📚 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

The need for transparent and democratic decision-making: Human bullshit and algorithmic bullshit are two sides of the same coin Summary: Data and algorithms are not inherently bad, but they should be used in a transparent and democratic way that empowers everyone. Instead of arguing about whether computer or human decision-making is better, we should focus on accountable and transparent decision-making. This means avoiding human biases and stereotypes as well as naive machine learning without considering its real-world implications. Transcript: Speaker 1 So the point is that it's not that data and algorithms are bad it's that they need to be applied in a way which is transparent and which is democratic and which empowers all of us to carry On these debates rather than simply being tools which accurately or inaccurately are being used to buy the powerful to control the rest of us it's silly to argue about which is better You know computer decision making or human decision making that's really not the point I mean the point is we should have accountable transparent decision making instead of bs there's Human bs which comes in the form of stereotypes in ideology and there's algorithmic bs which comes in the form of naive machine learning without thinking enough about its applications

Glen Weyl & Cris Moore on Plurality, Governance, and Decentralized Society

COMPLEXITY: Physics of Life

Two Models of Searching for Truth: Unearthing the Truth v.s. Growing Into The Truth Summary: Science is like carving away everything that isn't truth, but I think it's more like an infinite vacuum with trees growing in all directions. The search for truth is complex and ever-expanding. It's like ecology, where species have multiple solutions to a problem, which continually changes. I believe in infinite diversity and combinations, and that complexity can emerge from simplicity. Instead of focusing on the core, we should expect to branch out. Transcript: Speaker 2 One metaphor I like is that I think some people have as their image of science. Imagine we're sitting on the surface of a sphere, and they think they're kind of digging down to the core of the truth. They're discarding the earth beneath them, the falsities, and they're going to hit the truth. Speaker 1 We're carving away everything that isn't science, you're saying? Speaker 2 Yeah. And I think that the image I have instead is there's an infinite vacuum outside of that sphere, and there are trees growing out from the surface of the sphere in all directions. And as they grow out, more space is available, and they branch and expand. And that just goes on, and it gets more and more complex the further you get out. And that's kind of how I think of the search for the truth. That strikes people maybe initially is a little bit weird. I guess that's how I interpret like beginning of infinity, David Deutsches' phrase. But another way to see that is ecology, the way the species were. Species are all after some abstracted fitness landscape, I guess is one way to conceive of it. But somehow we don't end up with one solution to that problem. In fact, we get a bunch of solutions to the problem, and as that problem gets solved, it actually changes the problem, because now for all the other species you've got to deal with, and There's other species that you can eat, there's all kinds of stuff going on. That's how I think about it. I eat reflecting infinite diversity and infinite combinations. I think that there's just a lot of things going on, and you can build a lot of complexity from a small set of ingredients. And you shouldn't expect to get down to the core, you should expect to branch out from the core.

Glen Weyl & Cris Moore on Plurality, Governance, and Decentralized Society

COMPLEXITY: Physics of Life

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