Covid-19, the Evil Genius, and How to Think about Societal Risks

Interesting article connecting ideas about how we assess risk, the language we use, and how we interpret data depending on how it is presented to us. This connects well to the conflict between emotion and reason in decision making but also our inability to think probabilistically.

If you ask someone if they would be willing to sacrifice 100K people to avoid this intervention, people will be inclined to say no, “I would never put a price tag on human life, much less 100K human lives.” But let’s say you ask the question differently: “Would you be willing to accept a one in three thousand chance of dying this year to avoid this public health intervention?”… Once you ask the question this way then instead of focusing on the raw number of deaths, we can focus on the tradeoffs,

https://reason.com/2020/03/26/covid-19-the-evil-genius-and-how-to-think-about-societal-risks/#comments

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Here are some additional materials related to how we think about numbers and risk, etc. from my Maths page

Here is the article that accompanies the handout above.

Some different questions that get at the same point.

Coronavirus ‘Hits All the Hot Buttons’ for How We Misjudge Risk

When you encounter a potential risk, your brain does a quick search for past experiences with it. If it can easily pull up multiple alarming memories, then your brain concludes the danger is high. But it often fails to assess whether those memories are truly representative.

A classic example is airplane crashes.

If two happen in quick succession, flying suddenly feels scarier — even if your conscious mind knows that those crashes are a statistical aberration with little bearing on the safety of your next flight.

All numbers are made up, some are useful: Keeping track of stuff is hard

All the data that we trust and believe on a daily basis, is only accurate in a specific context, at a specific time, and at a specific level. If you dig deep enough, ultimately all of the data in the world that drives major and minor decisions alike is built on wobbly foundations.

https://vicki.substack.com/p/all-numbers-are-made-up-some-are

Why you might be counting in the wrong language

Learning numbers in a European language has probably affected your early maths ability. It turns out there are better ways to count.

So even though we might all be using the same numbers, the words we use may influence how we think about them. They say maths is a universal language, but perhaps that’s not true after all.

https://www.bbc.com/future/article/20191121-why-you-might-be-counting-in-the-wrong-language

Art and reality: How accurately does “Euphoria” portray real teens’ lives? Does it matter?

The central point here is that the show Euphoria inaccurately portrays teenagers’ lives which raises the question: Is there a responsibility that comes with creating artwork? Must it be accurate? Who decides?

The claim that the show is inaccurate is backup with statistics raises the question: How can math/statistics help us acquire knowledge? (or understand reality?)

People’s perceptions of teens’ behaviors seems to be generally inaccurate beyond what this show. If presented with this article and appropriate statistics would people change their mind or perceptions of these issues? I’m not sure that it would which leads us to the question: What is the role of intuition in acquiring knowledge? Can mathematical knowledge overcome intuitive beliefs?

This reminded me of an earlier article from the New York Times:

“The Kids Are More Than All Right”

https://well.blogs.nytimes.com/2012/02/02/the-kids-are-more-than-all-right/

Where Proof, Evidence and Imagination Intersect

Screen Shot 2019-03-23 at 8.17.54 PM

In mathematics, where proofs are everything, evidence is important too. But evidence is only as good as the model, and modeling can be dangerous business. So how much evidence is enough?

Those mathematicians know to be cautious when working with their models. Because they know that no matter how useful and interesting their model, no matter how compelling the evidence they collect, there might be something out there about elliptic curves that they didn’t quite imagine. And if you can’t imagine it, your model can’t capture it, and that means the evidence won’t reflect it.

https://www.quantamagazine.org/where-proof-evidence-and-imagination-intersect-in-math-20190314/

A Songwriting Mystery Solved: Math Proves John Lennon Wrote ‘In My Life’

Mathematics professor Jason Brown spent 10 years working with statistics to solve the magical mystery. Brown’s the findings were presented on Aug. 1 at the Joint Statistical Meeting in a presentation called “Assessing Authorship of Beatles Songs from Musical Content: Bayesian Classification Modeling from Bags-Of-Words Representations.”

https://www.npr.org/2018/08/11/637468053/a-songwriting-mystery-solved-math-proves-john-lennon-wrote-in-my-life