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Feeling like a speck in the wind amongst massive Societal systems Transcript: Speaker 1 I mean we have thousands of years of human history where you know since the agricultural revolution and the dawn of city-states it's just been constant change and one could argue that On a longish you know say century timescale we haven't been at equilibrium in 10,000 years what's next right how are all these nested feedback loops churning around between you know Societal structure and environmental structure to change the shape of society in the next couple hundred years Peter Turchin probably knows this better than I do but this is where I think thinking about these things at population scales rather than individual scales is it really helps me because when I think about things at the individual level like what can I do how do I live in the society right I find myself slightly distraught about like well I don't know I'm just a speck in the wind getting blown around by this maelstrom of society by trying To sort of think about the way the whole system is of all thing I can see it's not that I'm hurtling through space it's that we're all hurtling through space together in similar ways and That creates patterns that can then be identified what do you do with those patterns well then you know you get a professorship and you get to talk about it that helps sometimes

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

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

Explore v.s. Exploit: Finding Solutions Quickly Can Get You Stuck in a Local Optimum Transcript: Speaker 1 So when I started doing the work in AI, one of the really, very, very general ideas that comes across again and again in computer science is this idea of the explore, exploit trade on. And the idea is that you can't get a system that is simultaneously going to optimize for actually being able to do things effectively. That's the exploit part. And being able to figure out, search through all the possibilities. So let me try to describe it this way. I guess we're a podcast. So you're going to have to imagine this usually I wave my arms around a lot here. So imagine that you have some problem you want to solve or some hypothesis that you want to discover. And you can think about it as if there's a big box full of all the possible hypotheses and all the possible solutions to your problem or possible policies that you could have, for instance, Your reinforcement learning context. And now you're in a particular space in that box. That's what you know now. That's the hypotheses you have now. That's the policies you have now. Now what you want to do is get somewhere else. You want to be able to find a new idea, a new solution. And the question is how do you do that? And the idea is that there are actually two different kinds of strategies you could use. One of them is you could just search for solutions that are very similar to the ones you already have. And you could just make small changes in what you already think to accommodate new evidence or a new problem. And that has the advantage that you're going to be able to find a pretty good solution pretty quickly. But it has a disadvantage. And the disadvantage is that there might be a much better solution that's much further away in that high dimensional space. And any interesting space is going to be too large to just search completely systematically. You're always going to have to choose which kinds of possibilities you want to consider. So it could be that there's a really good solution, but it's much more different from where you currently are. And the trouble is that if you just do something like what's called hill climbing, you just look locally, you're likely to get stuck in what's called a local optimum.

Alison Gopnik on Child Development, Elderhood, Caregiving, and A.I.

COMPLEXITY: Physics of Life

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