I’m going to draw some practical distinctions among types of knowledge, as an attempt to tap into your intuitions and avoid having to give some convoluted, ivory-tower definition of the word.  I request that you try not to get distracted by where I’ve drawn my distinctions—the precise placement of the borders is not the point of the exercise. The point of the exercise is to shine your attention on the richness, depth, and complexity of your capacity to know—that the word “know” means more than one thing.

Let’s begin with a little formative assessment.

Do you know what comes out of your kitchen faucet?

Do you know what glaciers are made of?

Do you know what those big white fluffy things in the sky are?

If so, then you are familiar with water. When someone talks about it, you’re not completely lost.

Do you know at what temperature water boils?

Do you know the atomic composition of an ordinary water molecule?

Do you know what percentage of your body’s volume is water?

If so, then you know some facts about water. Your concept of water contains (at minimum) a few isolated pieces of accurate information.

Do you know the way water sounds when it pours into a cup?

Do you know how water feels when it runs over your skin?

Do you know the look of a stream's surface as it glitters in the sun?

If so, then you are able to identify water when you encounter it in real life. You have direct, experiential data. You are able to predict how various encounters with water will impact your senses, and you probably recognize water when those sensations occur (at least sometimes).

Do you know what happens if you leave a beer bottle in the freezer overnight?

Do you know how a water mill grinds grain into flour?

Do you know why it rains?

If so, then you probably have at least one model[1] of water.

Whether or not that model is explicit, it includes enough structure that you can predict the behavior of water in various situations, even if those situations are outside of your own direct experience. Your model might be rudimentary, in which case the above questions probably produced hesitation or “sort of?” and you’d maybe only be able to produce a short paragraph in response to each. Or your model might be rich and deep, in which case your “yes” was confident, and you could in principle write multiple essays on the subject.

Do you know how to swim?

Do you know what to expect when applying watercolor paints to a wet canvas?

Do you know how to make sea water safe to drink?

If so, then you have some practical mastery of water. It’s not just that you recognize water, or that you know some things about it, or that you can predict its behavior—your models of water are integrated with your models of yourself and other parts of the world, accurately and deeply enough that when you personally interact with water in real life, things tend to go more or less as you intend (at least in certain kinds of situations).

Breaking the format of the pop quiz now, to ask a more difficult question:

Can you name other things you know as intimately and thoroughly as water? Is there some swath of the territory with which you have extensive familiarity, lots of factual knowledge, rich predictive and explanatory models, and also practical mastery in a wide variety of situations? In other words, where do you think you might have deep mastery?

One such domain that’s likely common: many people have this portfolio of knowledge when it comes to driving cars. The average American spends a little under an hour a day in a car, so if you’re like the average American in this respect (and also you’re the one doing most of the driving for your household), then you’ve plausibly spent over three thousand hours behind the wheel in the past ten years. And if so, I expect you’ve deeply mastered driving[2].

If you've deeply mastered driving, then you have extensive familiarity with all sorts of driving-related tasks and phenomena. You recognize left-turn-only lanes, brake pedals, stop signs, curves in the road, the hazard lights button on your dashboard, erratic driving, potholes, high beams, deer, and so on.

You probably have tons of factual knowledge related to driving, as well, even if it’s been many years since you’ve taken a written test. If an inquisitive fourteen-year-old were sitting in the passenger seat, you could produce all sorts of relevant bits of data, such as what speeds they should expect to drive on what kinds of roads, or what fluids they’ll need to put into their car at what frequencies, or how many wheels most cars have, or what papers they’ll need if they get pulled over or have a minor accident.

You probably also have rich, complex models of driving itself, organized to allow you to make reasonable predictions about driving-related situations and phenomena. If your car breaks down on the road, you might or might not know how to fix it, but I bet you at least pull over to the side, because you know how roads work, and you know implicitly that if you stay put, other cars will come up behind you at high speeds and possibly crash into you. If I offered you a large amount of money to fill a hundred pages on “how driving works,” you could almost certainly do it, especially if I provided helpful prompts like “differences between driving in cities vs. driving in rural areas” or “things that other drivers frequently get wrong.”

And all of these different kinds of knowledge—facts, familiarity, implicit and practical models—they’re all seamlessly integrated with an experienced driver’s knowledge of themselves, and with their knowledge of adjacent domains like travel, geography, weather, car maintenance, the side effects of medication, etc. An experienced driver doesn’t (usually) access their knowledge about driving via explicit lookup. They can do that, on request, but most of the time they simply move through the world. They use their turn signal reflexively in the middle of deep conversation with their passengers. When someone suddenly swerves into their lane, they decelerate without deciding to decelerate. They stop for gas on road trips.  They notice when something about their car just feels off. And they acquired most of this knowledge in the process of developing the skills and habits required to safely operate the vehicle.

This is what I mean by “knowing,” in the sentence “Knowing the territory takes patient and direct observation.” By “knowing”, I mean something like deep mastery.

[If you want extensive familiarity, accurate factual knowledge, richly detailed predictive models, and thorough practical mastery of some part of] the territory (that is, if you want deep mastery), then you will have to engage in patient and direct observation.


  1. ^

    There is a principled distinction to be made between models and theories. I'm not making it here.

  2. ^

    If you're doing just fine and enjoying this essay so far, skip this footnote. Otherwise, I have some bonus words that might possibly help.

    It was around this point when some of my beta readers noticed their frustration with how slowly we were going. They found themselves falling out of the spacious, expansive mode I had hoped they would be in. I think this is fine to do, as long as you think falling out of that mode is a good idea.

    But I note that there is another thing you could do, if you're frustrated or bored, which is to look at your own mind, notice the reactions that are happening, ask yourself what they're happening in response to, and thereby ease back into wondering.

    Many of us have words for the kinds of distinctions I'm trying to draw here, such as "S1 vs S2",' or "tacit vs explicit knowledge", or "declarative vs procedural knowledge". And the thing about those distinctions is that they are a) useful, and b) curiosity-stoppers. They tell us "don't worry, you already know this" so you can get back to building a tower of interconnected concepts. Which is a good thing, most of the time, but it is a bad thing some of the time, and I expect that many of my readers do not know how to tell the difference. (I often do not know how to tell the difference.)

    That is (in part) why we are going slowly here, and feeling our way forward without much reliance on a large preexisting vocabulary. That large preexisting vocabulary is good, but it is not perfect. In order to see its flaws, you have to be able to stand outside of it somehow. I'm trying to help you step at least a little bit outside of it.


6 comments, sorted by Click to highlight new comments since: Today at 9:57 PM
New Comment

All this knowledge about water, and fully 10% of the time, I can't pour water from a coffee pot without having it spill...

I seriously love this categorization of different ways of "knowing," and am already thinking of ways to use it in some story or another.

"The thing about those distinctions is that they are a) useful, and b) curiosity-stoppers. They tell us "don't worry, you already know this" so you can get back to building a tower of interconnected concepts. Which is a good thing, most of the time, but it is a bad thing some of the time"


I liked this footnote, but I'm not sure why. I'm going to say some things to try to think about it more clearly.

What this footnote seems to me to be about (in part) is something like:

  1. Stop attaching string to your insane-person cork board
  2. Notice that the things you are connecting with string are sketches, not photos
  3. Remain stopped for long enough to fill in your sketches a bit, erase some bits, and add a little colour


On this model, I am truly appalling at [1], and therefore rarely get the opportunity to practice [2] and [3]. I actually quite enjoy the feeling of remaining stopped on its own, but I think adding string feels to me too much like 'learning things' for me to look past it very often. 

This model is (of course) wrong, but it feels closer to me than other words I have to point to it. 

I also noticed that my brain likes first-things to cause (be required by?) second-things, so my initial model of the main text was something like familiarity → facts → identification → models → mastery. This could be intended, and does reflectively seem fairly sensible, but I can imagine having practical mastery over something without a complex (or even correct) model of it. Exercising seems like a good example where I think sufficient experience could create practical mastery without a strong model or many facts. 

However, I was surprised when you picked out driving as an example, as I wouldn't have said I have a strong model of how a car works. This probably means I've misunderstood what you mean by 'models' and 'facts'. 

I think what's going on is that I'm getting distracted by the context I usually hear the words 'model' and 'fact' in, next to words like 'science', 'engineering' and 'textbook'. This is getting in the way of me thinking about things like 'if I exercise when I haven't in a long time, my arms and legs will feel sore afterwards' as facts. 

Ha! Wow! If this was a vector it would point towards mechanisms and control surfaces. Further still and we get internalization. Where the difference between the self and the subject are blurred. Exciting!

(Copying FB reactions I made as I read) 1. I care a lot about deep mastery. 2. It doesn’t feel immediately obvious that direct observation is the fastest route to deep mastery. Like, say I wanted to understand a new field - bio for example. I would start with some textbooks, not staring at my dog and hoping I’d understand how he worked. I’d get to examples and direct experience, but my initial instinct is to start with pre-existing frameworks. But maybe I’m just misunderstanding what “direct observation” means?

I want to understand how to actually get humans to do the right things, and that task feels gargantuan without building on the foundation of simplified handles other people have discovered.

Yet, I value something like naturalism because I don't trust many handles as they are now, especially coming from psychology. "Confusion-spotting" seems pretty important if I'm going to improve on the status quo.