📚 Quinn's Highlights

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To Eliminate Undesirable Behavior, You Have to Eliminate The Stimuli That Precedes It Summary: Self-control problems require structuring life to avoid stimuli that tempt bad behavior, similar to avoiding walking past a tavern if you're an alcoholic. Investors can improve mental hygiene by unfollowing negative sources and following those with a long-term perspective to reduce hyper reactivity to market fluctuations. Changing exposure helps in turning down the amplitude of emotions. Transcript: Speaker 1 But if you 're an alcoholic, you would be crazy to walk past the tavern and say, i will demonstrate the will power not to walk in. You can't do that, and you know you can't, so you walk on the other street. And that's the kind of governor that people need to put on their behavior. If you know that you have self control problems, you have to structure your life so that the things that tempt you into bad behavior don't get surfaced in your stimuli. And that's very easy for investors to do. If you, if you know you have a tendency toward hyper reactivity to, you know, red arrows pointing downward on stock market displays, then turn that web site off, un follow that person On twitter. Follow people who take a longer term perspective and aren't rattled by this kind of thing. Improve your mental hygiene. You can't turn yourself into someone who's unemotional, but you can turn down the amplitude of your own emotions if you change what your exposures are.

#4 Jason Zweig — Elevate Your Financial IQ

The Knowledge Project with Shane Parrish

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

The Danger of Incorrectly Mapping Between Scientific Measures and Truth Transcript: Speaker 1 And it's a problem when scientific culture tolerates too much ambiguity. There's always a caveat there, which is that at the early stage of theory development, sometimes you need ambiguity because you don't actually know really what you're talking about Yet. And so you need to allow for multiple interpretations to be possible until you can figure out what you mean. But a mature theory should be minimally ambiguous. This is at odds with things like metrics in terms of let's say how to evaluate something because people think, oh, well, it's scientific. Therefore, I want to use this to then therefore impose a value judge on something. It's better because it has a higher score on it. But that's not what science is actually able to do. Science can say, it has this score and it measures this thing because what it measures is this. If you say what it measures is this, and therefore it means this other thing, that's a problem because that's a false mapping. And it's not really about ambiguity versus precision. It's about, I think, the imprecision of the mapping between the measure and the term. So if you want to measure something like happiness or economic prosperity, you can say, well, we'll measure the genie coefficient, we'll measure GDP. But those are rigorous, clearly unambiguous measures. They have a meaning. This is what they are. This is how we measure them. We can compare things on this measure. And that's not problematic until you then say, and it is better to have a higher GDP full stop.

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

COMPLEXITY: Physics of Life

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