SFE. The FAQ is designed to allow the reader to skip around and head directly to questions that they are personally interested in, so please don’t feel the need to read the entire FAQ sequentially.

3.1 Why should I believe SFE?

4.11 We do interpersonal trade-offs in policy making all the time. Doesn’t interpersonal SFE rule out such trades?

1. Common Misconceptions

1.1 Is SFE the same as negative utilitarianism (NU)?

No, SFE is a broader category of ethical views that includes NU, see 2.1 for details.

1.2 Does SFE assume that there is more suffering than happiness in most people’s lives?

No, SFE’s core claim is that reducing suffering is more morally important than increasing happiness. This normative claim does not hinge on the empirical quantity of suffering and happiness in most people’s lives.

1.3 Are people who hold suffering-focused views much more concerned about mild suffering than, say, the average ethically concerned person?

This varies between versions of SFE. Most versions of SFE do not attach extreme moral importance to mild suffering. Threshold lexical SFE (see 2.8), for example, is compatible with the claim that mild suffering is morally symmetrical to mild happiness.

1.4 Is SFE motivated by pessimism?

No, there is no clear connection between SFE and pessimism. On the contrary, one can argue that SFE is much less pessimistic than standard versions of consequentialism. This is because, if SFE is true, then most moral progress will be found in the elimination of suffering, a goal that is much more achievable than maximizing the amount of happiness in the universe (which might be close to infinite).

1.5 Isn’t SFE a fringe moral theory?

People often hold that SFE is a fringe moral theory because they conflate SFE with negative utilitarianism (see 1.1). However, many more moderate views in the SFE family are arguably very widely held. See what we call the common sense SFE view here. It is also notable that even negative utilitarianism has had more supporters than it’s often given credit for. For more on philosophers and philosophical traditions that have supported SFE, see here.

1.6 Is prioritarianism a version of SFE?

No. Suppose no one in the world suffers, but some people are happier than others. Prioritarianism implies that we ought to prioritize those who are less happy, while SFE does not entail this implication. See 4.10 for more on the relationship between prioritarianism and SFE.

2. What views fall under Suffering Focused Ethics?

2.1 What is Suffering-focused Ethics (SFE)?

SFE is an umbrella term for ethical views that attach primary or special moral importance to the prevention of suffering. Although a large variety of possible ethical views fall under the category of SFE, they all share at least one common claim, which we may call the happiness/suffering asymmetry thesis:

The happiness/suffering asymmetry thesis: Suffering has higher ethical priority than happiness.

Different SFE theories will have different views on the extent and nature of the asymmetry (for details, please see 2.6-2.11 for this FAQ).

2.2 What is suffering?

Like happiness, suffering is ambiguous between two senses. Understood in the descriptive sense, suffering refers to mental states that are typically associated with pain, the feeling of dissastisfaction, etc. Since this sense of suffering is purely descriptive, there is a further value-oriented question as to whether suffering is morally bad. Alternatively, we can understand suffering in the normative sense. In this ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. Published on October 17, 2021.

This is an essay about one of those "once you see it, you will see it everywhere" phenomena. It is a psychological and interpersonal dynamic roughly as common, and almost as destructive, as motte-and-bailey, and at least in my own personal experience it's been quite valuable to have it reified, so that I can quickly recognize the commonality between what I had previously thought of as completely unrelated situations.

The original quote referenced in the title is "There are three kinds of lies: lies, damned lies, and statistics."

Background 1: Gyroscopes

Gyroscopes are weird.

Except they're not. They're quite normal and mundane and straightforward. The weirdness of gyroscopes is a map-territory confusion—gyroscopes seem weird because my map is poorly made, and predicts that they will do something other than their normal, mundane, straightforward thing.

In large part, this is because I don't have the consequences of physical law engraved deeply enough into my soul that they make intuitive sense.

I can imagine a world that looks exactly like the world around me, in every way, except that in this imagined world, gyroscopes don't have any of their strange black-magic properties. It feels coherent to me. It feels like a world that could possibly exist.

"Everything's the same, except gyroscopes do nothing special." Sure, why not.

But in fact, this world is deeply, deeply incoherent. It is Not Possible with capital letters. And a physicist with sufficiently sharp intuitions would know this—would be able to see the implications of a world where gyroscopes "don't do anything weird," and tell me all of the ways in which reality falls apart.

The seeming coherence of the imaginary world where gyroscopes don't balance and don't process and don't resist certain kinds of motion is a product of my own ignorance, and of the looseness with which I am tracking how different facts fit together, and what the consequences of those facts are. It's like a toddler thinking that they can eat their slice of cake, and still have that very same slice of cake available to eat again the next morning.

Background 2: H2O and XYZ

In the book Labyrinths of Reason, author William Poundstone delves into various thought experiments (like Searle's Chinese Room) to see whether they're actually coherent or not.

In one such exploration, he discusses the idea of a Twin Earth, on the opposite side of the sun, exactly like Earth in every way except that it doesn't have water. Instead, it has a chemical, labeled XYZ, which behaves like water and occupies water's place in biology and chemistry, but is unambiguously distinct.

Once again, this is the sort of thing humans are capable of imagining. I can nod along and say "sure, a liquid that behaves just like water, but isn't."

But a chemist, intimately familiar with the structure and behavior of molecules and with the properties of the elements and their isotopes, would be throwing up red flags.

"Just like water," they might say, and I would nod.

"Liquid, and transparent, with a density of 997 kilograms per meter cubed."

"Sure," I would reply.

"Which freezes and melts at exactly 0º Celsius, and which boils and condenses at exactly 100º Celsius."

"Yyyyeahhhh," I would say, uneasiness settling in.

"Which makes up roughly 70% of the mass of the bodies of the humans of Twin Earth, and which is a solvent for hydrophilic substances, but not hydrophobic ones, and which can hold ions and polar substances in solution."

"Um."

The more we drill down into what we mean by behaves exactly like water, the more it starts to become clear that there just isn't a possible substance which behaves exactly like water, but isn't. There are only so many configurations of electrons and protons and neutrons (especially while remaining small enough to mimic water's molari...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. Published on October 17, 2021.

This is an essay about one of those "once you see it, you will see it everywhere" phenomena. It is a psychological and interpersonal dynamic roughly as common, and almost as destructive, as motte-and-bailey, and at least in my own personal experience it's been quite valuable to have it reified, so that I can quickly recognize the commonality between what I had previously thought of as completely unrelated situations.

The original quote referenced in the title is "There are three kinds of lies: lies, damned lies, and statistics."

Background 1: Gyroscopes

Gyroscopes are weird.

Except they're not. They're quite normal and mundane and straightforward. The weirdness of gyroscopes is a map-territory confusion—gyroscopes seem weird because my map is poorly made, and predicts that they will do something other than their normal, mundane, straightforward thing.

In large part, this is because I don't have the consequences of physical law engraved deeply enough into my soul that they make intuitive sense.

I can imagine a world that looks exactly like the world around me, in every way, except that in this imagined world, gyroscopes don't have any of their strange black-magic properties. It feels coherent to me. It feels like a world that could possibly exist.

"Everything's the same, except gyroscopes do nothing special." Sure, why not.

But in fact, this world is deeply, deeply incoherent. It is Not Possible with capital letters. And a physicist with sufficiently sharp intuitions would know this—would be able to see the implications of a world where gyroscopes "don't do anything weird," and tell me all of the ways in which reality falls apart.

The seeming coherence of the imaginary world where gyroscopes don't balance and don't process and don't resist certain kinds of motion is a product of my own ignorance, and of the looseness with which I am tracking how different facts fit together, and what the consequences of those facts are. It's like a toddler thinking that they can eat their slice of cake, and still have that very same slice of cake available to eat again the next morning.

Background 2: H2O and XYZ

In the book Labyrinths of Reason, author William Poundstone delves into various thought experiments (like Searle's Chinese Room) to see whether they're actually coherent or not.

In one such exploration, he discusses the idea of a Twin Earth, on the opposite side of the sun, exactly like Earth in every way except that it doesn't have water. Instead, it has a chemical, labeled XYZ, which behaves like water and occupies water's place in biology and chemistry, but is unambiguously distinct.

Once again, this is the sort of thing humans are capable of imagining. I can nod along and say "sure, a liquid that behaves just like water, but isn't."

But a chemist, intimately familiar with the structure and behavior of molecules and with the properties of the elements and their isotopes, would be throwing up red flags.

"Just like water," they might say, and I would nod.

"Liquid, and transparent, with a density of 997 kilograms per meter cubed."

"Sure," I would reply.

"Which freezes and melts at exactly 0º Celsius, and which boils and condenses at exactly 100º Celsius."

"Yyyyeahhhh," I would say, uneasiness settling in.

"Which makes up roughly 70% of the mass of the bodies of the humans of Twin Earth, and which is a solvent for hydrophilic substances, but not hydrophobic ones, and which can hold ions and polar substances in solution."

"Um."

The more we drill down into what we mean by behaves exactly like water, the more it starts to become clear that there just isn't a possible substance which behaves exactly like water, but isn't. There are only so many configurations of electrons and protons and neutrons (especially while remaining small enough to mimic water's molari...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. Published on October 17, 2021.

A friend recently shared a sharing of a screenshot of a reblogging of a reblogging of this tumblr post:

Pro-Capitalist's defense of capitalism is just explaining how it works, and then when you say "yes I know, I just think it shouldn't be like that" they explain it to you again but angrier this time. —strawberry-crocodile.

I really like this perspective, even as someone relatively pro-capitalism, because I think it captures something that often goes wrong in these discussions.

The strongest argument in favor of capitalism is that in practice it works for most things, better than the other systems we've tried. Not because it was designed to work, but because that's just how it falls together. When someone points at a piece of the system that seems unfair or wasteful and says "I just think it shouldn't be like that," stopping it's going to have effects elsewhere in the system, often negative ones. And so pro-capitalism folks often respond by trying to explain capitalism harder: what role is the thing you want to change filling? When people propose removing something without engaging with how it ties in to the rest of the system, it is natural to assume they don't know about its function and try to explain.

As in the opening quote, however, people don't want more explanation of the workings of the status quo. Instead, I think a better response is to think about what you expect would go wrong, and ask if they would expect that. Perhaps they don't, and you can try and figure out where specifically your expectations diverge. Perhaps they do, and they think it's worth it. Perhaps they have additional proposals which work together. Whichever way the conversation goes, I think it probably is more productive?

(Overall my perspective is that while things are much worse than they could be, they're also much better than they have ever been. I really don't want us to break the system that keeps improving our ability to turn time and stuff into what people need. At the same time, to the extent that we can do it without breaking this cycle of improvement, I'd like to see far more redistribution of wealth. In my own life this looks like giving.)

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