📚 Quinn's Highlights

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

People's Understanding of Others' Lives Is Biased Based on the Structure of Their Social Network Transcript: Speaker 1 So there's something in that that I found really interesting about this social sampling, which is that as you mentioned, like if you happen to be worse off and everyone else is worse Off, as is the case with like income, for example, then being worse off, you're going to project your bias into that general population more accurately than if you're better off in some Situation for which the most of the population is worse off. And that these biases are not all created equal. Yes. It has to do with how they stand relative to the broader population. So what we show is that this kind of biases of judgments of the broader population can be explained by the structure of social network and not by some cognitive deficit or motivational, Motivational bias, some desire to be better than others or that or some idea that everybody's like me or some cognitive deficit that people cannot, that people are too stupid to understand How other people live. It's really determined by the context of memory, that by the content of one's memory, which comes from one social circle.

Mirta Galesic on Social Learning & Decision-Making

COMPLEXITY: Physics of Life

Left unchecked, your team members move from one task to the next, doing the easiest things, the things someone asked them to do, or simply the things right in front of them. Especially as stress increases, prioritization effectiveness declines. In one study of 43,000 encounters of doctors and patients, researchers found that when the workload was heaviest, physicians prioritized their easiest cases, leaving the most severe cases to wait the longest – a tendency known as “completion bias” (Gino and Staats 2016). Among all professions, it can be easy to get sucked into an endless stream of activities that feel like progress but that leave tomorrow looking much like yesterday.

The Leader Lab

Tania Luna and LeeAnn Renninger

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

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