speaker-0: So in the Bayesian framework, this is repeated observations, so you call them repeated ⁓ so you see a bunch of evidence, right? ⁓ and now when I say evidence, I'm not meaning a row of a CSV, right? So a row of a CSV is totally legit, that's that's data. What the Bayesian epistemologists meant is just you just see it. You just open your eyes and you just see evidence. But the question is, well, what evidence? So

speaker-1: So, that's ⁓ ⁓

speaker-0: What counts as evidence only counts in light of the theory. So you have to have your theory first, and then based on your theory, you can figure out what counts as evidence. But it it's a circularity or it's an infinite regress. If you're going to say that your theory comes from evidence, then that just doesn't make any sense. So there's one major issue, which is that when Bayesian epistemologists try to explain where Newton's theories come from. They start with a prior over H, and then you have your likelihood, evidence given H, but you've already ⁓ kicked a can down the road because the question is where does the pri where does H come from? And so H can't come from evidence. It has to come from some other process. So that's one problem with it. I think the other major problem with Bayesian epistemology is that it encourages you to find evidence in support of your hypothesis.

speaker-1: ⁓ ⁓ ⁓

speaker-0: Every astrologer on the planet, every conspiracy theorist on the planet looks to find evidence in support of their theory. ⁓ it's extremely easy to find evidence that supports your view that homeopathy is gonna cure your cold. It's extremely easy to find ⁓ further evidence that nine eleven was an inside job. If all you're looking for is evidence to support your preferred view, then you're gonna find a lot of that quite easily. What Popper did, and we can

speaker-1: Run.

speaker-0: Contan talking about Popper as long as possible, but he inevitably rears his rears his ⁓ his head when ⁓ this subject comes up. But Popper's whole point was that no, no, no, the scientific mind is not looking to find evidence in that confirms the hypothesis. It's always trying to find evidence to disconfirm your high your preferred hypothesis. And so I think to summarize, my main problems with Bayesian epistemology is that one, logically it just doesn't make any sense to

speaker-1: ⁓ ⁓

speaker-0: You can't explain where a hypothesis comes from by talking about evidence in support of it because the evidence only works given the hypothesis. And two, it produces dogmatism. It produces this this idea that you should only look for stuff that supports your your view. And three, which I haven't mentioned yet, but we can go into it, is that as soon as you start trying to patch the problem of induction with math on top of it, You're basically building an edifice on a logical impossibility, and then infinite numbers of paradoxes emerge. So one of the major paradoxes in Bayesian epistemology is something called ⁓ Hempel's paradox or the Raven Paradox, which some of your listeners may be familiar with this, but ⁓ is this idea that ⁓ if we're trying to find evidence in support of the theory that all ravens are black, all ravens are black. So that is equivalent to if a thing is a raven, then it is black. And as soon as you have a if then statement, you can take the contrapositive, which is if a thing is not black, then it's not a raven. And then that would logically it would be equivalent to ⁓ seeing a black raven. So the Hempel's paradox or the Raven paradox is just the idea that

speaker-1: ⁓ counter.

speaker-0: Any green shoe you look at or any orange ball or any was it, San Pelgrino cup that's not black would be in support of your hypothesis that all ravens are black. So how can you possibly claim to have knowledge and understanding about how ravens work by looking at green shoes? So Bayesian pistology, because it is so fundamentally subjective, because it really encourages you to go inwards and think about your beliefs. And it doesn't encourage you to go outwards and experiment. And it doesn't encourage you to go outwards and actively find information that goes against your beliefs and actually try to disconfirm your beliefs. I think it causes people to run in circles. I think it causes people to become extremely dogmatic, as Sam Bankman Fried I think is evidence of. ⁓ And I think it logically just doesn't make any sense. And so those are the main main reasons ⁓ why I

speaker-1: ⁓

speaker-0: ⁓ not a big fan of Bayesian pistology, but still I'm a huge fan of Bayesian statistics. And that line is something that I constantly have to walk in the other direction ⁓ with ⁓ like ⁓ our audience on the podcast, because some people who are extremely anti Bayesian now will wanna write off all of Bayesian stats too, and ⁓ have to be like, No, no, no, hold on. Bayesian statistics is great because you have data, because you can be falsified by your data and your and your models, but Bayesian pistology is less less good, shall we say.

speaker-1: some.