Epistemic Legibility

Tl;dr: being easy to argue with is a virtue, separate from being correct.

Introduction

Regular readers of my blog know of my epistemic spot check series, where I take claims (evidential or logical) from a work of nonfiction and check to see if they’re well supported. It’s not a total check of correctness: the goal is to rule out things that are obviously wrong/badly formed before investing much time in a work, and to build up my familiarity with its subject. 

Before I did epistemic spot checks, I defined an easy-to-read book as, roughly, imparting an understanding of its claims with as little work from me as possible. After epistemic spot checks, I started defining easy to read as “easy to epistemic spot check”. It should be as easy as possible (but no easier) to identify what claims are load-bearing to a work’s conclusions, and figure out how to check them. This is separate from correctness: things can be extremely legibly wrong. The difference is that when something is legibly wrong someone can tell you why, often quite simply. Illegible things just sit there at an unknown level of correctness, giving the audience no way to engage.

There will be more detailed examples later, but real quick: “The English GDP in 1700 was $890324890. I base this on $TECHNIQUE interpretation of tax records, as recorded in $REFERENCE” is very legible (although probably wrong, since I generated the number by banging on my keyboard). “Historically, England was rich” is not. “Historically, England was richer than France” is somewhere in-between. 

“It was easy to apply this blog post format I made up to this book” is not a good name, so I’ve taken to calling the collection of traits that make things easy to check “epistemic legibility”, in the James C. Scott sense of the word legible. Legible works are (comparatively) easy to understand, they require less external context, their explanations scale instead of needing to be tailored for each person. They’re easier to productively disagree with, easier to partially agree with instead of forcing a yes or no, and overall easier to integrate into your own models.

[Like everything in life, epistemic legibility is a spectrum, but I’ll talk about it mostly as a binary for readability’s sake]

When people talk about “legible” in the Scott sense they often mean it as a criticism, because pushing processes to be more legible cuts out illegible sources of value. One of the reasons I chose the term here is that I want to be very clear about the costs of legibility and the harms of demanding it in excess. But I also think epistemic legibility leads people to learn more correct things faster and is typically underprovided in discussion.

If I hear an epistemically legible argument, I have a lot of options. I can point out places I think the author missed data that impacts their conclusion, or made an illogical leap. I can notice when I know of evidence supporting their conclusions that they didn’t mention. I can see implications of their conclusions that they didn’t spell out. I can synthesize with other things I know, that the author didn’t include.

If I hear an illegible argument, I have very few options. Perhaps the best case scenario is that it unlocks something I already knew subconsciously but was unable to articulate, or needed permission to admit. This is a huge service! But if I disagree with the argument, or even just find it suspicious, my options are kind of crap. I write a response of equally low legibility, which is unlikely to improve understanding for anyone. Or I could write up a legible case for why I disagree, but that is much more work than responding to a legible original, and often more work than went into the argument I’m responding to, because it’s not obvious what I’m arguing against.  I need to argue against many more things to be considered comprehensive. If you believe Y because of X, I can debate X. If you believe Y because …:shrug:… I have to imagine every possible reason you could do so, counter all of them, and then still leave myself open to something I didn’t think of. Which is exhausting.

I could also ask questions, but the more legible an argument is, the easier it is to know what questions matter and the most productive way to ask them. 

I could walk away, and I am in fact much more likely to do that with an illegible argument. But that ends up creating a tax on legibility because it makes one easier to argue with, which is the opposite of what I want.

Not everything should be infinitely legible. But I do think more legibility would be good on most margins, that choices of the level of legibility should be made more deliberately, and that we should treat highly legible and illegible works more differently than we currently do. I’d also like a common understanding of legibility so that we can talk about its pluses and minuses, in general or for a particular piece.

This is pretty abstract and the details matter a lot, so I’d like to give some better examples of what I’m gesturing at. In order to reinforce the point that legibility and correctness are orthogonal; this will be a four quadrant model. 

True and Legible

Picking examples for this category was hard. No work is perfectly true and perfectly legible, in the sense of being absolutely impossible to draw an inaccurate conclusion from and having no possible improvements to legibility, because reality is very complicated and communication has space constraints. Every example I considered, I could see a reason someone might object to it. And the things that are great at legibility are often boring. But it needs an example so…

Acoup

Bret Devereaux over at Acoup consistently writes very interesting history essays that I found both easy to check and mostly true (although with some room for interpretation, and not everyone agrees). Additionally, a friend of mine who is into textiles tells me his textile posts were extremely accurate. So Devereaux does quite well on truth and legibility, despite bringing a fair amount of emotion and strong opinions to his work. 

As an example, here is a paragraph from a post arguing against descriptions of Sparta as a highly equal society.

But the final word on if we should consider the helots fully non-free is in their sanctity of person: they had none, at all, whatsoever. Every year, in autumn by ritual, the five Spartan magistrates known as the ephors (next week) declared war between Sparta and the helots – Sparta essentially declares war on part of itself – so that any spartiate might kill any helot without legal or religious repercussions (Plut. Lyc. 28.4; note also Hdt. 4.146.2). Isocrates – admittedly a decidedly anti-Spartan voice – notes that it was a religious, if not legal, infraction to kill slaves everywhere in Greece except Sparta (Isoc. 12.181). As a matter of Athenian law, killing a slave was still murder (the same is true in Roman law). One assumes these rules were often ignored by slave-holders of course – we know that many such laws in the American South were routinely flouted. Slavery is, after all, a brutal and inhuman institution by its very nature. The absence of any taboo – legal or religious – against the killing of helots marks the institution as uncommonly brutal not merely by Greek standards, but by world-historical standards.

Here we have some facts on the ground (Spartiates could kill their slaves, killing slaves was murder in most contemporaneous societies), sources for some but not all of them (those parentheticals are highly readable if you’re a classicist, and workable if you’re not), the inference he drew from them (Spartans treated their slaves unusually badly), and the conclusions he drew from that (Sparta was not only inequitable, it was unusually inequitable even for its time and place).

Notably, the entire post relies heavily on the belief that slavery is bad, which Devereaux does not bother to justify. That’s a good choice because it would be a complete waste of time for modern audiences – but it also makes this post completely unsuitable for arguing with anyone who disagreed. If for some reason you needed to debate the ethics of slavery, you need work that makes a legible case for that claim in particular, not work that takes it as an axiom.

Exercise for Mood and Anxiety

A few years ago I ESCed Exercise for Mood and Anxiety, a book that aims to educate people on how exercise can help their mental health and then give them the tools to do so. It did really well at the former: the logic was compelling and the foundational evidence was well cited and mostly true (although exercise science always has wide error bars). But out of 14 people who agreed to read the book and attempt to exercise more, only three reported back to me and none of them reported an increase in exercise. So EfMaA is true and epistemically legible, but nonetheless not very useful. 

True but Epistemically Illegible

You Have About Five Words is a poetic essay from Ray Arnold. The final ~paragraph is as follows:

If you want to coordinate thousands of people…

You have about five words.

This has ramifications on how complicated a coordinated effort you can attempt.

What if you need all that nuance and to coordinate thousands of people? What would it look like if the world was filled with complicated problems that required lots of people to solve?

I guess it’d look like this one.

I think the steelman of its core claim, that humans are bad at remembering long nuanced writing and the more people you are communicating with, the more you need to simplify your writing, is obviously true. This is good, because Ray isn’t doing crap to convince me of it. He cites no evidence and gives no explanation of his logic. If I thought nuance increased with the number of readers I would have nothing to say other than “no you’re wrong” or write my own post from scratch, because he gives no hooks to refute. If someone tried to argue that you get ten words rather than five, I would think they were missing the point. If I thought he had the direction right but got the magnitude of the effect wrong enough that it mattered (and he was a stranger rather than a friend), I would not know where to start the discussion.

[Ray gets a few cooperation points back by explicitly labeling this as poetry, which normally I would be extremely happy about, but it weakened its usefulness as an example for this post so right this second I’m annoyed about it.]

False but Epistemically Legible

Mindset

I think Carol Dweck’s Mindset and associated work is very wrong, and I can produce large volumes on specific points of disagreement. This is a sign of a work that is very epistemically legible: I know what her cruxes are, so I can say where I disagree. For all the shit I’ve talked about Carol Dweck over the years, I appreciate that she made it so extraordinarily easy to do so, because she was so clear on where her beliefs came from. 

For example, here’s a quote from Mindset

All children were told that they had performed well on this problem set: “Wow, you did very well on these problems. You got [number of problems] right. That’s a really high score!” No matter what their actual score, all children were told that they had solved at least 80% of the problems that they answered.

Some children were praised for their ability after the initial positive feedback: “You must be smart at these problems.” Some children were praised for their effort after the initial positive feedback: “You must have worked hard at these problems.” The remaining children were in the control condition and received no additional feedback.

And here’s Scott Alexander’s criticism

This is a nothing intervention, the tiniest ghost of an intervention. The experiment had previously involved all sorts of complicated directions and tasks, I get the impression they were in the lab for at least a half hour, and the experimental intervention is changing three short words in the middle of a sentence.

And what happened? The children in the intelligence praise condition were much more likely to say at the end of the experiment that they thought intelligence was more important than effort (p < 0.001) than the children in the effort condition. When given the choice, 67% of the effort-condition children chose to set challenging learning-oriented goals, compared to only 8% (!) of the intelligence-condition. After a further trial in which the children were rigged to fail, children in the effort condition were much more likely to attribute their failure to not trying hard enough, and those in the intelligence condition to not being smart enough (p < 0.001). Children in the intelligence condition were much less likely to persevere on a difficult task than children in the effort condition (3.2 vs. 4.5 minutes, p < 0.001), enjoyed the activity less (p < 0.001) and did worse on future non-impossible problem sets (p…you get the picture). This was repeated in a bunch of subsequent studies by the same team among white students, black students, Hispanic students…you probably still get the picture.

Scott could make those criticisms because Dweck described her experiment in detail. If she’d said “we encouraged some kids and discouraged others”, there would be a lot more ambiguity.

Meanwhile, I want to criticize her for lying to children. Messing up children’s feedback system creates the dependencies on adult authorities that lead to problems later in life. This is extremely bad even if it produces short-term improvements (which it doesn’t). But I can only do this with confidence because she specified the intervention.

The Fate of Rome

This one is more overconfident than false. The Fate of Rome laid out very clearly how they were using new tools for recovering meteorological data to determine the weather 2000 years ago, and using that to analyze the Roman empire. Using this new data, it concludes that the peak of Rome was at least partially caused by a prolonged period of unusually good farming weather in the Mediterranean, and that the collapse started or was worsened when the weather began to regress to the mean.

I looked into the archeometeorology techniques and determined that they, in my judgement, had wider confidence intervals than the book indicated, which undercut the causality claims. I wish the book had been more cautious with its evidence, but I really appreciate that they laid out their reasoning so clearly, which made it really easy to look up points I might disagree with them on.

False and Epistemically Illegible

Public Health and Airborne Pathogen Transmission

I don’t know exactly what the CDC’s or WHO’s current stance is on breathing-based transmission of covid, and I don’t care, because they were so wrong for so long in such illegible ways. 

When covid started, the CDC and WHO’s story was that it couldn’t be “airborne”, because the viral particle was > 5 microns.  That phrasing was already anti-legible for material aimed at the general public, because airborne has a noticeably different definition in virology (”can persist in the air indefinitely”) than it does for popular use (”I can catch this through breathing”). But worse than that, they never provided any justification for the claim. This was reasonable for posters, but not everything was so space constrained, and when I looked in February 2021 I could not figure out where the belief that airborne transmission was rare was coming from. Some researcher eventually spent dozens to hundreds of hours on this and determined the 5 micron number probably came from studies of tuberculosis, which for various reasons needs to get deeper in the lungs than most pathogens and thus has stronger size constraints. If the CDC had pointed to their sources from the start we could have determined the 5 micron limit was bullshit much more easily (the fact that many relevant people accepted it without that proof is a separate issue).

When I wrote up the Carol Dweck example, it was easy. I’m really confident in what Carol Dweck believed at the time of writing Mindset, so it’s really easy to describe why I disagree. Writing this section on the CDC was harder, because I cannot remember exactly what the CDC said and when they said it; a lot of the message lived in implications; their statements from early 2020 are now memory holed and while I’m sure I could find them on archive.org, it’s not really going to quiet the nagging fear that someone in the comments is going to pull up a different thing they said somewhere else that doesn’t say exactly what I claimed they said, or that I view as of a piece with what I cited but both statements are fuzzy enough that it would be a lot of work to explain why I think the differences are immaterial….

That fear and difficulty in describing someone’s beliefs is the hallmark of epistemic illegibility. The wider the confidence interval on what someone is claiming, the more work I have to do to question it.

And More…

The above was an unusually legible case of illegibility. Mostly illegible and false arguments don’t feel like that. They just feel frustrating and bad and like the other person is wrong but it’s too much work to demonstrate how. This is inconveniently similar to the feeling when the other person is right but you don’t want to admit it. I’m going to gesture some more at illegibility here, but it’s inherently an illegible concept so there will be genuinely legible (to someone) works that resemble these points, and illegible works that don’t.

Marks of probable illegibility:

  • The person counters every objection raised, but the counters aren’t logically consistent with each other. 
  • You can’t nail down exactly what the person actually believes. This doesn’t mean they’re uncertain – saying “I think this effect is somewhere between 0.1x and 10000x” is very legible, and sometimes the best you can do given the data. It’s more that they imply a narrow confidence band, but the value that band surrounds moves depending on the subargument. Or they agree they’re being vague but they move forward in the argument as if they were specific. 
  • You feel like you understand the argument and excitedly tell your friends. When they ask obvious questions you have no answer or explanation. 

A good example of illegibly bad arguments that are specifically trying to ape legibility are a certain subset of alt-medicine advertisements. They start out very specific, with things like “there are 9804538905 neurons in your brain carrying 38923098 neurotransmitters”, with rigorous citations demonstrating those numbers. Then they introduce their treatment in a way that very strongly implies it works with those 38923098 transmitters but not, like, what it does to them or why we would expect that to have a particular effect. Then they wrap it up with some vague claims about wellness, so you’re left with the feeling you’ll definitely feel better if you take their pill, but if you complain about any particular problem it did not fix they have plausible deniability.

[Unfortunately the FDA’s rules around labeling encourage this illegibility even for products that have good arguments and evidence for efficacy on specific problems, so the fact that a product does this isn’t conclusive evidence it’s useless.]

Bonus Example: Against The Grain

The concept of epistemic legibility was in large part inspired by my first attempt at James C. Scott’s Against the Grain (if that name seems familiar: Scott also coined “legibility” in the sense in which I am using it), whose thesis is that key properties of grains (as opposed to other domesticates) enabled early states. For complicated reasons I read more of AtG without epistemic checking than I usually would, and then checks were delayed indefinitely, and then covid hit, and then my freelancing business really took off… the point is, when I read Against the Grain in late 2019, it felt like it was going to be the easiest epistemic spot check I’d ever done. Scott was so cooperative in labeling his sources, claims, and logical conclusions. But when I finally sat down to check his work, I found serious illegibilities.

I did the spot check over Christmas this year (which required restarting the book). It was maybe 95% as good as I remembered, which is extremely high. At chapter 4 (which is halfway through the book, due to the preface and introduction), I felt kinda overloaded and started to spot check some claims (mostly factual – the logical ones all seemed to check out as I read them). A little resentfully, I checked this graph.

This should have been completely unnecessary, Scott is a decent writer and scientist who was not going to screw up basic dates. I even split the claims section of the draft into two sections, “Boring” and “Interesting”, because I obviously wasn’t going to come up with anything checking names and dates and I wanted that part to be easy to skip.

I worked from the bottom. At first, it was a little more useful than I expected – a major new interpretation of the data came out the same year the book was published, so Scott’s timing on anatomically modern humans was out of date, but not in a way that reflected poorly on him.

Finally I worked my way up to “first walled, territorial state”. Not thinking super hard, I googled “first walled city”, and got a date 3000 years before the one Scott cites. Not a big deal, he specified state, not walls. What I can google to find that out? “Earliest state”, obviously, and the first google hit does match Scott’s timing, but… what made something a state, and how can we assess those traits from archeological records? I checked, and nowhere in the preface, introduction, or first three chapters was “state” defined. No work can define every term it uses, but this is a pretty important one for a book whose full title is Against the Grain: A Deep History of the Earliest States

You might wonder if “state” had a widespread definition such that it didn’t need to be defined. I think this is not the case for a few reasons. First, Against The Grain is aimed at a mainstream audience, and that requires defining terms even if they’re commonly known by experts. Second, even if a reader knew the common definition of what made a state, how you determine whether something was a state or merely a city from archeology records is crucial for understanding the inner gears of the book’s thesis. Third, when Scott finally gives a definition, it’s not the same as the one on wikipedia.

[longer explanation] Among these characteristics, I propose to privilege those that point to territoriality and a specialized state apparatus: walls, tax collection, and officials.

Against the Grain

States are minimally defined by anthropologist David S. Sandeford as socially stratified and bureaucratically governed societies with at least four levels of settlement hierarchy (e.g., a large capital, cities, villages, and hamlets)

Wikipedia (as of 2021-12-26)

These aren’t incompatible, but they’re very far from isomorphic. I expect that even though there’s a fairly well accepted definition of state in the relevant field(s), there are disputed edges that matter very much for this exact discussion, in which Scott views himself as pushing back against the commonly accepted narrative. 

To be fair, the definition of state was not that relevant to chapters 1-3, which focus on pre-state farming. Unless, you know, your definition of “state” differs sufficiently from his. 

Against The Grain was indeed very legible in other ways, but loses basically all of its accrued legibility points and more for not making even a cursory definition of a crucial term in the introduction, and for doing an insufficient job halfway through the book.

This doesn’t mean the book is useless, but it does mean it was going to be more work to extract value from than I felt like putting in on this particular topic.

Why is this Important?

First of all, it’s costing me time.

I work really hard to believe true things and disbelieve false things, and people who argue illegibly make that harder, especially when people I respect treat arguments as more proven than their level of legibility allows them to be. I expect having a handle with which to say “no I don’t have a concise argument about why this work is wrong, and that’s a fact about the work” to be very useful.

More generally, I think there’s a range of acceptable legibility levels for a given goal, but we should react differently based on which legibility level the author chose, and that arguments will be more productive if everyone involved agrees on both the legibility level and on the proper response to a given legibility level. One rule I have is that it’s fine to declare something a butterfly idea and thus off limits to sharp criticism, but that inherently limits the calls to action you can make based on that idea. 

Eventually I hope people will develop some general consensus around the rights and responsibilities of a given level of legibility, and that this will make arguments easier and more productive. Establishing those rules is well beyond the scope of this post. 

Legibility vs Inferential Distance

You can’t explain everything to everyone all of the time. Some people are not going to have the background knowledge to understand a particular essay of yours. In cases like this, legibility is defined as “the reader walks away with the understanding that they didn’t understand your argument”. Illegibility in this case is when they erroneously think they understand your argument. In programming terms, it’s the difference between a failed function call returning a useful error message (legible), versus failing silently (illegible).  

A particularly dangerous way this can occur is when you’re using terms of art (meaning: words or phrases that have very specific meanings within a field) that are also common English words. You don’t want someone thinking you’re dismissing a medical miracle because you called it statistically insignificant, or invalidating the concept of thought work because it doesn’t apply force to move an object.

Cruelly, misunderstanding becomes more likely the more similar the technical definition is to the English definition. I watched a friend use the term “common knowledge” to mean “everyone knows that everyone knows, and everyone knows that everyone knows… and that metaknoweldge enables actions that wouldn’t be possible if it was merely true that everyone knew and thought they were the only one, and those additional possible actions are extremely relevant to our current conversation” to another friend who thought “common knowledge” meant “knowledge that is common”, and had I not intervened the ensuing conversation would have been useless at best.

Costs of Legibility

The obvious ones are time and mental effort, and those should not be discounted. Given a finite amount of time, legibility on one work trades off against another piece being produced at all, and that may be the wrong call.

A second is that legibility can make things really dry. Legibility often means precision, and precision is boring, especially relative to work optimized to be emotionally activating. 

Beyond that, legibility is not always desirable. For example, unilateral legibility in an adversarial environment makes you vulnerable, as you’re giving people the keys to the kingdom of “effective lies to tell you”. 

Lastly, premature epistemic legibility kills butterfly ideas, which are beautiful and precious and need to be defended until they can evolve combat skills.

How to be Legible

This could easily be multiple posts, I’m including a how-to section here more to help convey the concept of epistemic legibility than write a comprehensive guide to how to do it. The list is not a complete list, and items on it can be faked. I think a lot of legibility is downstream of something harder to describe. Nonetheless, here are a few ways to make yourself more legible, when that is your goal.

  • Make it clear what you actually believe.
    • Watch out for implicit quantitative estimates (“probably”, “a lot”, “not very much”) and make them explicit, even if you have a very wide confidence interval. The goals here are twofold: the first is to make your thought process explicit to you. The second is to avoid confusion – people can mean different things by “many”, and I’ve seen some very long arguments suddenly resolve when both sides gave actual numbers.
  • Make clear the evidence you are basing your beliefs on.
    • This need not mean “scientific fact” or “RCT”. It could be “I experienced this a bunch in my life” or “gut feeling” or “someone I really trust told me so”. Those are all valid reasons to believe things. You just need to label them.
  • Make that evidence easy to verify.
    • More accessible sources are better.
      • Try to avoid paywalls and $900 books with no digital versions.
      • If it’s a large work, use page numbers or timestamps to the specific claim, removing the burden to read an entire book to check your work (but if your claim rests on a large part of the work, better to say that than artificially constrict your evidence)
    • One difficulty is when the evidence is in a pattern, and no one has rigorously collated the data that would let you demonstrate it. You can gather the data yourself, but if it takes a lot of time it may not be worth it. 
    • In times past, when I wanted to refer to a belief I had in a blog post but didn’t have a citation for it, I would google the belief and link to the first article that came up. I regret this. Just because an article agrees with me doesn’t mean it’s good, or that its reasoning is my reasoning. So one, I might be passing on a bad argument. Two, I know that, so if someone discredits the linked article it doesn’t necessarily change my mind, or even create in me a feeling of obligation to investigate. I now view it as more honest to say “I believe this but only vaguely remember the reasons why”, and if it ends up being a point of contention I can hash it out later.
  • Make clear the logical steps between the evidence and your final conclusion.
  • Use examples. Like, so many more examples than you think. Almost everything could benefit from more examples, especially if you make it clear when they’re skippable so people who have grokked the concept can move on.
    • It’s helpful to make clear when an example is evidence vs when it’s a clarification of your beliefs. The difference is if you’d change your mind if the point was proven false: if yes, it’s evidence. If you’d say “okay fine, but there are a million other cases where the principle holds”, it’s an example.  One of the mistakes I made with early epistemic spot checks was putting too much emphasis on disproving examples that weren’t actually evidence.
  • Decide on an audience and tailor your vocabulary to them. 
    • All fields have words that mean something different in the field than in general conversation, like “work”, “airborne”, and “significant”. If you’re writing within the field, using those terms helps with legibility by conveying a specific idea very quickly. If you’re communicating outside the field, using such terms without definition hinders legibility, as laypeople misapply their general knowledge of the English language to your term of art and predictably get it wrong. You can help on the margins by defining the term in your text, but I consider some uses of this iffy.
      • The closer the technical definition of a term is to its common usage, the more likely this is to be a problem because it makes it much easier for the reader to think they understand your meaning when they don’t.
    • At first I wanted to yell at people who use terms of art in work aimed at the general population, but sometimes it’s unintentional, and sometimes it’s a domain expert who’s bad at public speaking and has been unexpectedly thrust onto a larger stage, and we could use more of the latter, so I don’t want to punish people too much here. But if you’re, say, a journalist who writes a general populace book but uses an academic term of art in a way that will predictably be misinterpreted, you have no such excuse and will go to legibility jail. 
    • A skill really good interviewers bring to the table is recognizing terms of art that are liable to confuse people and prompting domain experts to explain them.
  • Write things down, or at least write down your sources. I realize this is partially generational and Gen Z is more likely to find audio/video more accessible than written work, and accessibility is part of legibility. But if you’re relying on a large evidence base it’s very disruptive to include it in audio and very illegible to leave it out entirely, so write it down.
  • Follow all the rules of normal readability – grammar, paragraph breaks, no run-on sentences, etc.

A related but distinct skill is making your own thought process legible. John Wentworth describes that here.

Synthesis

“This isn’t very epistemically legible to me” is a valid description (when true), and a valid reason not to engage. It is not automatically a criticism.

“This idea is in its butterfly stage”, “I’m prioritizing other virtues” or “this wasn’t aimed at you” are all valid defenses against accusations of illegibility as a criticism (when true), but do not render the idea more legible.

“This call to action isn’t sufficiently epistemically legible to the people it’s aimed at” is an extremely valid criticism (when true), and we should be making it more often.

I apologize to Carol Dweck for 70% of the vigor of my criticism of her work; she deserves more credit than I gave her for making it so easy to do that. I still think she’s wrong, though.

Epilogue: Developing a Standard for Legibility

As mentioned above, I think the major value add from the concept of legibility is that it lets us talk about whether a given work is sufficiently legible for its goal. To do this, we need to have some common standards for how much legibility a given goal demands. My thoughts on this are much less developed and by definition common standards need to be developed by the community that holds them, not imposed by a random blogger, so I’ll save my ideas for a different post. 

Epilogue 2: Epistemic Cooperation

Epistemic legibility is part of a broader set of skills/traits I want to call epistemic cooperation. Unfortunately, legibility is the only one I have a really firm handle on right now (to the point I originally conflated the concepts, until a few conversations highlighted the distinction- thanks friends!). I think epistemic cooperation, in the sense of “makes it easy for us to work together to figure out the truth” is a useful concept in its own right, and hope to write more about it as I get additional handles. In the meantime, there are a few things I want to highlight as increasing or signalling cooperation in general but not legibility in particular:

  • Highlight ways your evidence is weak, related things you don’t believe, etc.
  • Volunteer biases you might have.
  • Provide reasons people might disagree with you.
  • Don’t emotionally charge an argument beyond what’s inherent in the topic, but don’t suppress emotion below what’s inherent in the topic either.
  • Don’t tie up brain space with data that doesn’t matter.

Thanks to Ray Arnold, John Salvatier, John Wentworth, and Matthew Graves for discussion on this post. 

Butterfly Ideas

Or “How I got my hyperanalytical friends to chill out and vibe on ideas for 5 minutes before testing them to destruction”

Sometimes talking with my friends is like intellectual combat, which is great. I am glad I have such strong cognitive warriors on my side. But not all ideas are ready for intellectual combat. If I don’t get my friend on board with this, some of them will crush an idea before it gets a chance to develop, which feels awful and can kill off promising avenues of investigation. It’s like showing a beautiful, fragile butterfly to your friend to demonstrate the power of flight, only to have them grab it and crush it in their hands, then point to the mangled corpse as proof butterflies not only don’t fly, but can’t fly, look how busted their wings are.

You know who you are

When I’m stuck in a conversation like that, it has been really helpful to explicitly label things as butterfly ideas. This has two purposes. First, it’s a shorthand for labeling what I want (nurturance and encouragement). Second, it explicitly labels the idea as not ready for prime time in ways that make it less threatening to my friends. They can support the exploration of my idea without worrying that support of exploration conveys agreement, or agreement conveys a commitment to act.

This is important because very few ideas start out ready for the rigors of combat. If they’re not given a sheltered period, they will die before they become useful. This cuts us off from a lot of goodness in the world. Examples:

  • A start-up I used to work for had a keyword that meant “I have a vague worried feeling I want to discuss without justifying”. This let people bring up concerns before they had an ironclad case for them and made statements that could otherwise have felt like intense criticism feel more like information sharing (they’re not asserting this will definitely fail, they’re asserting they have a feeling that might lead to some questions). This in turn meant that problems got brought up and addressed earlier, including problems in the classes “this is definitely gonna fail and we need to make major changes” and  “this excellent idea but Bob is missing the information that would help him understand why”.
    • This keyword was “FUD (fear, uncertainty, doubt)”. It is used in exactly the opposite way in cryptocurrency circles, where it means “you are trying to increase our anxiety with unfounded concerns, and that’s bad”. Words are tricky.
  • Power Buys You Distance From The Crime started out as a much less defensible seed of an idea with a much worse explanation. I know that had I talked about it in public it would have caused a bunch of unproductive yelling that made it harder to think because I did and it did (but later, when it was ready, intellectual combat with John Wentworth improved the idea further).
  • The entire genre of “Here’s a cool new emotional tool I’m exploring”
  • The entire genre of “I’m having a feeling about a thing and I don’t know why yet”

I’ve been on the butterfly crushing end of this myself- I’m thinking of a particular case last year where my friend brought up an idea that, if true, would require costly action on my part. I started arguing with the idea, they snapped at me to stop ruining their dreams. I chilled out, we had a long discussion about their goals, how they interpreted some evidence, and why they thought a particular action might further said goals, etc. 

A week later all of my objections to the specific idea were substantiated and we agreed not to do the thing- but thanks to the conversation we had in the meantime, I have a better understanding of them and what kinds of things would be appealing to them in the future. That was really valuable to me and I wouldn’t have learned all that if I’d crushed the butterfly in the beginning.

Notably, checking out that idea was fairly expensive, and only worth it because this was an extremely close friend (which both made the knowledge of them more valuable, and increased the payoff to helping them if they’d been right). If they had been any less close, I would have said “good luck with that” and gone about my day, and that would have been a perfectly virtuous reaction. 

I almost never discuss butterfly ideas on the public internet, or even 1:many channels. Even when people don’t actively antagonize them, the environment of Facebook or even large group chats means that people often read with half their brain and respond to a simplified version of what I said. For a class of ideas that live and die by context and nuance and pre-verbal intuitions, this is crushing. So what I write in public ends up being on the very defensible end of the things I think. This is a little bit of a shame, because the returns to finding new friends to study your particular butterflies with is so high, but ce la vie. 

This can play out a few ways in practice. Sometimes someone will say “this is a butterfly idea” before they start talking. Sometimes when someone is being inappropriately aggressive towards an idea the other person will snap “will you please stop crushing my butterflies!” and the other will get it. Sometimes someone will overstep, read the other’s facial expression, and say “oh, that was a butterfly, wasn’t it?”. All of these are marked improvements over what came before, and have led to more productive discussions with less emotional pain on both sides.

No Notes Research

I started watching The Vow with a friend, and got inspired to do a bunch of reading on cults in general and Vow’s cult, NXIVM, in particular. I didn’t originally take notes because this was coded in my head as a leisure activity, not real research. Eventually it became clear it was a real research project, but it seemed unfair to introduce real notes halfway through, so I decided to use it as an experiment in research without detailed notes instead (I did end up writing a few, but a far cry from The Algorithm). This turned out to be the right situation for that experiment, because my friend was a check on how much I actually remembered, especially on things we disagreed on, which was a lot.

Observations:

  • Memory is in fact hard. 
    • When I went to share what I learned with my friend, I often had to look back at my (sparse) notes to remember things I wanted to talk to him about. This is true even when I was talking to him the morning of the day after I read the book. 
    • Often he would ask me questions and the answer wasn’t in my notes- sometimes it was firmly in memory and I’d just forgotten to bring it up, sometimes I knew the book had the answer but I had lost it.
  • I mixed up sources a lot.
    • I would frequently assume my friend knew something, only to find out it wasn’t from our shared source. 
  • I’d compress specifics into patterns. This hindered me arguing with my friend.
  • In combination with the source mix-up, this often meant I couldn’t tell apart the following situations:
    • Books A and B repeating the same story with the same source (almost equivalent to one source)
    • Books A and B tell the same story from their own perspectives (stronger evidence the thing actually happened, but not evidence of a pattern)
    • Books A and B tell stories about similar things happening to different people (evidence of a pattern).
  • Losing the specifics that demonstrated a pattern also made it much harder to change my mind in response to new evidence. Is this more credible or a stronger signal than the data my current view is based on? Who’s to say, if I can’t remember the original evidence?
  • This feels way easier with something as emotionally salient as cults than it did with my more distant historical research. And when I did shift to a more history-style book (Mystics and Messiahs), I suddenly had to take real notes.
  • I did go into this with a question, but I didn’t know what it was until I’d read a few books and seen what felt live and what didn’t. 
    • The question was: how do we cultivate instincts/responses that seamlessly antagonize the unhealthy parts of cults while allowing for communities and new ideas.
    • Also: Interpersonal power: How does it work?

I feel like there’s more to learn from this experiment, but I really needed to write *something* today and a draft post I had on a beautiful theory of mine took a hit from an ugly gang of facts, so this is it.

This is pretty inside baseball and I suspect boring to most readers. You have my blessing to skip it.

Two weeks ago I published Knowledge Boostrapping v0.1, an algorithm for turning questions into answers. Since then I’ve gotten a moderate amount of feedback, a major theme of which is lack of clarity in certain sections. That’s entirely fair, but not something I immediately know how to fix. In an attempt to make some headway on the problem, I recorded in detail the steps I took in a recent attempt at using a method.

This isn’t really a central case of use- the question was of business norms, not The Truth, and the research I did more primed me to think of the solution myself, more than it provided the answer. But non-central examples are sometimes more useful and the cost to sharing is low given, so here we go.

 

 

  1. 12:30 Working on a project with someone, get to the point where we think “we should have a white paper”

  2. Problem: I’ve never written a white paper, and he has but isn’t really clear on what the goals or audience are. I decide this is worth an hour to get right.

  3. 12:45 Follow my own advice to come up with questions to investigate.

  4. Step 1 is make a mind map of questions. Discover Whimsical’s free plan only allows four creations, ever, even if you delete old work.

    1. Go on FB to complain to friend who recommended it to me

    2. 12:57. Try to create in Roam instead
      Start page currently looks like:

  5. 12:58 Google “white papers 101”

  6. 12:58: first hit https://thatwhitepaperguy.com/tips-best-practices-white-papers/ . Seems…uncomprehensive? Not high level enough? Extremely vague to the point of uselessness?

  7. 12:59 fight with NYTimes paywall https://www.nytimes.com/2009/05/12/business/smallbusiness/12toolkit.html . Article itself is useless but links to some promising pages.

    1. 1:01 https://smallbiztrends.com/2009/04/its-national-write-a-white-paper-month-april-2009.html . It’s basically a link to http://www.rgmcomms.com/whitepapers.html

      1. 1:02 http://www.rgmcomms.com/whitepapers.html no longer has the PDF referenced above

    2. 1:02 http://www.howtodothings.com/hobbies/a4622-how-to-write-a-white-paper.html

      1. Not useless. Begin notes on the top level Roam page, because I expect to only extract a few things from it.

      2. White papers can be aimed at a variety of audiences. Know which one you want and tailor it to them.

        1. 1:06 He doesn’t say it explicitly, but presumably a white paper is aimed at people you want things from. I think about who [partner] and I want things from

          1. This is kind of a dumb revelation. If white papers weren’t aimed at who we wanted things from, we should write a different kind of thing aimed at people we wanted things from.

          2. Rest of page is kind of vapid but I’m feeling pretty inspired.

 

 

 

 

 

 

  • 1:10 Remember to tag parent Roam page as a question. I would show you what Roam page looks like now, but apparently I forgot to take a picture.

  • Take that prompt and think a bit, till I come up with an algorithm I’m happy with

 

Breaking Questions Down

Previously I talked about discovering that my basic unit of inquiry should be questions, not books. But what I didn’t talk about was how to generate those questions, and how to separate good questions from bad. That’s because I don’t know yet; my own process is mysterious and implicit to me. But I can give a few examples.

For any given question, your goal is to disambiguate it into smaller questions that, if an oracle gave you the answers to all of them, would allow you to answer the original question. Best case scenario, you repeat this process and hit bedrock, an empirical question for which you can find accurate data. You feed that answer into the parent question, and eventually it bubbles up to answering your original question.

That does not always happen. Sometimes the question is one of values, not facts. Sometimes sufficient accurate information is not available, and you’re forced to use a range- an uncertainty that will bubble up through parent answers. But just having the questions will clarify your thoughts and allow you to move more of your attention to the most important things.

Here are a few examples.  First, a reconstructed mind map of my process that led to several covid+economics posts. In the interests of being as informative as possible, this one is kind of stylized and uses developments I didn’t have at the time I actually did the research.

Vague covid panic@2x.png

If you’re curious about the results of this, the regular recession post is here and the oil crisis post is here.

Second, a map I created but have not yet researched, on the cost/benefit profile of a dental cleaning while covid is present.

Risk model of dental cleanings in particular@2x.png

Aside: Do people prefer the horizontal or vertical displays? Vertical would be my preference, but Whimsical does weird things with spacing so the tree ends up with a huge width either way.

Honestly this post isn’t really done; I have a lot more to figure out when it comes to how to create good questions. But I wanted to have something out before I published v0.1 of my Grand List of Steps, so here we are.

Many thanks to Rosie Campbell for inspiration and discussion on this idea.

How to Find Sources in an Unreliable World

I spent a long time stalling on this post because I was framing the problem as “how to choose a book (or paper. Whatever)?”. The point of my project is to be able to get to correct models even from bad starting places, and part of the reason for that goal is that assessing a work often requires the same skills/knowledge you were hoping to get from said work. You can’t identify a good book in a field until you’ve read several. But improving your starting place does save time, so I should talk about how to choose a starting place.

One difficulty is that this process is heavily adversarial. A lot of people want you to believe a particular thing, and a larger set don’t care what you believe as long as you find your truth via their amazon affiliate link (full disclosure: I use amazon affiliate links on this blog). The latter group fills me with anger and sadness; at least the people trying to convert you believe in something (maybe even the thing they’re trying to convince you of). The link farmers are just polluting the commons.

With those difficulties in mind, here are some heuristics for finding good starting places.

  • Search “best book TOPIC” on google
    • Most of what you find will be useless listicles. If you want to save time, ignore everything on a dedicated recommendation site that isn’t five books.
    • If you want to evaluate a list, look for a list author with deep models on both the problem they are trying to address, and why each book in particular helps educate on that problem.  Examples:
    • A bad list will typically have a topic rather than a question they are trying to answer, and will talk about why books they recommend are generically good, rather than how they address a particular issue. Quoting consumer reviews is an extremely bad sign and I’ve never seen it done without being content farming.
  • Search for your topic on Google Scholar
    • Look at highly cited papers. Even if they’re wrong, they’re probably important for understanding what else you read.
    • Look at what they cite or are cited by
    • Especially keep an eye out for review articles
  • Search for web forums on your topic (easy mode: just check reddit). Sometimes these will have intro guides with recommendations, sometimes they will have where-to-start posts, and sometimes you can ask them directly for recommendations. Examples:
  • Search Amazon for books on your topic. Check related books as well.
  • Ask your followers on social media. Better, announce what you are going to read and wait for people to tell you why you are wrong (appreciate it, Ian). Admittedly there’s a lot of prep work that goes into having friends/a following that makes this work, but it has a lot of other benefits so if it sounds fun to you I do recommend it. Example:
  • Ask an expert. If you already know an expert, great. If you don’t, this won’t necessarily save you any time, because you have to search for and assess the quality of the expert.
  • Follow interesting people on social media and squirrel away their recommendations as they make them, whether they’re relevant to your current projects or not.

Types of Knowledge

This is a system for sorting types of knowledge. There are many like it, but this one is mine.

First, there is knowledge you could regurgitate on a test. In any sane world this wouldn’t be called knowledge, but the school system sure looks enthusiastic about it, so I had to mention it. Examples:

  • Reciting the symptoms of childbed fever on command 
  • Reciting Newton’s first law of motion
  • Reciting a list of medications’ scientific and brand names
  • Reciting historical growth rate of the stock market
  • Reciting that acceleration due to gravity on Earth is 9.807 m/s²

 

Second, there is engineering knowledge- something you can repeat and get reasonably consistent results. It also lets you hill climb to local improvements. Examples:

  • Knowing how to wash your hands to prevent childbed fever and doing so
  • Driving without crashing
  • Making bread from a memorized recipe.
  • What are the average benefits and side effects from this antidepressant?
  • Knowing how much a mask will limit covid’s spread
  • Investing in index funds
  • Knowing that if you shoot a cannon ball of a certain weight at a certain speed, it will go X far.
  • Knowing people are nicer to me when I say “please” and “thank you”

 

Third, there is scientific knowledge. This is knowledge that lets you generate predictions for how a new thing will work, or how an old thing will work in a new environment, without any empirical knowledge.

Examples: 

  • Understanding germ theory of disease so you can take procedures that prevent gangrene and apply them to childbed fever.
  • Knowing the science of baking so you can create novel edible creations on your first try.
  • Knowing enough about engines and batteries to invent hybrid cars.
  • Actually understanding why any of those antidepressants works, in a mechanistic way, such that you can predict who they will and won’t work for.
  • A model of how covid is spread through aerosols, and how that is affected by properties of covid and the environment.
  • Having a model of economic change that allows you to make money off the stock market in excess of its growth rate, or know when to pull out of stocks and into crypto.
  • A model of gravity that lets you shoot a rocket into orbit on the first try.
  • A deep understanding of why certain people’s “please”s and “thank you”s get better results than others.

 

Engineering knowledge is a lot cheaper to get and maintain than scientific knowledge, and most of the time it works out. Maybe I pay more than I needed to for a car repair; I’ll live (although for some people the difference is very significant). You need scientific knowledge to do new things, which either means you’re trying something genuinely new, or you’re trying to maintain an existing system in a new environment.

I don’t know if you’ve noticed, but our environment was changing pretty rapidly before a highly contagious, somewhat deadly virus was released on the entire world, and while that had made things simpler in certain ways (such as my daily wardrobe), it has ultimately made it harder to maintain existing systems. This requires scientific knowledge to fix; engineering won’t cut it.

And it requires a lot of scientific knowledge at that- far more than I have time to generate. I could trust other people’s answers, but credentials and authority have never looked more useless, and identifying people I trust on any given subject is almost as time consuming as generating the answers myself.  And I don’t know what to do about that.

 

What to write down when you’re reading to learn

One of the hardest questions I’ve had to answer as part of the project formerly known as epistemic spot checks is: “how do I know what to write down?”

This will be kind of meandering, so here’s the take home. 

For shallow research:

  • Determine/discover what you care about before you start reading.
  • Write down anything relevant to that care.

For deep research:

  • Write down anything you find interesting.
  • Write down anything important to the work’s key argument.
  • Write down anything that’s taking up mental RAM, whether it seems related or interesting or not. If you find you’re doing this a lot, consider you might have a secret goal you don’t know about.
  • The less 1:1 the correspondence between your notes and the author’s words the better. Copy/pasting requires little to no engagement, alternate theories for the explanations spread over an entire chapter require a lot.

 

Now back to our regularly scheduled blog post.

Writing down a thing you’ve read (/heard/etc) improves your memory and understanding, at the cost of disrupting the flow of reading. Having written a thing down makes that one thing easier to rediscover, at the cost of making every other thing you have or will ever write down a little harder to find. Oh, and doing the math on this tradeoff while you’re reading is both really costly and requires knowing the future. 

I would like to give you a simple checklist for determining when to save a piece of information. Unfortunately I never developed one. There are obvious things like “is this interesting to me (for any reason)?” and “is this key to the author’s argument?”, but those never got rid of the nagging feeling that I was losing information I might find useful someday, and specifically that I was doing shallow research (which implies taking the author’s word for things) and not deep (which implies making my own models). 

The single most helpful thing in figuring out what to write down was noticing when my reading was slowing down, which typically meant either there was a particular fact that needed to be moved from short to long term storage, or that I needed to think about something. Things in these categories need to be written down and thought about regardless of their actual importance, because their perceived importance is eating up resources, and 30 seconds writing something down to regain those resources is a good trade even if I never use that information again. If I have one piece of advice, it’s “learn to recognize the subtle drag of something requiring your attention.”

An obvious question is “how do I do that though?”. I’m a mediocre person to answer this question because I didn’t set out to learn the skill, I just noticed I was doing it. But for things in this general class, the best thing I have found to do is get yourself in a state where you are very certain you have no drag (by doing a total brain dump), do some research, and pay attention to when drag develops. 

But of course it’s much better if my sense of “this is important, record it” corresponds with what is actually important. The real question here is “Important to what?” When I was doing book-based reviews, the answer at best was “the book’s thesis”, which as previously discussed gives the author a huge amount of power to control the narrative. But this became almost trivial when I switched the frame to answering a specific set of questions. As long as I had a very clear goal in mind, my subconscious would do most of the work. 

This isn’t a total solution though, because of the vast swath of territory labeled “getting oriented with what I don’t know”. For example right now I want to ask some specific questions about the Great Depression and what it can tell us about the upcoming economic crisis, but I don’t feel I know enough. It is very hard to get oriented with patchwork papers: you typically need books with cohesive narratives, and then to find other ways to undo the authors’ framing. Like a lot of things, this is solved by going meta. “I want to learn enough about the Great Depression that I have a framework to ask questions about parallels to the current crisis” was enough to let me evaluate different “Top Books about the Great Depression” lists and identify the one whose author was most in line with my goals (it was the one on fivebooks, which seems to be the case much more often than chance).

I mentioned “losing flow” as a cost of note taking in my opening, but I’m not actually convinced that’s a cost. Breaking flow also means breaking the author’s hold on you and thinking for yourself. I’ve noticed a pretty linear correlation between “how much does this break flow?” and “how much does this make me think for myself and draw novel conclusions?”. Copy/pasting an event that took place on a date doesn’t break flow but doesn’t inspire much thought. Writing down your questions about information that seems to be missing, or alternate interpretations of facts, takes a lot longer.

Which brings me to another point: for deep reading, copy pasting is almost always Doing It Wrong. Even simple paraphrasing requires more engagement than copy/pasting. Don’t cargo cult this though: there’s only so many ways to say simple facts, and grammar exercises don’t actually teach you anything about the subject.

So there is my very unsatisfying list of how to know what to write down when you’re reading to learn. I hope it helps.

Where to Start Research?

When I began what I called the knowledge bootstrapping project, my ultimate goal was “Learn how to learn a subject from scratch, without deference to credentialed authorities”. That was too large and unpredictable for a single grant, so when I applied to LTFF, my stated goal was “learn how to study a single book”, on the theory that books are the natural subcomponents of learning (discounting papers because they’re too small). This turned out to have a flawed assumption baked into it.

As will be described in a forthcoming post, the method I eventually landed upon involves starting with a question, not a book. If I start with a book and investigate the questions it brings up (you know, like I’ve been doing for the last 3-6 years), the book is controlling which questions get brought up. That’s a lot of power to give to something I have explicitly decided not to trust yet. 

Examples:

  • When reading The Unbound Prometheus, I took the book’s word that a lower European birth rate would prove Europeans were more rational than Asians and focused on determining whether Europe’s birth rates were in fact lower (answer: it’s complicated), when on reflection it’s not at all clear to me that lower birth rates are evidence of rationality.
  • “Do humans have exactly 4 hours of work per day in them?” is not actually a very useful question. What I really wanted to know is “when can I stop beating myself up for not working?“, and the answer to the former doesn’t really help me with the latter. Even if humans on average have 4 hours, that doesn’t mean I do, and of course it varies by circumstances and type of work… and even “when can I stop beating myself up?” has some pretty problematic assumptions built into it, such as “beating myself up will produce more work, which is good.” The real question is something like “how can I approach my day to get the most out of it?”, and the research I did on verifying a paper on average daily work capacity didn’t inform the real question one way or the other.

 

What would have been better is if I’d started with the actual question I wanted to answer, and then looked for books that had information bearing on that question (including indirectly, including very indirectly). This is what I’ve started doing.

This can look very different depending on what type of research I’m doing. When I started doing covid research, I generated a long list of  fairly shallow questions.  Most of these questions were designed to inform specific choices, like “when should I wear what kind of mask?” and “how paranoid should I be about people without current symptoms?”, but some of them were broader and designed to inform multiple more specific questions, such as “what is the basic science of coronavirus?”. These broader, more basic questions helped me judge the information I used to inform the more specific, actionable questions (e.g., I saw a claim that covid lasted forever in your body the same way HIV does, which I could immediately dismiss because I knew HIV inserted itself your DNA and coronaviruses never enter the nucleus).

 


 

I used to read a lot of nonfiction for leisure. Then I started doing epistemic spot checks– taking selected claims from a book and investigating them for truth value, to assess the book’s overall credibility- and stopped being able to read nonfiction without doing that, unless it was one of a very short list of authors who’d made it onto my trust list. I couldn’t take the risk that I was reading something false and would absorb it as if it were true (or true but unrepresentative, and absorb it as representative). My time spent reading nonfiction went way down.

About 9 months ago I started taking really rigorous notes when I read nonfiction. The gap in quality of learning between rigorous notes and my previous mediocre notes was about the same as the gap between doing an epistemic spot check and not. My time spent reading nonfiction went way up (in part because I was studying the process of doing so), but my volume of words read dropped precipitously.

And then three months ago I shifted from my unit of inquiry being “a book”, to being “a question”. I’m sure you can guess where this is going- I read fewer words, but gained more understanding per word, and especially more core (as opposed to shell or test) understanding. 

The first two shifts happened naturally, and while I missed reading nonfiction for fun and with less effort, I didn’t feel any pull towards the old way after I discovered the new way. Giving up book-centered reading has been hard. Especially after five weeks of frantic covid research, all I wanted to do was to be sat down and told what questions were important, and perhaps be walked through some plausible answers. I labeled this a desire to learn, but when I compared it to question-centered research, it became clear that’s not what it was. Or maybe it was a desire to go through the act of learning something, but it was not a desire to answer a question I had and was not prioritized by the importance of a question. It was best classified as leisure in the form of learning, not resolving a curiosity I had.  And if I wanted leisure, better to consume something easier and less likely to lead me astray, so I started reading more fiction, and the rare non-fiction of a type that did not risk polluting my pool of data. And honestly I’m not sure that’s so safe: humans are built to extract lessons from fiction too.

Put another way: I goal factored (figured out what I actually wanted from) reading a nonfiction book, and the goal was almost never best served by using a nonfiction book as a starting point. Investigating a question I cared about was almost always better for learning (even if it did eventually cash out in reading a book), and fiction was almost always better for leisure, in part because it was less tiring, and thus left more energy for question-centered learning when that was what I wanted.

 

Really Ridiculously Thorough Notes

Recently I tried an experiment. My note taking method already involves trying to record every single claim a book makes- I added to that “record every thought I have about the claim.” This included information that bore on the claim (e.g. if the claim was “A wave of German Catholics emigrated to American colonies from 1720-1741”, my thoughts would include “wait, wasn’t Germany Protestant?” and “Germany didn’t have any colonies of its own”) , questions it raised (e.g., “what was the state of German Protestant immigration to American colonies?”, “what constitutes a wave of immigration?”, “how did they fund the travel?”) and potential implications (“They would learn to need English”). Obviously this would be incredibly onerous to do all the time; my goal was to see what changes occurred when I did it, and perhaps train the muscle so it would be easier to do so in the future.

For a test subject I chose Children in Colonial America, of which I had skipped the last three chapters because they didn’t bear on my overall question that much. However they were a much better size and format than my the next two books in my queue, and I’d be able to get to the meat faster because I’d already read the previous chapters.

You can see my notes for the book as a whole here, the experiment starts with Chapter 10

Day 1, Round 1 (Chapter 10):

  • Not a perfect experiment; I’d taken Ritalin for the first time in a while before deciding to run the experiment and it obviously altered the experience a lot.
  • I got through a pre-read (basically a non-exhaustive reading of the first and last few paragraphs) and two pages in 1.5 hours.
  • After 1.5 hours I was done. Could not continue with the experiment for love or money. I went on to work on a blog post about cat-mitigation strategies for an hour, so it’s not that the Ritalin quit.
  • Even explicitly giving myself prompts to write down *everything* I thought related to a claim, I would sometimes notice new thoughts well after I’d left a particular claim.

Day 1, Round 2:

  • Tried for a bit but couldn’t muster the energy to go really into detail like I did above.

 

Day 2, Round 1 (Chapter 10):

  • More intense than D1R2 but less than D1R1, finished early when I finished a chapter.

 

Day 3 and 4, Round 1 (Chapter 11):

  • Started (day 3) and finished (day 4) Chapter 11 of Children in Colonial America. Either something has changed in my capacity to this work, or the work showed me something was wrong with the chapter, even though I can’t put my finger on it.

 

Day 5 (Chapter 12):

  • Became irritated in the pre-reading phase, spent 2 hours writing a blog post about why the final paragraph signaled low quality.

 

Day 6, Round 1 (Chapter 1):

  • Coincidentally experimented with caffeine + theanine + MCT oil in the morning.
  • Published complaints about Chapter 12.
  • I wanted to extend the experiment- both the deep note-taking, and predicting work quality in the pre-reading stage. I have some books out from the library, but they’re full books, not anthologies, and I feel like stand-alone chapters give me faster feedback.
  • Discovered that Children in Colonial America is book 3 in a series on children in America, and there are three other books with the same editor on different time periods. This is great because it lets me minimize the changing variables as I continue the experiment.
  • Read Chapter 1 and deep note take Children and Youth in a New Nation (notes). I’m not able able to go quite as deep as in attempt 1, but then, Ritalin. Chapter 1 of CaYiaNN is one of those middling history works that doesn’t have an overarching thesis but knows it is structurally expected to have one, so makes a thesis out of its uncertainty: “Some people had a variety of experiences with X for a variety of reasons.” Become inspired to write Fake Thesis Vs Absent Thesis.

 

Both my note-taking process and the notes I took on it gradually declined as I attempted to read Chapter 2 of Children and Youth in a New Nation, culminating in going days where I couldn’t even push myself into reading. So… can’t say I recommend this. I’m working out other ways to approach the goal of contextualizing information as I read.