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