Types of Knowledge

by ElizabethAceso Under Glass3 min read20th Jun 202011 comments

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Rationality
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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.

 

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I originally named the types of knowledge "Type 1", "Type 2", and "Type 3", but was encouraged by early reviewers to actually name them. In light of the conversation here, I think doing that was a mistake. Unless I was sure my names were out of the park correct (and maybe not even then), I should have left it generic so I could get input on more people for what the names, and for that matter definitions, should be.

First, an apology as this comment is going to be frustratingly lacking much in the way of concrete examples. I have the kernel of an idea, but it would require more thought than I'm willing to put into it to expand it. I post it to get it out of my head and in case maybe someone else will want to think about it more...

I kind of understand the categories you're trying to carve out, but I'm also leery of them. It feels like your descriptions of the categories make assumptions about meanings and these assumptions are hidden and a person could trick themselves.

I'd have to think about it a lot more to really pin down the ephemeral idea I'm trying to get at, but it's similar to the observation I've made here before that Sherlock Holmes's observation that "Once you eliminate the impossible, whatever remains, no matter how improbable, must be the truth." is dangerous because its too easy to convince yourself that you've explored all the possible explanations.

In a similar manner, your description of why something should be considered engineering or scientific knowledge feels as if a person could convince themselves without realizing it that a thing belongs in one category or another where from an objective standpoint you'd be able to make a rational argument for something appearing in either category.

It also feels as if many things will switch between categories depending upon your priors. Your system as stated seems like it would put reciting the steps to make bread and making bread with those steps into separate categories. As an avid bread maker, I'm currently unconvinced of the utility of a category system that would put reciting the steps to make bread and actually making bread with those steps into different categories. I guess I would ask what is the goal of putting those two things into separate categories? What do you hope to get out of doing so?

On the other hand, I'm also unconvinced that a category system has to be so rigorous to be useful. It might be that a category system can be just rigorous enough to help a person...but, like I said, I'm leery that it will lead a person astray from their goals of using such a system without them realizing it.

Not to quibble on words and categories but this feels like a straw man of engineering knowledge that reinforces the view I've mentioned before that people here often have a bias that causes them to think of engineering as "trivial" or "easy". As I understand it, you're arguing that engineering knowledge only allows local improvements whereas science allows global optimization, whereas I feel like the reality is murkier but something like what Jason Crawford described in his recent post on shuttling between science and invention. I'm also partial to Eric Drexler's framing where science is about universal quantifiers -- the space of mechanisms -- whereas engineering is about existential quantifiers -- if there exists even one way to do something, then let's find it. This is a little abstract so I'll discuss a few of your examples.

To take your example of hybrid cars, yes inventing hybrid cars' components certainly required scientific breakthroughs but their economic feasibility heavily benefited from improved engineering of both batteries and cars. I'm not an expert on this but I feel like as a general statement it's hopefully not that controversial?

Related to this, the thing you mention about car maintenance is discussing mechanics, who are distinct from engineers. Consider an alternative statement, "yeah it's good that cars are 10X cheaper than they used to be, but the important thing was inventing the internal combustion engine in the first place." Isn't this a more balanced comparison for the role of engineering and science in car production?

Happy to go into more detail but will stop here because I'm having trouble anticipating whether my comment will 1) provoke disagreement and 2) which aspects are confusing / most likely to be disagrees with.

Ignoring the labels I put on them, do you feel like you have a good sense of what I mean by each kind of knowledge? if so, what would you label them?

Good question, yes I know what you mean. I don't think these are great labels but to me the categories seem like reguritated facts, pre-scientific empiricism / folk knowledge, and science and engineering. Admittedly I know these don't make great section headings, so I'll think more about better names.

Another comment mentions "know what", "know how", and "know why" which I suspect captures some of what you're getting at but not all of it? Only some because there are different types of whys within levels 2 & 3, right?

A thing I really structured to capture was that "i did actual research and had actual models for why masks would help against covid, but it's still not type-3", which is why "know why" doesn't feel right to me.

I tentatively think that some of what you're calling engineering knowledge would fit into what I call scientific (which is a strike agains the names), and/or that I didn't do a good enough job explaining why engineering knowledge is useful.

A thing I really structured to capture was that "i did actual research and had actual models for why masks would help against covid, but it's still not type-3", which is why "know why" doesn't feel right to me.

I share this feeling based on my understanding of the boundaries you're trying to draw.

I tentatively think that some of what you're calling engineering knowledge would fit into what I call scientific (which is a strike agains the names), and/or that I didn't do a good enough job explaining why engineering knowledge is useful.

Yeah, like I said, I don't want to bike shed over terms but I do think the distinction between science and engineering is an interesting one. Regarding the "and/or" isn't it more like there's often an interplay between the levels? First we get "folk knowledge" and then we "do science" to understand it and then we use that science to "engineer" it?

Also, I found an Overcoming Bias post where Robin quotes Drexler on the distinction:

The essence of science is inquiry; the essence of engineering is design. Scientific inquiry expands the scope of human perception and understanding; engineering design expands the scope of human plans and results. …

Scientists seek unique, correct theories, and if several theories seem plausible, all but one must be wrong, while engineers seek options for working designs, and if several options will work, success is assured. Scientists seek theories that apply across the widest possible range (the Standard Model applies to everything), while engineers seek concepts well-suited to particular domains (liquid-cooled nozzles for engines in liquid-fueled rockets). Scientists seek theories that make precise, hence brittle predictions (like Newton’s), while engineers seek designs that provide a robust margin of safety. In science a single failed prediction can disprove a theory, no matter how many previous tests it has passed, while in engineering one successful design can validate a concept, no matter how many previous versions have failed. ..

Simple systems can behave in ways beyond the reach of predictive calculation. This is true even in classical physics. …. Engineers, however, can constrain and master this sort of unpredictability. A pipe carrying turbulent water is unpredictable inside (despite being like a shielded box), yet can deliver water reliably through a faucet downstream. The details of this turbulent flow are beyond prediction, yet everything about the flow is bounded in magnitude, and in a robust engineering design the unpredictable details won’t matter. …

The reason that aircraft seldom fall from the sky with a broken wing isn’t that anyone has perfect knowledge of dislocation dynamics and high-cycle fatigue in dispersion-hardened aluminum, nor because of perfect design calculations, nor because of perfection of any other kind. Instead, the reason that wings remain intact is that engineers apply conservative design, specifying structures that will survive even unlikely events, taking account of expected flaws in high-quality components, crack growth in aluminum under high-cycle fatigue, and known inaccuracies in the design calculations themselves. This design discipline provides safety margins, and safety margins explain why disasters are rare. …

The key to designing and managing complexity is to work with design components of a particular kind— components that are complex, yet can be understood and described in a simple way from the outside. … Exotic effects that are hard to discover or measure will almost certainly be easy to avoid or ignore. … Exotic effects that can be discovered and measured can sometimes be exploited for practical purposes. …

When faced with imprecise knowledge, a scientist will be inclined to improve it, yet an engineer will routinely accept it. Might predictions be wrong by as much as 10 percent, and for poorly understood reasons? The reasons may pose a difficult scientific puzzle, yet an engineer might see no problem at all. Add a 50 percent margin of safety, and move on.

I'm not sure I agree with this distinction between science and engineering.

Theories are a kind of product. They're akin to an algorithm, machine, or process. They allow you to rapidly do a form of useful work: to predict experimental outcomes, design tools and interventions, and explain observed phenomena. An experiment is like a prototype. It's just a way of testing your ideas out in the real world. Just like a prototype, sometimes it takes many attempts to get an experiment to work convincingly (either to support or falsify), because there are so many details in the execution.

A scientist who studies scotopic vision in the Elephant Hawk Moth, Deilephila elpenor, is striving to build an accurate model of moth vision. This is not fundamentally different from an engineer who's designing night vision goggles or a pharmaceutical company researcher trying to develop a drug to improve night vision in people with an eye disorder. It's just a different kind of product - a conceptual, predictive product, rather than a tool or a drug. Their moth vision model doesn't have to work perfectly, either: just well enough to achieve statistical significance.

An engineer and a scientist may both be dissatisfied with imprecision when something important is at stake. If fuel efficiency doesn't matter because gas is cheap and global warming is unknown, then figuring out how to double gas mileage doesn't matter. But if we're trying to sell an electric car, it's not enough to build one that drives. It needs to go fast, far, be quick to fuel, and cheap to make. That might require investigating the fundamentals of battery technology.

Insofar as there's a difference between science and engineering, it's that scientists are making products you can't easily sell. Engineers are making business products. But scientists are still engineers in the sense that they're trying to build theories and explanations and concepts that they can "sell" to their research community.

In light of this, I might rename Elizabeth's three categories "trivia," "practice," and "innovation." Innovation builds on practice, and practice builds on trivia. Each has some key outcomes. Trivia lets you regurgitate facts and explanations. Practice lets you achieve a reliable, useful result using known tools and methods. Innovation lets you create something new, whether it's a theory, prediction, tool, or process.

One more point that may clarify things: I think you're lumping applying science to the problem of making things under science whereas to me that's the essence of engineering. That is, the scientist seeks to understand things for their own sake whereas the engineer asks "what can I build with this?" Again these are obviously idealized, extreme characterizations.

With respect to COVID, we ought to allocate some credit to engineering for building the high-throughput systems that enable rapid testing and experimentation. We also will require some impressive engineering to scale up vaccine production once we hopefully have a viable vaccine.

[-][anonymous]1y 3

Could these three categories be roughly summarized as "Know that", "Know how", and "Know why"?

I could trust other people’s answers, but credentials and authority have never looked more useless

I wonder whether less trust in authorities could make people more interested in LW-style rationality.

I can't find the link now, but a few people mentioned disappointment in authorities as an important step on their road to rationality. An impulse to figure out things on your own, because you can no longer trust that the experts will get everything right.