📚 Quinn's Highlights

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A batch of the best highlights from what Quinn's read, .

Ambiguity in Communication is Both a Feature and a Bug Summary: In 1984, Eisenberg proposed that ambiguity in communication is important and influential. This idea suggests that being too clear can limit interpretation and hinder coalition-building. Ambiguity can be used to evade accountability, but it is also a general principle of communication. Transcript: Speaker 1 It's Eisenberg in 1984 in communication monographs or something. It's this great rambling paper and this idea has been massively influential to me, but he's basically arguing that it would seem like the point of communication should be clarity, To be as clear as possible. For me to say, I mean this and you do know exactly what I mean and that's the goal and ambiguity is therefore a bad thing. He argues that actually no ambiguity is a really important thing and other people have expanded on this. Now the way I think about this is like a blend of Eisenberg and then other people who've come a bit later, but that in a lot of ways if you're trying to get let's say a coalition, you don't Want to say this is exactly what our goal is and this is what we're trying to do. You want to use vague terms so that a bunch of people can sort of map whatever they think that the goal is onto and say that's consistent. It also leads to a reduction in accountability because after you do something and someone says, you said you were going to do this and you say, nah-ah listen to what I said, it's consistent With what I did because what I said was ambiguous. So it's pernicious in a way too. It's used nefariously in a lot of ways by let's say politicians and other kinds of leaders to avoid accountability, but it's also just a general principle of communication I think.

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

Why Bigger Animals Live Longer: The Relationship between Size, Energy, and Longevity Summary: The larger an animal is, the more efficient it becomes in terms of energy consumption. This is because the self-similar fractal structure of larger animals allows them to save energy. Bigger animals require less energy proportionally to run their bodies due to the massive amount of tissue per gram or per cell. As a result, bigger animals experience less wear and tear and live longer than smaller animals. The reason for less wear and tear is that bigger animals use less energy and create less damage, reducing entropy. This principle can also be observed in machines, where those subjected to less stress and driven at lower revs per minute tend to last longer. Transcript: Speaker 2 So that's why we don't need to double our metabolism when we double our weight. It's that fractal like self similarity that allows us to get these essentially efficient savings in the amount of energy we need. So it's better to be bigger, isn't it? Because you don't need as much energy proportionally to run yourself. Correct. Speaker 1 So you need massive tissue per gram of tissue or per cell. You need less energy, the bigger you are. And by the way, this has huge consequences throughout all aspects of biology and life. And maybe one just to tie it back to the beginning of this discussion where we started out by talking about aging and mortality. This means that the bigger you are, the less hard your cell is working. The bigger you are, there's less wear and tear the longer you live systematically. So this is the origin of why bigger things live longer than smaller things. Speaker 2 And why is there less wear and tear if you're bigger? Speaker 1 You're using less energy and creating less entropy. That is you're creating less damage the bigger you are because simply you're using much less energy if you have an engine, an automobile and you insist on racing it at 10,000 revs per Minute every time you drive it, I can assure you that car will not live as long as a car that's driven by a little old lady or a little old man like me who keeps the revs at about two or three Thousand revs per minute. So you know, cars and machines last much longer, the less stress you put on them.

Scaling 2 — You and I Are Fractals

Simplifying Complexity

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