📚 Quinn's Highlights

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The Problem of Scale Clash in Human Collaboration Summary: The problem goes beyond ideal scale of humanity. Different things we want involve different scales. Science works on a huge scale for problems like climate change while other things work on medium or small scales. There is a clash of different scales and no optimal scale. The big scales tend to win and squash out the small scales. However, over long time scales, these complex systems tend to implode. It's about a dynamic balance where different forces coexist. How do we handle this in light of global coordination, bioregional organization, and personal relationships at the neighborhood level? Transcript: Speaker 2 I think the problem is even worse than what you're describing I'm going to try to pessimize what you said I mean when you ask me a question like have we gone past the ideal scale of humanity That implies that there is an ideal scale that we could plausibly hit if we could somehow convince people to scale back. For me the real worry is there's no ideal scale of humanity because different things we want to be involved in demand different scales science works really big good on a huge scale solving Problems like climate change our massive scale problems that everyone has to get together on and then there are other things that work at medium or small scales and there's just this Unsolvable scale clash my real worry is that different parts of us and our needs call us to different scales and there is not an optimal scale and so I have to participate in these different Scales or in tension with each other and also the big scales tend to win because they get really powerful and so they squash out the small scales. Speaker 3 Over short time scales though right because over long time scales those like you know this is the Bob May will a complex system large complex system be stable question it's like at some Point those things tend to implode so it's not about like an equilibrium so much as it is about a a dynamic balance or a zone at which these different forces are able to coexist how do you Deal with all of this in light of both the need for global coordination and bioregional organization and neighborhood level personal relationships etc.

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

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

People have more accurate models of people in close proximity than they do of people far away (socially) Summary: People have a good understanding of their friends and are accurate in predicting their behavior. This is shown by their ability to accurately predict election results based on their friends' voting preferences. However, biases arise when people are asked to judge unfamiliar populations. These biases can be attributed to the structure of their personal social networks. The more biased their social networks are, the more biased their estimates of the general population will be. Transcript: Speaker 1 Oh yeah, after seven years of research on this paper, that people actually have a quite a good idea about their friends, family, acquaintances, people that they meet on every day basis And then we'd whom they need to cooperate with, learn from or avoid. And that they're actually not that not as biased as a traditional social psychology would like us to think. And we see that because when we ask people about their friends, we see that this predicts societal trends quite well. So in one line of research, we asked a national probabilistic sample of people to tell us who their friends are going to vote for. We average those things across the national sample and got better prediction of election results than when we asked people about their own behavior. And this would not have happened if people were biased in reporting their friends. They must have told us something that must have given us information that's accurate and that's goes beyond their own behavior in order for that to happen to predict the elections better. And by now we saw that in four further, so we five elections all together in the US 2016 in France, the Netherlands, the Sweden and US 2018, and we hope to predict again 2020. So things like that tell us that people are actually pretty good in understanding their social circles and then the apparent biases show up when people are asked to judge people that They don't know so well. So when I'm asked to tell you something about people in another state or another country or people from another socioeconomic cluster, which I don't know well, then I am likely to have Some biases. But these biases we show can be explained by what I know about my friends. So if you ask me something like that, I will really try to answer your question honestly. And to do that, I will try to recall from my memory everything that I know about our social my social world. But you know, if I'm surrounded by rich people like here on the East side of Santa Fe, it could be very difficult to imagine in what poverty people can live in other parts. And so even if I'm trying my best to recall, you know, the most poor person I know, I might never recall such poverty that actually exists in the world. And when asked about the overall level of income in the US, I'm likely to overestimate the overall level. And similarly, if you are poor, you're people who are poor might have problems imagining the wealth of really rich people and they will typically underestimate the wealth of the country. So okay, so let me let me summarize this. So this piece actually suggests that people are not that biased when it comes to judging their immediate friends. They have a lot of useful information about their friends and pretty accurate. The bias is show up when people are asked about other populations that they don't know so well. And they can be mostly explained by the structure of their own personal social networks. The more biased your social networks are, the more biased your estimates will be about the general population.

Mirta Galesic on Social Learning & Decision-Making

COMPLEXITY: Physics of Life

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