📚 Quinn's Highlights

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The Danger of Incorrectly Mapping Between Scientific Measures and Truth Transcript: Speaker 1 And it's a problem when scientific culture tolerates too much ambiguity. There's always a caveat there, which is that at the early stage of theory development, sometimes you need ambiguity because you don't actually know really what you're talking about Yet. And so you need to allow for multiple interpretations to be possible until you can figure out what you mean. But a mature theory should be minimally ambiguous. This is at odds with things like metrics in terms of let's say how to evaluate something because people think, oh, well, it's scientific. Therefore, I want to use this to then therefore impose a value judge on something. It's better because it has a higher score on it. But that's not what science is actually able to do. Science can say, it has this score and it measures this thing because what it measures is this. If you say what it measures is this, and therefore it means this other thing, that's a problem because that's a false mapping. And it's not really about ambiguity versus precision. It's about, I think, the imprecision of the mapping between the measure and the term. So if you want to measure something like happiness or economic prosperity, you can say, well, we'll measure the genie coefficient, we'll measure GDP. But those are rigorous, clearly unambiguous measures. They have a meaning. This is what they are. This is how we measure them. We can compare things on this measure. And that's not problematic until you then say, and it is better to have a higher GDP full stop.

Paul Smaldino & C. Thi Nguyen on Problems With Value Metrics & Governance at Scale

COMPLEXITY: Physics of Life

Organizational Entropy: the tendency for artifacts you produce to start rotting immediately Summary: Any artifact produced within an organization immediately begins to deteriorate, much like a new car losing its value. Once published, such as a memo, it begins to become outdated. Entropy, in this context, always increases, requiring continual input of energy to prevent deterioration. This demands the creation of reinforcement mechanisms to ensure that all content remains current and functional, such as periodically checking and updating a database of memos. Transcript: Speaker 1 Organizational entropy, which is any artifact that you produce immediately starts rotting the moment that you have created it. Speaker 2 It's like driving a new car after a lot. Speaker 1 Yeah, the moment that anything is published in the company, you write a memo, it is already rotting. It is already going to be out of date. And so the concept of entropy is it is always increasing. And so the only way to keep entropy at bay is you have to add more energy into the system. So you have to create reinforcement mechanisms for any piece of content that you have. If you have a database of all your memos, you have to check them every once in a while to make sure they're up to date. You need to create more energy always has to go in in order to keep things fresh and functional.

#694 — Sam Corcos, Co-Founder of Levels — The Ultimate Guide to Virtual Assistants, 10x Delegation, and Winning Freedom by Letting Go

The Tim Ferriss Show

How Measurability/Mathematical Bias Limits the Scope of Scientific Inquiry and Human Discovery Transcript: Speaker 1 So there's this old paper from the, I think, 1960s by Eugene Vigner, the Nobel Prize physicist. It's called something like, on the unreasonable effectiveness of mathematics. The fun paper, and he's like, there's no good reason why mathematics should work as well as it does. And there's no good reason why there should be a tool that allows humans to predict things as well as math does. There's no good reason. It's kind of nuts. And we should all just be grateful. And he says some other things, but he's basically just kind of being all about how great mathematics is and how there's no good reason why it should be. And it's pretty cool that it does work so well. I think that there's a counter to that, which is that not everything is that easily described that mathematics. And there's lots of things for which mathematics is not that effective at describing. And it's actually just the things that were well described or easily described by mathematics are the things that were discovered using mathematical tools. They're the things that lend themselves that were amenable to mathematical inquiry. And a lot of the things that we're interested in terms of social science and cognitive science and the related philosophical inquiry are things that are much less tangible in terms Of this kind of specification. And you can see it like in a physics equation, right, a physical theory, whether it's about mass or electricity or something else, right, you have a theory about how things work. And then you can write out equations. And all the terms in the equations have units. And they are all directly related to the things that are measurable. The theories are directly about relationships between things that are measured. And in social theories and cognitive theories, so often our theories are about relating constructs. And then we have proxy measurements, but the theory isn't about the relationship between the proxy measures. The theory is about the constructs and the relationships between the constructs that are social in nature, that are cognitive in nature, but aren't the things that are being measured. And so there's this gap. And I don't know the extent to which that gap can be overcome.

Paul Smaldino & C. Thi Nguyen on Problems With Value Metrics & Governance at Scale

COMPLEXITY: Physics of Life

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