Join 📚 Quinn's Highlights
A batch of the best highlights from what Quinn's read, .
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
The Dataome: The Energy Intensity of the Digital World
Key takeaways:
• The generation and usage of digital data requires a significant amount of energy and resources.
• Silicon chip production is an energy-intensive process due to the creation of ordered structures from disordered material.
• Efforts to generate electric power for the current informational world are hindered by the fight against entropy.
• The energy requirements for computation, data storage, and data transmission are increasing exponentially.
• Without significant improvements in efficiency, the energy needed to run our digital data homes may soon match the global civilization's total energy usage.
Transcript:
Speaker 1
Its everything, right? It's this conversation in recording to yr bits. It's the information that went to and from your phone when you picked it up in the morning. It's the video you made. It's all the financial transactions, it's all the scientific computation. And that, of course, all takes energy. It takes the construction of te technology. In the first instance, making silican chips is an extraordinarily energy intensive thing, because you're making these exquisitely ordered structures out of very disordered material. And so there too, we go back to simo dynamics. And you're fighting, in this sense, against entropines. In a local fashion, we're having to generate electric to power current informational world, that piece of the data. And the rather sobering thing is that already, the amount of energy and resources that we're putting into this, it's about the same as the total metabolic utilization of around 700 Million human and if you look at the trend in energy requirements for computation, for data storage and data transmission, the trends all upwards. Its an expedential curve. And they suggest that perhaps, even if we have some improvements in efficiency, unless those improvements are then in a few decades time, we may be at a point where the amount of energy, Just electrical energy, required to run our digital data home, is roughly the same as the total amount of electrical energy we utilize as a global civilization at this time.
Speaker 3
The
Caleb Scharf on the Ascent of Information — Life in the Human Dataome
COMPLEXITY: Physics of Life
Have we overshot the scale at which humans can effectively coordinate?
Summary:
We need Jim Rutt to join the conversation to discuss whether we have exceeded our ability to coordinate effectively.
The slow progress of science and the population growth curve are related to this question. Sam Bowles and his work on behavioral engineering and the return of civil society are also important in this discussion.
We are currently witnessing a clash between institutions and individuals, and something has to give.
Transcript:
Speaker 3
We need Jim Rutt on this conversation right because ultimately this is about have we actually overshot the scale at which we can effectively coordinate and all these studies like you Know this I know it's controversial but like the slowed canonical progress of science these kinds of questions they seem related in a way to the sigmoidal curve of population growth. Have we risen above a level at which intelligibility can actually happen and if so where was that level. I mean I remember you know Sam Bowles is another person who has been looming large for me over this whole conversation not only for his work on the problems of viewing humans as agents That can be governed through behavioral engineering via incentive but also because of the paper that he wrote with Wendy Carlin the article he wrote in Vox EU in 2020 on the battle for The COVID-19 narrative which talked about the return of the civil society you know meaning that the Mesoscopic world of guilds and church groups and sports clubs and pubs and neighborhood Organizations mutual aid networks and all of these other human scale sub-done bar number structures that we found ourselves suddenly very much in need of and yet were eroded by the Radical success of both state power and market power in every way it feels like we are in a kind of clash of the titans right now we're like you know we watch institutions going up against Large institutions and people are struggling to remain unpolverized underfoot. At some point something has to give right.
Paul Smaldino & C. Thi Nguyen on Problems With Value Metrics & Governance at Scale
COMPLEXITY: Physics of Life
...catch up on these, and many more highlights
Readwise