Hello, everyone. Welcome to the Mindscape Podcast. I'm your host, Sean Carroll, and you know, economics is always tough for me to cover here on the podcast.

I love it. I think it's fascinating. It's also kind of intimidating and difficult.

I don't think that there's any other field which is simultaneously as quantitatively rigorous and deeply involved with the complexities of social life as economics is.

It's a story of theorems and results, but also one that is supposed to apply to the real world.

And as we all know, economic theory has both successes and kind of dramatic failures.

Happily, today's guest, Samuel Boles, is super easy to talk to. I don't do that much talking in this particular podcast because Sam is so good at explaining what he's thinking about.

And it's very, very interesting stuff about how to think about the role of human beings within economic systems, but also going backwards, what thinking economically can teach us about human beings, how human beings actually behave, not just the simple toy models of very oversimplified economic models.

Now, Sam has been a long time collaborator of previous mindscape guest, Herb Guinness, sadly Herb passed away back in January at the age of 82, but he and Sam have been working together since their graduate school years in the 1960s.

It's a fascinating story. I have the impression that if Sam were here to tell you his biography, it would last many, many hours and be super fascinating. He's led quite a colorful life.

But one part of that story is that Sam and Herb Guinness were asked by Martin Luther King to help him study up on economics to ask a bunch of questions about economics and inequality in 1968, just before the poor people's march just before Martin Luther King was assassinated.

And apparently the story goes that the two of them, Sam and Herb, were kind of chagrined that their expert training in economics, I think they were still grad students at the time, was completely inadequate to answer these questions about inequality.

So they really started thinking deeply about these questions that were being ignored by mainstream economics, and they both taught at Harvard for a little while before being stolen away by UMass Amherst, who hired a group of people.

There was this famous radical group of economists that UMass Amherst hired, and they worked together for a long time.

Herb and Sam wrote a quite well-known book called Schooling in Capitalist America about how the educational system interacts with and serves our capitalist system of economics.

And certainly both of them, Sam included, are identified as members of the left wing of economic thought, but as you will see, as you will hear anyway, in the podcast, Sam is scrupulously fair.

When he describes the contributions and the shortcomings of different aspects of economics, he is very, very quick to give credit to the free market, to Adam Smith, even to sort of 20th century neoliberal economics, but he thinks that we can do better, he thinks we can go further and we'll talk about that.

But also, this particular conversation is not very political, to be honest. We're really thinking about how to analyze systems economically, what we learn about human beings by doing experiments that are informed by economics, by thinking about game theory and interactions, why are human beings as cooperative and altruistic as they sometimes are.

In fact, a much more recent book, maybe the last book that Sam and Herb wrote together is called a cooperative species, human reciprocity and its evolution.

And there's a very short motto for the lesson of this book that you will have to listen to the podcast to hear about.

But you know, we human beings were complicated. Sometimes we're kind of selfish, self-interested as Adam Smith assumed for the purposes of his models. By the way, as Sam points out, Adam Smith didn't think we were always perfectly self-interested, just that's a good starting point for the model.

Sometimes though, human beings are kind of nice to each other. Sometimes, how do you fit that into the model? What does that have to say? How can that help us?

How can we use these insights to tackle some of the global large-scale problems we have today? All that and more in this episode, so let's go!

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