And this feels very natural, and just like what we would do by default, right? The reason that I think the concept of thinking in grayscale is important, is that we don’t apply that framework reliably or systematically to all of our beliefs. But somewhere in between, and that level fluctuates. So you’re not 0% sure or 100% sure that you got the job. And that might lower your confidence that you got the job. Then you might also find out that they had an unusually large number of applicants this year. And so, that might boost your confidence that you got the job. Like let’s say you get a new piece of information that – oh – turns out they were really looking for people with the degree I have. Like if you apply for a job, and you’re waiting to hear back about the job – you get new pieces of information that might shift your confidence, that you did in fact get the job. Because we do apply that – we do use that framework for a lot of beliefs. Julia: Well, so I think everyone already has some intuitive understanding of beliefs being grayscale. And you’ve said before – in your videos on YouTube that it made you realize that your beliefs– The theorem made you realize that your beliefs are grayscale. But it’s a way to think about what happens when new information comes along and affects an existing idea or belief or probability. You can work out the math, or you can even draw it out on a piece of paper and graph it. So it’s – but the purpose of it is – it’s a way of– And then that equals out a certain probability. It’s a little, little division that – 2 things multiplied together over another thing. It’s one of those math formulas that you look at and go, “No thank you.” But it’s not actually a very complicated math formula. And when you first look at it, it seems that way. But about how much your belief, how much your degree of confidence in some idea should change as you encounter new evidence that’s relevant to that belief.ĭavid: Right and this sounds extremely complicated. You could rephrase the rule as saying how much – as being about – not just an abstract probability. And if you want to make the small or large leap – depending on who you ask – from that theorem to a way of approaching a rule. Julia: So Bayes’ rule, or Bayes’ Theorem is just a simple theorem from probability that tells you how a probability should change in response to new evidence. So first, people who have never ever, ever heard of what this is. I was like, “Oh God, this is extremely useful.” And very powerful. I – like you, found that this was like the– This was the keystone that like made everything else make sense to me. She is also the host of the Rationally Speaking Podcast and has written for Slate, Science, Scientific American, and Popular Science.ĭavid: This is going to come up, and people are going to sort of just drop it casually in conversation sometimes. Julia Galef is the president and co-founder of the Center for Applied Rationality, a non-profit organization that training people and organization to make better decisions. This is the interview with Julia Galef from episode 073 of the You Are Not So Smart Podcast.ĭownload – iTunes – Stitcher – RSS – Soundcloud
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