Here is some non-basketball content I read or listened to this week that I found interesting:
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Why It’s So Freaking Hard To Make A Good COVID-19 Model - “Numbers aren’t facts. They’re the result of a lot of subjective choices that have to be documented transparently and in detail before you can even begin to consider treating the output as fact. How data is gathered — and whether it is gathered the same way each time — matters.”
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Don’t Believe the COVID-19 Models
- “Here’s the tricky part: When an epidemiological model is believed and acted on, it can look like it was false. These models are not snapshots of the future. They always describe a range of possibilities—and those possibilities are highly sensitive to our actions.”
- “Modeling an exponential process necessarily produces a wide range of outcomes. In the case of COVID-19, that’s because the spread of the disease depends on exactly when you stop cases from doubling. Even a few days can make an enormous difference.”
- " With COVID-19 models, we have one simple, urgent goal: to ignore all the optimistic branches and that thick trunk in the middle representing the most likely outcomes. Instead, we need to focus on the branches representing the worst outcomes, and prune them with all our might."
- “At the beginning of a pandemic, we have the disadvantage of higher uncertainty, but the advantage of being early: The costs of our actions are lower because the disease is less widespread. As we prune the tree of the terrible, unthinkable branches, we are not just choosing a path; we are shaping the underlying parameters themselves, because the parameters themselves are not fixed. If our hospitals are not overrun, we will have fewer deaths and thus a lower fatality rate. That’s why we shouldn’t get bogged down in litigating a model’s numbers. Instead we should focus on the parameters we can change, and change them.”
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Super Pumped - The story of the rise and fall of Uber, and their former CEO Travis Kalanick.