Hello everyone, welcome to the Mindscape Podcast. I'm your host Sean Carroll.

Obviously on a podcast like mine, we're often going to be talking to scientists,

various sorts, theoretical scientists in particular. Some of my favorites, not all of them,

but a lot of times we're talking about the ideas for how we might explain some scientific phenomenon.

Right? Coming up with a new theory, whether it's a dark matter or evolutionary biology or whatever

it's going to be, you might remember in fact that I had Lee Smolin on the podcast very recently

and despite the fact that we work in very similar areas and we're personally very friendly,

we have different ideas about how to go about building the better next generation scientific theories.

Why is that? How can two scientists who are both more or less trained in the same way come up

with very different preferences when it comes to building new explanations? That's what we're

on about today on today's podcast with Simon Didayo. Simon actually started as a theoretical

cosmologist, much like myself, but he switched into some combination of statistics and data-driven

social science and cognitive science. So it's a wonderfully difficult specialty to pin down,

but he's both at Carnegie Mellon University and also the Santa Fe Institute. So we overlap a lot

in our intellectual interests and what Simon talks about in the paper that we're going to be

discussing today is how explanations work and honestly explanations in this sense is kind of a

synonym for a theoretical viewpoint or formalism to answer some kind of question, right? So you have

some phenomenon whether it's my car broke down or there's dark matter in the universe and you want

to explain it. Now what happens is you can invent an explanation and different people will prefer

different kinds of explanations for different reasons. So what Simon and his collaborators have

done is to break down the different parts of Bayesian analysis that go into making a good explanation

and sort of quantify different preferences or different values you may have for choosing your

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