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Speaker 1: This is Master's in Business with Barry rid Holds on

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Speaker 1: Bloomberg Radio.

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Speaker 2: This week on the podcast What Can I Say? Cass

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Speaker 2: Sunstein is an intellectual force in American jurisprudence, law, behavioral finance,

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Speaker 2: public policy. I don't even know where else to go.

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Speaker 2: What a fascinating career and really incredibly interesting person. I

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Speaker 2: guess life is easy when you're co authors are all

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Speaker 2: Nobel laureates or George Lucas. He's just he's just done

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Speaker 2: so many amazing things in a career that spans everywhere

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Speaker 2: from the Supreme Court to the Chicago School of Business

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Speaker 2: and the Chicago School of Law, Harvard Law School, and

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Speaker 2: just multiple public policy positions, public service positions for the

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Speaker 2: White House, for the Attorney General's Office, for the Pentagon.

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Speaker 2: I mean, his influence is just so far reaching and

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Speaker 2: fascinating you kind of forget that he also teaches law

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Speaker 2: at Harvard. I found this conversation to be delightful, entrancing,

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Speaker 2: and fascinating, and I think you will also, with no

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Speaker 2: further ado, my sit down with Harvard Laws Cass Sunstein.

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Speaker 2: Cass Sunstein, Welcome to Bloomberg.

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Speaker 1: Thank you a great pleasure to be here.

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Speaker 2: Thank you so much for joining us. So you co

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Speaker 2: author two books with two Nobel laureates and you practically

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Speaker 2: write a third one with George Lucas. How much fun

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Speaker 2: is that?

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Speaker 1: Well, I'd say it was amazing. Writing on Star Wars

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Speaker 1: was crazy fun, yeah, and also a very unlikely thing

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Speaker 1: for a law professor to do. Writing a book with

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Speaker 1: Dick Taylor was not crazy fun, but was really fun

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Speaker 1: because he's fun.

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Speaker 2: There's nobody in the world of economics or behavioral finance

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Speaker 2: like Dick Taylor. He's one of my favorite people.

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Speaker 1: Agreed, he's unique, and writing with him was a joy

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Speaker 1: and a laugh a minute. Writing with Danny Kahneman was astonishing.

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Speaker 1: He's the most creative person I've ever met. He's also

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Speaker 1: immensely self critical. He's almost as critical of his co

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Speaker 1: authors as he is of himself. And it was a

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Speaker 1: roller coaster and an incredible learning experience, and his integrity

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Speaker 1: and sense of we can do better kept me up

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Speaker 1: most nights.

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Speaker 2: He supposedly agonizes over every word, every sentence. There. Nothing

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Speaker 2: gets published without being looked over nine ways from something.

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Speaker 1: That understates that. So you got an email maybe at

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Speaker 1: four in the morning saying this chapter is horrible. I

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Speaker 1: don't know how we could have written it. The whole

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Speaker 1: book is horrible. I don't know why we decided to

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Speaker 1: write it. And then two hours later he'd say, I

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Speaker 1: see the fundamental flaw and we have to give up.

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Speaker 1: And then an hour later, maybe four forty five in

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Speaker 1: the morning, he'd say, I might have a way to

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Speaker 1: correct the fundamental flaw, but I don't think so. And

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Speaker 1: then at five point fifteen in the morning, I'll send

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Speaker 1: you a note saying I have a glimmer of an insight.

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Speaker 1: It's probably going to fail, but I'm going to try it.

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Speaker 1: And then at five point forty five in the morning

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Speaker 1: he says, I have a new draft of the entire

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Speaker 1: chapter which was a catastrophe, and I'm sure this is

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Speaker 1: very bad too, but it's less catastrophic.

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Speaker 2: That sounds like. Just skip to the last one and

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Speaker 2: read that. So we'll get into a lot of your

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Speaker 2: writings a little later, but before I want to dive

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Speaker 2: into your background. You graduate Harvard with a BA in

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Speaker 2: seventy five, Harvard Law School in seventy eight. I assume

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Speaker 2: the nineteen seventies were very different than the eighties and nineties,

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Speaker 2: when so many people at places like Harvard Law. I

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Speaker 2: wanted to go to Wall Street. What was that era

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Speaker 2: like at an Ivy League law school.

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Speaker 1: It was the aftermath of the nineteen sixties, so it

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Speaker 1: was later than all the Civil rights and Vietnam stuff,

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Speaker 1: but it was like a wave that was starting to recede,

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Speaker 1: but extremely visible. So there were people who wanted to

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Speaker 1: have great careers and whatever they could find. There were

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Speaker 1: people who thought, I want to make the world better.

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Speaker 1: There are people who thought, I'm kind of sick of

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Speaker 1: people who want to make the world better. I don't

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Speaker 1: want to be like that. And there were different categories

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Speaker 1: of types. There was a lot of intensity. There was

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Speaker 1: a sense that our country had been through something very

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Speaker 1: traumatic and thrilling, and the question is in what direction

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Speaker 1: were we going to go? It was pre Reagan era,

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Speaker 1: and you could kind of see the dawn of the

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Speaker 1: Reagan era and some of my classmates, and you could

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Speaker 1: see even the dawn of some of the woke stuff

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Speaker 1: today and some of my classmates critical race theory kind

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Speaker 1: of about to be born, and you could see the

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Speaker 1: origins of it there as well as you could see

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Speaker 1: the Federal Society, which is the conservative movement that's had

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Speaker 1: an amazing influence that the theoretical foundations were kind of

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Speaker 1: being laid by twenty somethings in the seventies.

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Speaker 2: Interesting, So you clerk for Justice Benjamin Caplan on the

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Speaker 2: Massachusetts Supreme Court and then clerk for Justice Thurgood Marshal

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Speaker 2: at the Supreme Court of the United States, and this

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Speaker 2: is seventy nine eighty. Tell us a little bit about

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Speaker 2: what those experiences were like.

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Speaker 1: Well, Justice Kaplan on the Massachusetts Supreme Court, he's not

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Speaker 1: in the history books, but he could be. He was

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Speaker 1: a person who was fair and rigorous, and it's almost

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Speaker 1: like there's one word for Caplin, fair and rigorous. And

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Speaker 1: he was a little like Daddy Kahneman in the sense

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Speaker 1: that he'd obsessed over every word. He also was very

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Speaker 1: critical of himself, and he could be very critical of

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Speaker 1: his clerks. At one point I was told before I started,

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Speaker 1: he's going to take you in the equivalent of woodshed,

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Speaker 1: and kind of threatened to fire you. And sure enough

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Speaker 1: that happened, and I reacted with fire. I said to him,

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Speaker 1: this is unfair. And it was quite an encounter. And

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Speaker 1: the next day said are you still mad at me?

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Speaker 1: Which was a recognition of my humanity, and I said,

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Speaker 1: I still think you were unfair, and we became great friends,

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Speaker 1: and I learned so much from him. He had been

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Speaker 1: a Harvard professor, maybe the best Harvard professor of his generation,

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Speaker 1: and he was an extraordinary judge. Marshall was the historic

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Speaker 1: person and larger than life, and full of humor and

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Speaker 1: wit and moral commitment that was never drawing attention to itself.

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Speaker 1: It was more about the people. It was never about himself.

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Speaker 1: And what I learned from Marshall is where lawyers typically

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Speaker 1: at least of the supreme level, focus on paper and think,

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Speaker 1: you know it was the argument solid is the other

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Speaker 1: paper better papers, which lawyer has the better of, the

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Speaker 1: argument on precedence, on statutes. Marshall, of course thought about

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Speaker 1: all of those things, but he saw behind the paper

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Speaker 1: people and that was an enduring lesson for me, that

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Speaker 1: there was someone vulnerable or not vulnerable, but who was

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Speaker 1: a person who was at risk in a case. And

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Speaker 1: he always wanted to know who were those people and

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Speaker 1: what were the actual stakes for them? And of the

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Speaker 1: thousands or millions of similarly situated they might be investors,

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Speaker 1: they might be workers, they might be companies, how would

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Speaker 1: they be affected? And more than any justice at the time,

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Speaker 1: and I think maybe more than any justice in history.

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Speaker 1: That's what he put his finger on.

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Speaker 2: So you finish up your clerkship and you go to

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Speaker 2: the University of Chicago, where you end up staying as

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Speaker 2: a professor for twenty seven years. That's a heck of

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Speaker 2: a good run. What made Chicago such a special place

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Speaker 2: to teach at.

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Speaker 1: I did have something in between, i should say, which

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Speaker 1: wasn't like a vacation in Paris or a time being

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Speaker 1: a shoplifter. I had at the Department of Justice, where

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Speaker 1: I worked for a year in an office called the

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Speaker 1: Office of Legal Counsel under both Carter and Reagan, which

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Speaker 1: advises the president on the legality of what he proposes

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Speaker 1: to do.

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Speaker 2: So not like the Solicitor General that's arguing in front

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Speaker 2: of the Supreme Court. This is working directly with potus.

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Speaker 1: Yes, and well, when you say directly, that's true. Except

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Speaker 1: the number of meetings I had with President Reagan was zero.

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Speaker 1: The number of mediated interactions I had with President Reagan

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Speaker 1: was about five. And the amount of work that I

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Speaker 1: did for the President was basically every day. So the

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Speaker 1: Solicitor General's office argues the cases in front of the

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Speaker 1: Supreme Court. The Officer of Legal Counsel resolves conflicts, eg.

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Speaker 1: Between the State Department and the Defense Department, or if

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Speaker 1: the president says, can I make a treaty? Or can

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Speaker 1: I fire the air traffic controllers? Or can I do

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Speaker 1: something about civil rights? The Office of Legal Counsel is

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Speaker 1: the one that answers that question. And it's I think

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Speaker 1: it's at least as interesting as the Solicitor General's office

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Speaker 1: because you're not pleading to a court please agree with us.

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Speaker 1: You're actually resolving a controversy. And it's kind of in

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Speaker 1: between being a judge. You write opinions, kind of like

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Speaker 1: a judge, and you are part of a political operation

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Speaker 1: that is the executive branch. And if the president wants

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Speaker 1: to do something, you're not indifferent to the fact that

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Speaker 1: the president wants to do that. But saying no is

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Speaker 1: a very honorable tradition in that office. And we said

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Speaker 1: no plenty. And one reason you say no is the

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Speaker 1: president has an obligation to take care of the laws

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Speaker 1: be faithfully executed, right, and that's solemn.

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Speaker 2: Do we still do that anymore? If we kind of

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Speaker 2: waved that.

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Speaker 1: Off, No, that still happens. So under recent presidents, all

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Speaker 1: of them, the Office of Legal Consul has occasionally said no. Now,

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Speaker 1: in some times, the Office of Legal Console is more

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Speaker 1: politically let's say, what's the right word compromise. I want

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Speaker 1: to use a softer word, but that's not I don't

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Speaker 1: have to.

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Speaker 2: I have no ties to the unity, so I could

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Speaker 2: drop whatever bombs I want. I know, you need to

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Speaker 2: be a touchpec.

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Speaker 1: I think it is correct to say that the legal

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Speaker 1: independence of the Office of Legal Console varies over time. Okay,

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Speaker 1: but by tradition it is not just a lackey. And then,

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Speaker 1: as you say, went to the University of Chicago. I

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Speaker 1: went there because I was fearful that being a professor

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Speaker 1: would be like retiring in your twenties. And I thought,

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Speaker 1: that's not what I want to do.

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Speaker 2: The last landed gentry in America are tenured professors.

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Speaker 1: That's what I was fearful of. So I thought, you know,

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Speaker 1: I was in the Justice Department. I had clerked for

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Speaker 1: the Supreme Court. I had uh uh career plans and

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Speaker 1: the idea of just uh sitting in an office and

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Speaker 1: thinking what ideas do I have? That didn't feel really

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Speaker 1: like living. It felt more like celtifying. But at the

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Speaker 1: University of Chicago, the faculty was full of dynamism and energy,

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Speaker 1: and whether they were producing new ideas about the economic

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Speaker 1: analysis of law or new ideas about what freedom means,

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Speaker 1: or new ideas about the securities law, it was like

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Speaker 1: uh uh, it was electric. It was like Paris. And

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Speaker 1: the University of Chicago Law School at that time was,

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Speaker 1: you know, as lively an intellectual community as you know,

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Speaker 1: they say, Vienna at one point was like that, and

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Speaker 1: Berlin at one point was like that, and uh, Cambridge

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Speaker 1: and Oxford at some points have been like that. Chicago

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Speaker 1: was like that.

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Speaker 2: Are you still a quote Chicago person? Through and through?

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Speaker 1: I wouldn't say that. I think that everyone is themselves

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Speaker 1: rather than the Chicago person or you know, new Yorker.

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Speaker 1: Forgive me for those who consider themselves New Yorkers. There yourself.

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Speaker 1: But I was certainly inspired by and influenced by the

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Speaker 1: fact that at Chicago there was in his intense curiosity

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Speaker 1: and a sense that trying to figure out what's true

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Speaker 1: is thrilling and.

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Speaker 2: Noble.

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Speaker 1: So I saw Gary Becker, who won the Nobel and

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Speaker 1: the great Chicago economists, who was almost a law professor,

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Speaker 1: who was around all the time, man did he think

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Speaker 1: I was full of nonsense? And when he would ask

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Speaker 1: me questions in his workshop, the feeling of you are

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Speaker 1: wrong was combined with a feeling of respect that I'll

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Speaker 1: never forget. He was, you know, a giant, and I

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Speaker 1: was a nothing.

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Speaker 2: Wait wait, wait, I have to interrupt you here. So

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Speaker 2: you come out of clerking not for one Supreme Court,

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Speaker 2: but a state and the Supreme Court. Then you are

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Speaker 2: serving the White House in the Office General Counsel, and

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Speaker 2: suddenly you're a one l being pulled on again, feeling

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Speaker 2: that like panic, rise, am I going to get this wrong?

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Speaker 1: And being massed in front of everybody, Well, a little

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Speaker 1: like that. So I was in my twenties, mind you,

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Speaker 1: and I remember a dinner that Dick Posner had for

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Speaker 1: me as a newcomer of the University of Chicago, and

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Speaker 1: George Stigler, who was also a Nobel Prize guy, was

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Speaker 1: there and he asked me what I taught. And I

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Speaker 1: was teaching welfare law, and that was when I courses

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Speaker 1: in Chicago in Chicago, and it was about the social

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Speaker 1: security law and anti poveri law. George Stigler said that

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Speaker 1: why would you teach that? There aren't any poor people

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Speaker 1: in America. And he had written a paper showing that

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Speaker 1: if you earn six dollars a week, or something purporting

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Speaker 1: to show I should say, if you have six dollars

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Speaker 1: a week, you're going to be fine. And my reaction

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Speaker 1: to that was, your name may be Stigler, and you

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Speaker 1: may have a Nobell, but I don't believe a second

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Speaker 1: that that paper is correct. And he was much smarter

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Speaker 1: and more learned than I was, and it was a

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Speaker 1: terrible dinner. But I did have, back then maybe now

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Speaker 1: a sense that, you know, I'm going to give him

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Speaker 1: a best shot, and I didn't have a sense that

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Speaker 1: I was necessarily wrong. And I remember Stigler's fierceness, and

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Speaker 1: he was Becker was a great man who was respectful

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Speaker 1: as well as skeptical. Stigler was contemptuous as well as curious.

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Speaker 1: Who was this young fool who was at our dinner party.

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Speaker 1: But Tick Posner, who was there, who was also a giant,

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Speaker 1: was at that dinner. He was kind, so he saw

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Speaker 1: I was in trouble because Stigler was so amazingly smart

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Speaker 1: and quick. And Posner, who agreed with Stiegler, came to

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Speaker 1: my defense and that was the start of a great friendship.

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Speaker 2: That's really really quite interesting. And thank goodness there are

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Speaker 2: no poor people in America, because just think about how

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Speaker 2: uncomfortable would be to see homeless in big cities and

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Speaker 2: people unable to pay for medical care. I mean, what

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Speaker 2: sort of a country as that sort of thing? Yeah,

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Speaker 2: I mean, thank goodness, he was right.

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Speaker 1: Yeah, we probably need a progressive income tax or something,

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Speaker 1: and jobs programs and educational opportunity.

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Speaker 2: So here is the fascinating irony about your career starting

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Speaker 2: in Chicago and now you've been at Harvard for quite

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Speaker 2: a while, back and forth to public service, but still

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Speaker 2: at Harvard Law School for quite a while. It seems

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Speaker 2: like those are the endpoints on the intellectual spectrum, at

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Speaker 2: least in terms of legal thought. Am I overstating that

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Speaker 2: or is that fair?

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Speaker 1: It's a great question. So Chicago, when I was there,

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Speaker 1: was the center of right of center legal thought. It

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Speaker 1: had a very large percentage of the most influential right

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Speaker 1: of center people, and they were fantastic and they continue

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Speaker 1: to be great friends. Harvard was the place where critical

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Speaker 1: legal studies was born. It's kind of not a thing anymore,

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Speaker 1: but that was the left of center to law and economics,

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Speaker 1: which was the right of center, I thought. Even when

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Speaker 1: I was at Chicago, though I wasn't right of center,

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Speaker 1: I thought law and economics was extremely important and kind

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Speaker 1: of on the right track. And I thought critical legal

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Speaker 1: studies was a bunch of adjectives and nouns and not

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Speaker 1: really adding up to much. But I admired at Harvard

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Speaker 1: the contraditional law of people who were fantastically clear headed

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Speaker 1: about the law, for sure. And I admired the students

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Speaker 1: at Harvard who were so diverse in terms of intellectual

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Speaker 1: interests and intellectual background and politics and everything. Chicago has

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Speaker 1: intellectual diversity too, but it's just smaller. So I felt

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Speaker 1: that Harvard was a little like New York City and

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Speaker 1: Chicago was a little like Boston, smaller, more tightly connected

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Speaker 1: everyone to everyone else.

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Speaker 2: And I love them both. So you work at Harvard

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Speaker 2: with some just legendary professors. Did you overlap with Guido

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Speaker 2: Calabrisi when he was I think dean of you.

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Speaker 1: You know, he was at Yale, and I know him

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Speaker 1: very well and I love him dearly, and he's ninety

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Speaker 1: something now, and he was a great influence on me

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Speaker 1: and Harvard and Yale often have intellectual interactions that are

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Speaker 1: breeding a friendship and Chicago and Yale also, and Calibrazy

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Speaker 1: was a founder of economic analysis of law and a

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Speaker 1: little more, let's say, focused on poor people and people

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Speaker 1: are struggling than Chicago economics. So there's a Yale school

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Speaker 1: in Chicago school and Calibrazy. I can't quite say he

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Speaker 1: was a mentor, but he feels like that to me.

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Speaker 2: And Lawrence Tribe probably the pre eminent constitutional law scholar

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Speaker 2: in the country. Is am I again, am I overstating that?

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Speaker 2: Or is that a fair I.

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Speaker 1: Think it's a little like basketball, and some people like

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Speaker 1: Michael Jordan and some people like Lebron James and something.

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Speaker 1: And Bill Russell, of course was the greatest winner of

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Speaker 1: all time. Tribe was my teacher and oh really, and

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Speaker 1: he was maybe of the three the most like Michael Jordan.

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Speaker 1: His intellectual athleticism was and is next level, next level.

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Speaker 1: And he was when when he was my teacher, he

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Speaker 1: was charismatic, he was clear, he was bursting with ideas.

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Speaker 1: He was writing his great treatise at the time, and

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Speaker 1: it was a bonfire of thinking in a constructive bonfires destroyed.

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Speaker 1: Tribe didn't destroy anything, and I thought he was dazzling,

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Speaker 1: And he wrote a letter for me, actually for my

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Speaker 1: Supreme Court clerkship with Justice Marshall, which I'm very grateful for.

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Speaker 1: He's he's still a great friend, and you know he's

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Speaker 1: he's in many ways he's different from me in the

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Speaker 1: last years. Particularly he's more politically engaged in a way

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Speaker 1: that is not my typical style. But I'm full of

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Speaker 1: admiration for him.

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Speaker 2: Really really quite interesting. So let's talk a little bit

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Speaker 2: about this program. What leads to something like this coming about.

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Speaker 2: It doesn't sound like your typical law school sort of

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Speaker 2: class completely.

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Speaker 1: So there has been, as I think everyone's aware now,

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Speaker 1: an explosion of work in behavioral economics and behavioral science

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Speaker 1: about human behavior. So we know how people depart from

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Speaker 1: perfect rationality. So people are often focused on short term,

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Speaker 1: not the long term. They're often unrealistically optimistic, their attention

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Speaker 1: is limited. They can be manipulated because they'll focus on

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Speaker 1: one or two features of let's say a product, rather

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Speaker 1: than seven, and that means they'll get two features they

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Speaker 1: like and five that they in the long run will despise.

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Speaker 1: So we know a lot about that. This has major

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Speaker 1: implications for law So with respect to fiduciary obligations, let's

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Speaker 1: say a fiduciary, what do they have to tell people

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Speaker 1: and what do they have to make clear to people

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Speaker 1: and not just tell people? And behavioral science tells us

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Speaker 1: a lot about that. If we're thinking about free speech

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Speaker 1: law and we're thinking about the marketplace of ideas, behavioral

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Speaker 1: science behavioral economics might tell us something about how people

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Speaker 1: get confused or fooled. If we're talking about property law,

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Speaker 1: toward law or contract law, there has to be a

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Speaker 1: sense of how people are going to react to what

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Speaker 1: the law is doing. So if the law has a

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Speaker 1: default term, let's say that you have to perform in

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Speaker 1: a reasonable time, and let's say the company that's doing

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Speaker 1: the performance thinks a reasonable time means maybe next year.

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Speaker 1: What does the law do about that? And so there

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Speaker 1: are are zillion questions. Algorithms in AI are kind of

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Speaker 1: top of mind now for the law to try to

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Speaker 1: figure out that have a behavioral feature, and that's that's

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Speaker 1: kind of what we're doing with our program.

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Speaker 2: That sounds really interesting, assuming since you co authored Nudge

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Speaker 2: with Dick Thaylor, which came first working with Sailor or

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Speaker 2: the program on behavioral economics and public policy.

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Speaker 1: I'll tell you a story. Before I met Faylor, I

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Speaker 1: was overwhelmed the best way by the work of conomin University,

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Speaker 1: a failure. So I thought, this is the thing, and

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Speaker 1: I started to work on some papers, one of which

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Speaker 1: was called behavioral analysis of law. And then Taylor came

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Speaker 1: to the University of Chicago and we started having lunch together,

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Speaker 1: and I started working with him when he was working

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Speaker 1: on a paper with a law professor named Christine Joels

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Speaker 1: that I thought was going too slowly, and I said,

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Speaker 1: if you don't write that paper, I'm going to write

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Speaker 1: my paper and it might steal your thunder. It won't

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Speaker 1: be as good as yours, but it'll be earlier. And

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Speaker 1: Dick said, you know, this was a fantastic moment for me.

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Speaker 1: He said, why don't you join us? And we wrote

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Speaker 1: it together. So I was intrigued by the behavioral stuff

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Speaker 1: before I met fayalor after I met Faylor, I had

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Speaker 1: a the world's best partner on this stuff. And then

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Speaker 1: when I went to Harvard our program that followed, and

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Speaker 1: some of it involves nudges, some of it has nothing

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Speaker 1: to do with nudges, but all of it has to

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Speaker 1: do with behavioral science.

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Speaker 2: So you also co wrote Noise with with Danny Kahneman.

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Speaker 2: It seems that there's a theme in all your books, nudge, noise, sludge.

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Speaker 2: You're constantly looking at the decision making process, and not

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Speaker 2: just from an academic perspective, but how it affects people

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Speaker 2: in the real world, how it affects organizations, how it

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Speaker 2: affects individuals. Tell us a little bit about the integration

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Speaker 2: of behavioral finance and behavioral economics with law.

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Speaker 1: Okay, well, let's talk a little bit about groups, shall we.

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Speaker 1: If you get a group of like minded people together,

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Speaker 1: they typically end up thinking a more extreme version of

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Speaker 1: what they thought before they started to talk. So if

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Speaker 1: you get a group of people who tend to think,

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Speaker 1: you know, we ought to invest in X. Take your pick, soap,

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Speaker 1: there's new kind of soap, we ought to invest in X.

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Speaker 1: That's the average view soap. Everyone needs to be clean

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Speaker 1: and with climate change, soap it's going to be crazy

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Speaker 1: soap companies. If that's the average view, I'm starting to

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Speaker 1: convince myself by the way to invest in soap companies,

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Speaker 1: which is probably not necessarily right. Let's put it that way.

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Speaker 1: If people talk with one another, and they start with

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Speaker 1: an initial disposition, they tend to think an extreme version

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Speaker 1: of what they thought. They become more confident, more unified,

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Speaker 1: and more extreme. This is a real problem for companies,

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Speaker 1: it's a real problem for law. We have data suggesting

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Speaker 1: if you get three judges who are let's say, Democratic

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Speaker 1: appointees on Court of Appeals, not two Democratic appointees and

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Speaker 1: one Republican three Democratic appointees, the likelihood of a left

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Speaker 1: of center opinion shoots up really dramatically. That's a crazy

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Speaker 1: finding because if you have two Democratic appointees on a

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Speaker 1: three judge panel, they have the votes, they don't need

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Speaker 1: that Republican appointee, but they are much more moderate, and

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Speaker 1: it's symmetrical. Three Republican appointees are much more right wing

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Speaker 1: in their voting patterns then two Republican appointees on a

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Speaker 1: panel with one Democratic appointee.

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Speaker 2: So group think even amongst judges is worse if there's

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Speaker 2: three of them in no countering voices versus, hey, we

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Speaker 2: have a majority and we're going to sign how we want,

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Speaker 2: but everybody kind of wants to be rational and cooperative.

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Speaker 1: Is that the suggest And here's the really cool thing.

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Speaker 1: There is a book called group think a few decades ago.

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Speaker 1: It's a fantastic term. It's not clear what group think is,

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Speaker 1: and if we clear clarify what is, it's not clear

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Speaker 1: whether it exists. So the rigorous efforts to test group

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Speaker 1: think have a bunch of question marks. But there's something

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Speaker 1: like groupthink which does exist, which is a testable hypothesis,

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Speaker 1: which is, if you've got a group of people, it

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Speaker 1: will end up after deliberation in a more extreme point,

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Speaker 1: in line with its pre deliberation tendencies. So that's a mouthful.

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Speaker 1: But let's suppose you have a group of six people

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Speaker 1: deciding whether to invest in soap or instead electric cars.

435
00:26:28,800 --> 00:26:31,080
Speaker 1: Those are the options. Let's say four of them think

436
00:26:31,160 --> 00:26:33,760
Speaker 1: soap and two of them think electric cars, and they

437
00:26:33,760 --> 00:26:36,000
Speaker 1: think the same thing. They think what they do with

438
00:26:36,080 --> 00:26:40,160
Speaker 1: equal intensity. At the end of the discussion, the prediction

439
00:26:40,320 --> 00:26:43,000
Speaker 1: is the group is going to go soap, soap, soap, soap, soap,

440
00:26:43,320 --> 00:26:47,600
Speaker 1: and it's going to do that with a considerable confidence

441
00:26:47,640 --> 00:26:51,360
Speaker 1: as well as unanimity. That will be the statistical regularity.

442
00:26:51,720 --> 00:26:55,840
Speaker 1: And I've done work on political issues, climate change, affirmative action,

443
00:26:56,040 --> 00:26:58,720
Speaker 1: same sex stuff, where if you've get a group that

444
00:26:58,760 --> 00:27:01,480
Speaker 1: has a conservative disposition, they go whish to the right

445
00:27:01,520 --> 00:27:03,680
Speaker 1: after they talk with one another. If they have a

446
00:27:03,760 --> 00:27:06,440
Speaker 1: left of center disposition, they go whish to the left

447
00:27:06,560 --> 00:27:09,439
Speaker 1: after they talk with one another. And Conneman and I

448
00:27:09,520 --> 00:27:13,000
Speaker 1: did this a study with this on punitive damages jury awards,

449
00:27:13,359 --> 00:27:16,560
Speaker 1: where for jury's mad at a company, they're going to

450
00:27:16,600 --> 00:27:19,560
Speaker 1: be super mad at a company after they talk with

451
00:27:19,640 --> 00:27:23,080
Speaker 1: one another, which helps explain why punitive damages are both

452
00:27:23,160 --> 00:27:25,560
Speaker 1: unpredictable and often really really high.

453
00:27:25,680 --> 00:27:29,919
Speaker 2: So that's so let's take that basic concept and apply

454
00:27:30,040 --> 00:27:35,720
Speaker 2: it to online where you have social media and all

455
00:27:35,760 --> 00:27:39,800
Speaker 2: sorts of trolling activities, and you end up with conspiracy

456
00:27:39,840 --> 00:27:44,760
Speaker 2: theories like QAnon. How should public policy deal with these

457
00:27:44,800 --> 00:27:51,040
Speaker 2: sort of things between anti vaxxers and anti democratic election deniers.

458
00:27:51,640 --> 00:27:54,480
Speaker 2: This is a genuine threat to the health and safety

459
00:27:54,520 --> 00:27:55,160
Speaker 2: of the country.

460
00:27:55,800 --> 00:27:59,639
Speaker 1: So back in two thousand, I agreed to write a

461
00:27:59,680 --> 00:28:05,160
Speaker 1: book for Princeton University Press called Republic dot Com. And

462
00:28:05,200 --> 00:28:07,760
Speaker 1: I had a title, but I didn't have a book,

463
00:28:08,280 --> 00:28:14,840
Speaker 1: and I had six months of failure, like unbelievable failure,

464
00:28:15,040 --> 00:28:20,240
Speaker 1: like either nothing or it was terrible. You I was

465
00:28:20,359 --> 00:28:23,359
Speaker 1: worse than common. And because what he didn't like in

466
00:28:23,400 --> 00:28:26,399
Speaker 1: his own work. His work is actually good. What I

467
00:28:26,480 --> 00:28:29,600
Speaker 1: produced in those six months was in fact horrible. I

468
00:28:29,640 --> 00:28:32,520
Speaker 1: still have it somewhere. But then I thought, okay, the

469
00:28:32,560 --> 00:28:37,840
Speaker 1: real problem is echo chambers and the absence of shared

470
00:28:38,680 --> 00:28:42,440
Speaker 1: exposure to things. And then when I thought echo chambers,

471
00:28:42,480 --> 00:28:46,800
Speaker 1: shared exposures, I sketched out nine chapters, and I wrote

472
00:28:46,840 --> 00:28:49,000
Speaker 1: a chapter a day, and I had a book after

473
00:28:49,120 --> 00:28:51,240
Speaker 1: nine days. I've never had anything like that. It was

474
00:28:51,360 --> 00:28:57,320
Speaker 1: like a frenzy, a happy frenzy of book writing. And

475
00:28:57,400 --> 00:29:00,400
Speaker 1: that book has now gone through three editions. It was

476
00:29:00,600 --> 00:29:03,520
Speaker 1: first called republic dot.

477
00:29:03,280 --> 00:29:07,160
Speaker 2: Com hashtag republic divided democracy in the age of social media.

478
00:29:07,240 --> 00:29:10,080
Speaker 1: That one, that's the very recent one, and it's exactly

479
00:29:10,120 --> 00:29:14,480
Speaker 1: on your point. So what should be done by various actors,

480
00:29:14,520 --> 00:29:17,760
Speaker 1: I think is a really hard question. But the existence

481
00:29:17,800 --> 00:29:23,120
Speaker 1: of the problem is palpable. If you're thinking about yourself

482
00:29:23,320 --> 00:29:26,440
Speaker 1: just as an individual, to try to be exposed to

483
00:29:26,560 --> 00:29:31,040
Speaker 1: diverse ideas is a really good idea. They're apps. There's

484
00:29:31,040 --> 00:29:33,040
Speaker 1: one I don't know if it still is working. I hope.

485
00:29:33,040 --> 00:29:36,160
Speaker 1: So it's called read across the aisle, where you where

486
00:29:36,200 --> 00:29:38,840
Speaker 1: you can tell whether you're just reading one kind of

487
00:29:38,840 --> 00:29:42,040
Speaker 1: thing or another kind of thing, so there's self monitoring.

488
00:29:42,160 --> 00:29:46,040
Speaker 1: I know that some social media platforms have thought hard

489
00:29:46,240 --> 00:29:50,880
Speaker 1: about how to handle the echo chamber phenomenon, and hard

490
00:29:50,960 --> 00:29:56,680
Speaker 1: also about how to think about the the misinformation problem.

491
00:29:57,000 --> 00:30:02,560
Speaker 1: And there are various things that behavioral scientis would counsel

492
00:30:02,680 --> 00:30:11,720
Speaker 1: consideration of, including warnings, including reduced circulation levels, including in

493
00:30:11,840 --> 00:30:15,960
Speaker 1: extreme cases, very extreme cases, taking things down. Not through

494
00:30:16,000 --> 00:30:19,200
Speaker 1: government because that then there's a First Amendment issue, but

495
00:30:19,320 --> 00:30:23,960
Speaker 1: through voluntary action. And one size doesn't fit all, but

496
00:30:25,520 --> 00:30:28,280
Speaker 1: I agree this is a very serious challenge.

497
00:30:28,320 --> 00:30:31,600
Speaker 2: So a different book, I assume, is on rumors, how

498
00:30:31,640 --> 00:30:35,720
Speaker 2: falsehood spread, why we believe them, and what can be done.

499
00:30:36,040 --> 00:30:40,960
Speaker 2: It seems like we are very predisposed to believe nonsense

500
00:30:41,240 --> 00:30:44,200
Speaker 2: if it confirms our prior beliefs. We believe what we

501
00:30:44,240 --> 00:30:46,560
Speaker 2: want to believe in. Who cares about the facts.

502
00:30:46,600 --> 00:30:49,320
Speaker 1: Okay, so here let's talk about three things might we

503
00:30:51,000 --> 00:30:56,120
Speaker 1: The first is if I tell you that it's raining

504
00:30:56,160 --> 00:31:01,160
Speaker 1: outside right now, you aren't going to think think he's

505
00:31:01,440 --> 00:31:06,200
Speaker 1: fooling me. It's sunny and beautiful outside. You're probably going

506
00:31:06,240 --> 00:31:09,320
Speaker 1: to think, maybe I should get an umbrella. So when

507
00:31:09,360 --> 00:31:13,360
Speaker 1: people hear something and there's probably a good evolutionary explanation

508
00:31:13,480 --> 00:31:17,280
Speaker 1: for this. Under ordinary circumstances, they think it's true, and

509
00:31:17,360 --> 00:31:21,840
Speaker 1: that truth bias is it's sometimes called is essential. If

510
00:31:21,880 --> 00:31:23,600
Speaker 1: we try to live in a world in which we

511
00:31:23,640 --> 00:31:27,480
Speaker 1: thought everything people said was false, we couldn't get through

512
00:31:27,720 --> 00:31:28,120
Speaker 1: a day.

513
00:31:28,360 --> 00:31:33,080
Speaker 2: Cooperative primates in a social group provided a survival advantage,

514
00:31:33,480 --> 00:31:37,040
Speaker 2: so you're not inclined to disbelieve someone looking eye and

515
00:31:37,040 --> 00:31:38,320
Speaker 2: telling you something completely.

516
00:31:38,560 --> 00:31:43,280
Speaker 1: But truth bias can lead us in really terrible directions,

517
00:31:43,360 --> 00:31:46,800
Speaker 1: and that's independent of motive. So I don't need to

518
00:31:47,560 --> 00:31:49,840
Speaker 1: want to think it's raining to think if someone tells

519
00:31:49,880 --> 00:31:53,800
Speaker 1: me it's raining, it's umbrella time. That's one truth bias.

520
00:31:53,880 --> 00:31:58,280
Speaker 1: The other thing is confirmation bias, where if we're told

521
00:31:58,400 --> 00:32:01,560
Speaker 1: things that fit with what we think, we tend to

522
00:32:01,680 --> 00:32:04,320
Speaker 1: like that and we tend to believe it because it

523
00:32:04,360 --> 00:32:07,760
Speaker 1: fits with what we think, and that can aggravate the

524
00:32:09,600 --> 00:32:13,680
Speaker 1: problem of echo chambers where people it's confirmation bias is

525
00:32:13,720 --> 00:32:16,280
Speaker 1: being catered to. So if you think the thing is

526
00:32:16,440 --> 00:32:20,600
Speaker 1: your investment in X is really going great, even though

527
00:32:20,600 --> 00:32:25,600
Speaker 1: all the indication is that it's risky. The confirmation, the

528
00:32:25,640 --> 00:32:30,120
Speaker 1: confirmatory material will have credibility. We have recent data suggesting.

529
00:32:30,200 --> 00:32:32,840
Speaker 1: There's a third thing, which is I think cooler than

530
00:32:32,880 --> 00:32:38,080
Speaker 1: truth bias or confirmation bias. Its name is desirability bias,

531
00:32:38,520 --> 00:32:44,680
Speaker 1: and it's like confirmation bias, except it's different. And maybe

532
00:32:44,680 --> 00:32:47,800
Speaker 1: I like it because of the phenomenon it draws attention

533
00:32:47,920 --> 00:32:51,000
Speaker 1: to because I find it desirable in a way that

534
00:32:51,120 --> 00:32:52,360
Speaker 1: it indicates it's fun.

535
00:32:52,560 --> 00:32:56,840
Speaker 2: So the desirability bias appeals to your own desirability.

536
00:32:56,200 --> 00:32:58,880
Speaker 1: Yeah, it does, because it fits with my conception of

537
00:32:58,960 --> 00:33:03,240
Speaker 1: human nature. Confirmation both, but let's pull them apart a bit.

538
00:33:03,360 --> 00:33:08,640
Speaker 1: So desirability bias means that people believe things if they

539
00:33:08,680 --> 00:33:12,440
Speaker 1: find it enjoyable to believe them. Where enjoyable is a

540
00:33:12,440 --> 00:33:15,120
Speaker 1: big concept. So it might mean it makes them smile,

541
00:33:15,200 --> 00:33:17,800
Speaker 1: It might make me makes them feel secure. It might

542
00:33:17,840 --> 00:33:22,080
Speaker 1: mean it makes them feel pleased. It could make feel

543
00:33:22,120 --> 00:33:24,960
Speaker 1: them make them feel grateful. It can be any number

544
00:33:25,040 --> 00:33:30,680
Speaker 1: of things, but desirability bias and confirmation bias are emphatically

545
00:33:30,720 --> 00:33:34,680
Speaker 1: not the same thing. You might hear something that fits

546
00:33:34,720 --> 00:33:40,280
Speaker 1: with your belief that is, like, you're really sick, but

547
00:33:40,360 --> 00:33:42,360
Speaker 1: you don't want to believe that because you don't want

548
00:33:42,400 --> 00:33:45,080
Speaker 1: to believe you're really sick. And so if something is

549
00:33:45,120 --> 00:33:49,360
Speaker 1: disconfirming but desirable. The data we have suggests that the

550
00:33:49,440 --> 00:33:54,600
Speaker 1: desirable will beat the confirmatory. So if you think the

551
00:33:54,640 --> 00:33:57,680
Speaker 1: economy is going to go sour and then you learn

552
00:33:58,400 --> 00:34:04,200
Speaker 1: that's not true, you might well be extremely credulous, meaning

553
00:34:04,280 --> 00:34:08,440
Speaker 1: willing to believe the happy thing even though it's disconfirming

554
00:34:08,480 --> 00:34:13,839
Speaker 1: of your belief. So desirability bias means things that please us,

555
00:34:13,920 --> 00:34:17,440
Speaker 1: we will tend to believe, even if they are disconfirming

556
00:34:17,800 --> 00:34:19,560
Speaker 1: of what we start believing.

557
00:34:20,440 --> 00:34:25,120
Speaker 2: That's really intriguing. What I find so fascinating about confirmation

558
00:34:25,320 --> 00:34:30,120
Speaker 2: bias is the underlying investment in the model of the

559
00:34:30,160 --> 00:34:33,640
Speaker 2: world our brains create. I think our brains consume twenty

560
00:34:33,680 --> 00:34:38,080
Speaker 2: five percent of our daily energy, and so the models

561
00:34:38,400 --> 00:34:44,200
Speaker 2: we create over time we are so reluctant to challenge.

562
00:34:44,320 --> 00:34:48,239
Speaker 2: We don't want to look for disconfirming evidence because hey,

563
00:34:48,320 --> 00:34:51,279
Speaker 2: we have all these sunk costs over here to bring

564
00:34:51,360 --> 00:34:55,200
Speaker 2: up another fallacy. Tell us a little more about how

565
00:34:55,239 --> 00:34:59,239
Speaker 2: you test for desirability bias and how it manifests in

566
00:34:59,280 --> 00:35:00,640
Speaker 2: things like public policy.

567
00:35:01,000 --> 00:35:04,880
Speaker 1: Okay, so let's talk a little bit about confirmation bias.

568
00:35:05,680 --> 00:35:11,560
Speaker 1: If I believe that the Holocaust happened, if I read

569
00:35:11,640 --> 00:35:16,120
Speaker 1: something think saying it didn't happen. I will dismiss that,

570
00:35:16,880 --> 00:35:23,279
Speaker 1: not because please that the Holocaust happened, but because I

571
00:35:23,320 --> 00:35:27,120
Speaker 1: am so clear that the Holocaust happened that the information

572
00:35:27,239 --> 00:35:32,000
Speaker 1: that's inconsistent with my belief has no credibility. So it's basian.

573
00:35:32,360 --> 00:35:36,440
Speaker 1: It's not about motivation. So I believe that dropped objects fall.

574
00:35:36,840 --> 00:35:39,120
Speaker 1: If a magician comes to me and says, you know,

575
00:35:39,200 --> 00:35:42,560
Speaker 1: you're not quite right on that, I will think, magician,

576
00:35:42,600 --> 00:35:45,720
Speaker 1: you're pretty good at your job. But I really believe

577
00:35:46,040 --> 00:35:49,000
Speaker 1: dropped objects fall. It's not about my motivations. It's what

578
00:35:49,400 --> 00:35:52,839
Speaker 1: I start with. So a lot of what we call

579
00:35:52,920 --> 00:35:57,200
Speaker 1: confirmation bias is basian updating. Given our priors, we dismiss

580
00:35:57,280 --> 00:35:59,759
Speaker 1: what is disconfirming on the ground that how can it

581
00:35:59,760 --> 00:36:02,680
Speaker 1: be true that dropped objects don't fall? Or how can

582
00:36:02,719 --> 00:36:05,000
Speaker 1: it be true that Bill Russell isn't the greatest winner

583
00:36:05,000 --> 00:36:08,560
Speaker 1: in the history of organized sports. I have actually an

584
00:36:08,600 --> 00:36:11,280
Speaker 1: emotional investment as well as.

585
00:36:11,480 --> 00:36:14,400
Speaker 2: One sports opinion, which is emotion. The other is physics.

586
00:36:14,480 --> 00:36:20,120
Speaker 2: But all that aside, So desirability bias is even if

587
00:36:20,200 --> 00:36:25,520
Speaker 2: disconfirming seems to have a great resonance within ourselves. Yeah,

588
00:36:25,960 --> 00:36:27,359
Speaker 2: why do we think that is okay.

589
00:36:27,480 --> 00:36:32,080
Speaker 1: So that's about motivation. Desirability bias isn't about rational updating.

590
00:36:32,120 --> 00:36:35,280
Speaker 1: It's only about motivation. Here's something that pulls them apart.

591
00:36:35,360 --> 00:36:38,399
Speaker 1: I'm going to give a simplified version of the best

592
00:36:38,480 --> 00:36:41,000
Speaker 1: date i'm aware of on this, where people in the

593
00:36:41,000 --> 00:36:46,600
Speaker 1: twenty sixteen election who favored Trump or Clinton also had

594
00:36:46,640 --> 00:36:50,480
Speaker 1: predictions about whether Trump or Clinton would win before the election.

595
00:36:51,280 --> 00:36:55,400
Speaker 1: Let's take Clinton voters. If they thought that Trump would

596
00:36:55,440 --> 00:36:59,600
Speaker 1: win and then they were given information that suggested Clinton

597
00:36:59,600 --> 00:37:04,160
Speaker 1: would win, they found it particularly credible. Now that was

598
00:37:04,200 --> 00:37:09,120
Speaker 1: disconfirming information. It suggested what they believed would happen was false,

599
00:37:09,560 --> 00:37:13,440
Speaker 1: but it was pleasing information. It suggested that the information

600
00:37:13,600 --> 00:37:17,440
Speaker 1: they were receiving would make them smile rather than suffer.

601
00:37:17,840 --> 00:37:21,200
Speaker 1: And it worked exactly the same for Trump voters who

602
00:37:21,280 --> 00:37:24,200
Speaker 1: thought that Trump would lose, but then when they got

603
00:37:24,239 --> 00:37:28,319
Speaker 1: information suggesting that Trump would win, they thought, I'll believe that.

604
00:37:28,800 --> 00:37:32,000
Speaker 1: And it's because it was desirable. So we're just learning

605
00:37:32,040 --> 00:37:36,320
Speaker 1: about desirability bias. It has an overlap with optimism bias.

606
00:37:36,320 --> 00:37:40,080
Speaker 1: It has implications for law. So in law and among

607
00:37:40,160 --> 00:37:45,040
Speaker 1: real lawyers, you can create something pretty funny instantly which

608
00:37:45,080 --> 00:37:48,000
Speaker 1: is you tell them, you know, imagine you're representing the

609
00:37:48,000 --> 00:37:51,080
Speaker 1: plaintiff in a lawsuit, what are the chances the person

610
00:37:51,120 --> 00:37:53,560
Speaker 1: will win? They say really high. If you ask the

611
00:37:53,560 --> 00:37:56,120
Speaker 1: same kind of people, you're representing the defendant, what's the

612
00:37:56,200 --> 00:37:59,160
Speaker 1: chance the defendant will win, they say the chances urge

613
00:37:59,160 --> 00:38:01,920
Speaker 1: are really high. So you can instantly put people in

614
00:38:01,960 --> 00:38:04,520
Speaker 1: the role of plane off lawyer or defense consul and

615
00:38:04,560 --> 00:38:07,920
Speaker 1: that their predictions about outcomes will fit with what they

616
00:38:08,000 --> 00:38:12,600
Speaker 1: think is desirable given the role they assumed thirty seconds ago.

617
00:38:13,200 --> 00:38:16,920
Speaker 2: So that's kind of interesting. Let's relate this to another book,

618
00:38:17,120 --> 00:38:22,960
Speaker 2: How change happens. When we look at things sexual harassment, smoking,

619
00:38:23,120 --> 00:38:27,000
Speaker 2: white supremacy, gay riots, climate change, seems like there's been

620
00:38:27,120 --> 00:38:31,640
Speaker 2: an ongoing evolution. Some of these things are very gradual,

621
00:38:32,000 --> 00:38:35,960
Speaker 2: even things like seat belts. So suddenly, I think the

622
00:38:36,080 --> 00:38:39,520
Speaker 2: number today is something like ten or fifteen percent of

623
00:38:39,520 --> 00:38:42,200
Speaker 2: people don't use seat belts, But the number was forty

624
00:38:42,239 --> 00:38:46,160
Speaker 2: to fifty percent for long, long periods of time until

625
00:38:46,200 --> 00:38:50,400
Speaker 2: we started with the beeping to nudge them to do that.

626
00:38:50,920 --> 00:38:54,640
Speaker 2: So tell us a little bit. How does social change happen?

627
00:38:54,840 --> 00:38:59,000
Speaker 2: Is this Hemingway esk or is it continually gradual, and

628
00:38:59,080 --> 00:38:59,839
Speaker 2: not all at once.

629
00:39:00,560 --> 00:39:05,200
Speaker 1: Well, okay, So to understand this we need to have

630
00:39:05,239 --> 00:39:10,080
Speaker 1: some moving parts. One thing is that people have in

631
00:39:10,120 --> 00:39:15,600
Speaker 1: their heads beliefs and desires that they don't tell anyone about.

632
00:39:16,520 --> 00:39:21,960
Speaker 1: So you might think, I think that violence against people

633
00:39:21,960 --> 00:39:26,120
Speaker 1: of color is pervasive and horrible, or you might think

634
00:39:26,200 --> 00:39:29,319
Speaker 1: I think meat eating is a really bad idea, or

635
00:39:29,360 --> 00:39:33,120
Speaker 1: you might think I think gun rights are very important

636
00:39:33,480 --> 00:39:37,520
Speaker 1: and it's terrible that there are people in the United

637
00:39:37,560 --> 00:39:41,280
Speaker 1: States who are seeking to disarm the American public. Okay,

638
00:39:41,320 --> 00:39:43,719
Speaker 1: people who think all of those three things, at some

639
00:39:43,840 --> 00:39:48,360
Speaker 1: point over the last fifty years have shut up thinking

640
00:39:48,400 --> 00:39:51,480
Speaker 1: if they say any of those things, they will be

641
00:39:51,560 --> 00:39:55,960
Speaker 1: ostracized or disliked or something I think of political correctness

642
00:39:56,280 --> 00:40:00,360
Speaker 1: rit large. Sometimes what happens, and this is the first

643
00:40:00,360 --> 00:40:04,040
Speaker 1: moving part, is that people are given a permission slip.

644
00:40:04,680 --> 00:40:08,360
Speaker 1: So it might be that a political candidate says black

645
00:40:08,400 --> 00:40:12,680
Speaker 1: lives matter, or it might be that a prominent female

646
00:40:12,719 --> 00:40:16,520
Speaker 1: actor says I was sexually harassed and if you were

647
00:40:16,560 --> 00:40:20,239
Speaker 1: to say, hashtag me too on Twitter. Or it might

648
00:40:20,320 --> 00:40:25,279
Speaker 1: be that someone says, I think people should be allowed

649
00:40:25,280 --> 00:40:27,919
Speaker 1: to get married, regardless of whether I want to marry

650
00:40:27,920 --> 00:40:30,239
Speaker 1: a man or a woman, regardless of their gender, and

651
00:40:31,120 --> 00:40:34,200
Speaker 1: it's a free country. Go for it, and then people

652
00:40:34,320 --> 00:40:39,440
Speaker 1: will feel licensed to say what they had shut up about.

653
00:40:40,000 --> 00:40:44,120
Speaker 1: And for many social movements, the fall of communism is

654
00:40:44,160 --> 00:40:46,759
Speaker 1: an example. The rise of the federalist Socie in the

655
00:40:46,840 --> 00:40:48,960
Speaker 1: United States is another example. I saw that in real

656
00:40:49,000 --> 00:40:53,200
Speaker 1: time the success of President Trump the success of President Obama,

657
00:40:53,600 --> 00:40:57,640
Speaker 1: for all their differences, those all involved, in significant part

658
00:40:58,040 --> 00:41:01,680
Speaker 1: people being given a permission slip that they never had before.

659
00:41:02,280 --> 00:41:05,560
Speaker 1: The second thing that matters is that whether we want

660
00:41:05,600 --> 00:41:09,400
Speaker 1: to participate or endorse a social change depends on what

661
00:41:09,520 --> 00:41:12,360
Speaker 1: our threshold is for doing that. Now. It might be

662
00:41:12,400 --> 00:41:16,040
Speaker 1: a threshold for becoming active, It might be a threshold

663
00:41:16,120 --> 00:41:18,440
Speaker 1: for just voting for someone. It might be a threshold

664
00:41:18,480 --> 00:41:21,799
Speaker 1: for saying something. And we all have different thresholds and

665
00:41:21,840 --> 00:41:24,560
Speaker 1: we probably don't know what they are. So if you

666
00:41:24,640 --> 00:41:28,520
Speaker 1: think of some movement for something, a lot of people

667
00:41:28,560 --> 00:41:31,959
Speaker 1: participated in it, maybe the civil rights movement that Martin

668
00:41:32,040 --> 00:41:36,320
Speaker 1: Luther King helped lead, and there were people who had

669
00:41:36,600 --> 00:41:38,400
Speaker 1: a very low threshold. They were just going to go

670
00:41:38,520 --> 00:41:40,920
Speaker 1: for it. And there are others who would join if

671
00:41:40,960 --> 00:41:44,120
Speaker 1: a certain number of people joined and the thresholds really matter,

672
00:41:44,200 --> 00:41:47,120
Speaker 1: and we don't know what their distribution is in advance,

673
00:41:47,360 --> 00:41:50,600
Speaker 1: and it has to play itself out. So that happened

674
00:41:50,640 --> 00:41:53,920
Speaker 1: with seatwelt buckling. And the third thing, which is maybe

675
00:41:53,960 --> 00:41:57,759
Speaker 1: most important, is social influences. So you might buckle your

676
00:41:57,800 --> 00:42:01,880
Speaker 1: belt if everyone else is buckling their belt. There are

677
00:42:01,880 --> 00:42:04,480
Speaker 1: other people who won't buckle their belt if no one's

678
00:42:04,480 --> 00:42:06,759
Speaker 1: buckling their belt. I remember a time when if you

679
00:42:06,800 --> 00:42:10,080
Speaker 1: buckled your belt, you were saying that the driver is

680
00:42:10,560 --> 00:42:13,600
Speaker 1: extremely dangerous, or you were saying that you were yourself

681
00:42:13,680 --> 00:42:17,239
Speaker 1: really cowardly and timid. And who wants to buckle their

682
00:42:17,239 --> 00:42:19,840
Speaker 1: belt and accuse a friend of being an unsafe driver

683
00:42:20,320 --> 00:42:23,480
Speaker 1: or disclosed that you're a terrified, scared rabbit. And now

684
00:42:23,520 --> 00:42:27,120
Speaker 1: buckling a seat belt doesn't accuse the driver and doesn't

685
00:42:27,239 --> 00:42:30,800
Speaker 1: confess timidity. And did the social norm changed?

686
00:42:31,040 --> 00:42:34,399
Speaker 2: And can I share a quick story. I had Bob

687
00:42:34,440 --> 00:42:37,400
Speaker 2: Schiller on the show a couple of times, and once

688
00:42:37,800 --> 00:42:40,239
Speaker 2: he had to go someone from here, and we took

689
00:42:40,640 --> 00:42:42,440
Speaker 2: a cab together to I think was to the New

690
00:42:42,480 --> 00:42:44,400
Speaker 2: York Times building and we got into the back of

691
00:42:44,480 --> 00:42:49,160
Speaker 2: the cab and Bob buckles his safety belt in the

692
00:42:49,200 --> 00:42:51,839
Speaker 2: back of the cab. I'm like, well, here's a guy

693
00:42:51,840 --> 00:42:56,440
Speaker 2: who studies behavioral finance, and as an economist, I hadn't

694
00:42:56,480 --> 00:42:58,920
Speaker 2: really I always wear my seat belt when I'm driving

695
00:42:59,360 --> 00:43:01,000
Speaker 2: or in the front seat. You get into the back,

696
00:43:01,080 --> 00:43:04,440
Speaker 2: you don't even think about it. Maybe I've been overlooking

697
00:43:04,520 --> 00:43:08,120
Speaker 2: this because of who he was and all the social

698
00:43:08,160 --> 00:43:13,040
Speaker 2: proof involved. It changed my perspective on wearing a seat

699
00:43:13,040 --> 00:43:15,719
Speaker 2: buckle seat belt in the back of a car. It

700
00:43:15,800 --> 00:43:20,680
Speaker 2: was just like, exactly what you're describing. Suddenly the whole

701
00:43:20,760 --> 00:43:22,360
Speaker 2: framework completely shifted.

702
00:43:22,560 --> 00:43:25,719
Speaker 1: That's fantastic. That's a great example. And something like that

703
00:43:25,840 --> 00:43:30,440
Speaker 1: is happening, you know, for non political issues, for economic choices,

704
00:43:30,640 --> 00:43:35,520
Speaker 1: for investment decisions, and it happens really fast. So you

705
00:43:35,560 --> 00:43:39,680
Speaker 1: can see a flood of movement towards something or away

706
00:43:39,719 --> 00:43:42,759
Speaker 1: from something, just because people think that other people are

707
00:43:42,840 --> 00:43:43,800
Speaker 1: joining that flood.

708
00:43:44,000 --> 00:43:45,960
Speaker 2: Let's talk a little bit about this book. I'm kind

709
00:43:45,960 --> 00:43:49,120
Speaker 2: of intrigued by the idea that you started writing this

710
00:43:49,200 --> 00:43:52,400
Speaker 2: in the nineteen nineties. Is that possibly correct? That it

711
00:43:52,520 --> 00:43:55,880
Speaker 2: is correct thirty years? I thought you were so prolific,

712
00:43:56,480 --> 00:43:57,120
Speaker 2: Why so long?

713
00:43:57,200 --> 00:43:59,720
Speaker 1: It's a slow burn. This book is a slow burn.

714
00:44:00,520 --> 00:44:03,719
Speaker 1: So I thought the idea of how we decide, how

715
00:44:03,760 --> 00:44:06,600
Speaker 1: we decide. It's one of the most fundamental things of

716
00:44:06,640 --> 00:44:09,440
Speaker 1: all and I thought there should be a book on this,

717
00:44:09,480 --> 00:44:11,480
Speaker 1: and I co authored a paper on it in the

718
00:44:11,600 --> 00:44:16,560
Speaker 1: nineteen nineties, but I never figured it out until yesterday.

719
00:44:17,400 --> 00:44:22,759
Speaker 2: So how has your thinking about decision making evolved over

720
00:44:22,800 --> 00:44:23,400
Speaker 2: that time.

721
00:44:24,719 --> 00:44:28,440
Speaker 1: I think the fundamental idea, which was developed in a

722
00:44:28,520 --> 00:44:34,239
Speaker 1: paper with a philosopher omen Marglie, is that we have

723
00:44:34,400 --> 00:44:37,359
Speaker 1: an identifiable set of strategies. It's going to be very

724
00:44:37,400 --> 00:44:40,920
Speaker 1: intuitive when we're stuck. So we might flip a coin.

725
00:44:42,200 --> 00:44:46,240
Speaker 1: We might decide who's an expert. I'll trust the expert.

726
00:44:46,680 --> 00:44:49,120
Speaker 1: We might decide I'm not going to marry her, I'm

727
00:44:49,160 --> 00:44:52,440
Speaker 1: going to live with her. That's like a really small step.

728
00:44:52,920 --> 00:44:56,080
Speaker 1: We might decide that, you know, I'm just going to

729
00:44:56,120 --> 00:44:58,920
Speaker 1: opt where it's not about flipping a coin. It's not

730
00:44:58,960 --> 00:45:01,480
Speaker 1: like picking flipping a coin. It's like I'm going to

731
00:45:01,560 --> 00:45:04,480
Speaker 1: do something really big, like jump over a chasm.

732
00:45:05,200 --> 00:45:06,120
Speaker 2: Or it might mean moved.

733
00:45:06,440 --> 00:45:08,400
Speaker 1: We might think that we're going to adopt a rule

734
00:45:09,040 --> 00:45:13,759
Speaker 1: no liquor ever except maybe Saturday night. And if you

735
00:45:13,800 --> 00:45:18,520
Speaker 1: think about business decisions, each of these strategies is used

736
00:45:18,680 --> 00:45:22,120
Speaker 1: all the time, sometimes deliberately. The head of a company

737
00:45:22,160 --> 00:45:25,200
Speaker 1: will say, here's our rule, or we'll say, if we're stuck,

738
00:45:25,239 --> 00:45:27,640
Speaker 1: we're going to go to this person, or we'll say,

739
00:45:27,719 --> 00:45:30,680
Speaker 1: you know, it's a coin flip. And we're not as

740
00:45:30,719 --> 00:45:33,360
Speaker 1: disciplined sometimes as we should be in thinking about these

741
00:45:33,400 --> 00:45:37,160
Speaker 1: But that's the basic framework. What I hadn't thought through

742
00:45:37,440 --> 00:45:40,960
Speaker 1: was how do we decide whether to acquire information? How

743
00:45:40,960 --> 00:45:44,319
Speaker 1: did we decide what to believe? How do we think

744
00:45:44,320 --> 00:45:48,720
Speaker 1: about algorithms? How do we think about freedom? And these

745
00:45:49,520 --> 00:45:52,719
Speaker 1: questions which are all basically part of the same thing

746
00:45:53,719 --> 00:45:57,160
Speaker 1: we're stirring around in the head. And I kind of

747
00:45:57,160 --> 00:46:00,360
Speaker 1: figured out at least provisional responses to the question students

748
00:46:00,400 --> 00:46:01,440
Speaker 1: in the course of the book.

749
00:46:01,520 --> 00:46:06,520
Speaker 2: So, opt, delegate, no believe are the four big frameworks.

750
00:46:07,280 --> 00:46:11,480
Speaker 2: But given your background in behavioral finance, let's talk a

751
00:46:11,480 --> 00:46:15,880
Speaker 2: bit about biases. How should we contextualize heuristics that can

752
00:46:16,160 --> 00:46:20,279
Speaker 2: derail our cognitive processes when someone is trying to make

753
00:46:20,320 --> 00:46:23,319
Speaker 2: a rational decision. Maybe they do, maybe they don't.

754
00:46:23,960 --> 00:46:30,000
Speaker 1: Okay, So one bias is present bias where today really

755
00:46:30,080 --> 00:46:34,480
Speaker 1: matters and the future is a foreign country called later Land,

756
00:46:35,000 --> 00:46:37,480
Speaker 1: and we're not sure, we're ever going to visit, and

757
00:46:37,520 --> 00:46:41,920
Speaker 1: that actually has roots in the brain present bias. And

758
00:46:41,960 --> 00:46:45,160
Speaker 1: we know if we're baking investment choices, if we think

759
00:46:45,360 --> 00:46:49,960
Speaker 1: what we want to really maximize is wealth this week,

760
00:46:50,480 --> 00:46:53,680
Speaker 1: that's probably dumb. It's going to produce a lot of problems.

761
00:46:53,719 --> 00:46:56,880
Speaker 1: This is your field, of course, and we might decide

762
00:46:57,000 --> 00:47:00,880
Speaker 1: we're just going to adopt rule for investments which will

763
00:47:01,080 --> 00:47:05,520
Speaker 1: counteract our own present bias. Or we might think in

764
00:47:05,880 --> 00:47:09,640
Speaker 1: state government, let's say that unrealistic optimism is part of

765
00:47:09,680 --> 00:47:13,239
Speaker 1: the human species. Thank goodness for that. If you're being

766
00:47:13,360 --> 00:47:15,880
Speaker 1: chased by a lion, you ought not to think the

767
00:47:15,920 --> 00:47:19,080
Speaker 1: lines faster than I am, I'm going to die soon.

768
00:47:19,200 --> 00:47:22,319
Speaker 1: You ought to think I can really run. That's optimistic,

769
00:47:22,360 --> 00:47:24,239
Speaker 1: it's probably unrealistically optimistic.

770
00:47:24,400 --> 00:47:26,759
Speaker 2: Or just run faster than the guy you w Yeah.

771
00:47:26,480 --> 00:47:30,000
Speaker 1: Completely completely, and then the line will eat That other person,

772
00:47:30,080 --> 00:47:32,640
Speaker 1: who is profoundly to be hoped is not a dear friend.

773
00:47:33,239 --> 00:47:37,439
Speaker 1: So optimistic bias can create problems. So we might think

774
00:47:37,480 --> 00:47:42,640
Speaker 1: that given unrealistic optimism with respect to medical decisions, we're

775
00:47:43,000 --> 00:47:46,920
Speaker 1: just going to rely on the doctor. That's one thing

776
00:47:46,920 --> 00:47:50,000
Speaker 1: you might do. Or we might think, if you're a judge.

777
00:47:50,120 --> 00:47:53,200
Speaker 1: You might think I'm prone to mistakes with respect this

778
00:47:53,280 --> 00:47:56,440
Speaker 1: might be the future. I'm prone to mistakes with respect

779
00:47:56,440 --> 00:47:58,880
Speaker 1: to dealing with certain kinds of people what's call the

780
00:47:59,000 --> 00:48:03,680
Speaker 1: criminal defendants, and sentencing might be biased against one group

781
00:48:03,760 --> 00:48:05,879
Speaker 1: or another. I don't even know. I'm going to rely

782
00:48:05,920 --> 00:48:07,680
Speaker 1: on the algorithm.

783
00:48:07,719 --> 00:48:12,120
Speaker 2: I'm always fascinated by the sentencing studies that show the

784
00:48:12,200 --> 00:48:14,440
Speaker 2: longer judge is sitting on the bench that day, the

785
00:48:14,560 --> 00:48:18,839
Speaker 2: closer we are to lunch, the worse the sentences are.

786
00:48:19,040 --> 00:48:24,560
Speaker 2: It seems almost as if they're not algorithms, they're fallible

787
00:48:24,640 --> 00:48:28,000
Speaker 2: humans making decisions, some of which are not great.

788
00:48:28,239 --> 00:48:31,239
Speaker 1: Yeah, the most fun of these kinds of studies is

789
00:48:31,320 --> 00:48:35,640
Speaker 1: if the judges football team won over the weekend, the

790
00:48:35,719 --> 00:48:38,560
Speaker 1: judge is more lenient on the next day, and then

791
00:48:38,600 --> 00:48:40,600
Speaker 1: it's the favor of football team lost.

792
00:48:41,920 --> 00:48:46,040
Speaker 2: Amazing. So let's talk about some other influences. We've talked

793
00:48:46,080 --> 00:48:51,040
Speaker 2: about social media and mass media, and there's misinformation is ripe,

794
00:48:51,040 --> 00:48:55,360
Speaker 2: there's even propaganda and social networks. How does that impact

795
00:48:55,520 --> 00:49:00,000
Speaker 2: our decision making process, especially if it seems the peace

796
00:49:00,000 --> 00:49:04,080
Speaker 2: people most affected are the least aware of these these

797
00:49:04,680 --> 00:49:07,680
Speaker 2: you know, very very below the radar. Or not so

798
00:49:07,760 --> 00:49:10,719
Speaker 2: below the radar influences.

799
00:49:11,880 --> 00:49:16,680
Speaker 1: This is a fantastic question. And here's something over the

800
00:49:16,800 --> 00:49:20,680
Speaker 1: last maybe fifteen years, when Dick Taylor and I started

801
00:49:20,680 --> 00:49:25,680
Speaker 1: working on nudges, we were and we remain very upbeat

802
00:49:26,120 --> 00:49:32,680
Speaker 1: about the potential use of GPS like things to help

803
00:49:32,800 --> 00:49:36,799
Speaker 1: overcome people's biases. When I say GPS like things, I

804
00:49:36,840 --> 00:49:40,040
Speaker 1: mean a GPS device. It's a nudge. It helps you

805
00:49:40,160 --> 00:49:42,480
Speaker 1: get you where you want to go. It gives you

806
00:49:42,520 --> 00:49:45,440
Speaker 1: the best route. If you don't like what it says,

807
00:49:45,480 --> 00:49:49,200
Speaker 1: you can ignore it. So it's completely freedom producing or

808
00:49:49,239 --> 00:49:53,759
Speaker 1: freedom maintaining. And then there are other things like a

809
00:49:53,840 --> 00:49:57,680
Speaker 1: package that says this has shrimp in it. Personally I am

810
00:49:57,640 --> 00:50:00,840
Speaker 1: allergic to shrimp, so hooray for that disclosure. Or you

811
00:50:00,880 --> 00:50:04,160
Speaker 1: can have something that tells you a warning about side

812
00:50:04,200 --> 00:50:07,480
Speaker 1: effects and they might be relevant to your choices. These

813
00:50:07,520 --> 00:50:11,960
Speaker 1: are all nudges, okay, and they are designed to help

814
00:50:12,280 --> 00:50:17,719
Speaker 1: people deal with their cognitive limits. They might involve a bias,

815
00:50:17,880 --> 00:50:21,400
Speaker 1: they might involve an absence of information, but we know

816
00:50:21,719 --> 00:50:24,600
Speaker 1: and this is what at least I wasn't sufficiently alert

817
00:50:24,680 --> 00:50:28,480
Speaker 1: to in two thousand and eight that self interested or

818
00:50:28,600 --> 00:50:35,719
Speaker 1: malevolent types can use behavioral biases to manipulate people. So

819
00:50:35,760 --> 00:50:39,000
Speaker 1: you might use present bias to try to get people

820
00:50:39,040 --> 00:50:42,960
Speaker 1: to buy some product where the long term economic effects

821
00:50:43,000 --> 00:50:45,960
Speaker 1: are horrifying though the first week is going to be

822
00:50:45,960 --> 00:50:49,799
Speaker 1: pretty good. Or you might get people to buy some

823
00:50:50,040 --> 00:50:53,680
Speaker 1: product where you'd have to be crazy optimistic to think

824
00:50:53,680 --> 00:50:56,319
Speaker 1: it's a sensible thing to do because the risk associated

825
00:50:56,360 --> 00:50:59,360
Speaker 1: with it or horrible, or and I think this is

826
00:50:59,400 --> 00:51:03,240
Speaker 1: the most of all, you might use people's limited attention

827
00:51:03,920 --> 00:51:06,719
Speaker 1: to get them, let's say, to opt into something which

828
00:51:06,800 --> 00:51:08,640
Speaker 1: is going to be really hard to opt out of,

829
00:51:09,080 --> 00:51:13,360
Speaker 1: and once they've opted into it, they're stuck with something

830
00:51:13,400 --> 00:51:17,640
Speaker 1: that's going to be very expensive and not beneficial. So

831
00:51:18,000 --> 00:51:23,240
Speaker 1: the manipulation of people, we're just talking about the economic

832
00:51:23,280 --> 00:51:29,439
Speaker 1: sphere right now poses a very serious challenge, and social media, etc.

833
00:51:30,280 --> 00:51:35,640
Speaker 1: Make this unprecedentedly doable. I've worked with private sector entities

834
00:51:35,719 --> 00:51:40,400
Speaker 1: which are trying to use behavioral stuff to improve outcomes

835
00:51:40,400 --> 00:51:44,920
Speaker 1: for their customers and their investors, and that's fantastic. But

836
00:51:45,080 --> 00:51:49,160
Speaker 1: there are others who were trying to improve outcomes for themselves,

837
00:51:49,239 --> 00:51:52,120
Speaker 1: which is also fantastic, but not if it's at the

838
00:51:52,160 --> 00:51:53,640
Speaker 1: expense of the most vulnerable.

839
00:51:54,080 --> 00:51:59,040
Speaker 2: So you mentioned present bias. I love this Danny Kahneman quote,

840
00:51:59,320 --> 00:52:01,879
Speaker 2: nothing in life life is as important as you think

841
00:52:01,920 --> 00:52:06,600
Speaker 2: it is when you're thinking about it. That really says everything.

842
00:52:06,840 --> 00:52:11,600
Speaker 2: Talk about present bias. In the moment, it's very hard

843
00:52:11,680 --> 00:52:16,520
Speaker 2: to let anything else come into the picture. How should

844
00:52:16,560 --> 00:52:20,520
Speaker 2: we act around that and how should public policy be

845
00:52:20,600 --> 00:52:25,160
Speaker 2: set up to not let people's wetwear be taken advantage?

846
00:52:25,400 --> 00:52:29,880
Speaker 1: That's fantastic. So the only exception to Connomon's phrase nothing

847
00:52:29,920 --> 00:52:31,920
Speaker 1: in life is as important as you think it is

848
00:52:31,960 --> 00:52:35,560
Speaker 1: when you're thinking about it is that statement. That statement

849
00:52:35,719 --> 00:52:38,000
Speaker 1: is as important as it is when you're thinking about it.

850
00:52:39,880 --> 00:52:45,799
Speaker 1: So it might be that policy makers can put on

851
00:52:45,880 --> 00:52:51,120
Speaker 1: people's viewscreens things that they're not thinking about. So let's

852
00:52:51,120 --> 00:52:54,600
Speaker 1: say you're buying some product and that there are add

853
00:52:54,640 --> 00:52:59,040
Speaker 1: on fees of various kinds that are findable but not

854
00:52:59,080 --> 00:53:03,000
Speaker 1: really there aren't thinking about them. To put those add

855
00:53:03,000 --> 00:53:06,840
Speaker 1: on prices on people's viewscreens is a really good idea

856
00:53:07,480 --> 00:53:12,279
Speaker 1: for companies actually to do that and use competition to

857
00:53:12,320 --> 00:53:16,920
Speaker 1: promote fuller clarity on the part of consumers. That's a

858
00:53:16,960 --> 00:53:21,040
Speaker 1: really good idea. I think for securities, the securities laws,

859
00:53:21,080 --> 00:53:24,200
Speaker 1: there's a lot to say about them, but in so

860
00:53:24,360 --> 00:53:28,680
Speaker 1: far as they're trying to prevent people from falling victim

861
00:53:28,800 --> 00:53:33,719
Speaker 1: to present bias or limited attention or on realistic optimism,

862
00:53:34,280 --> 00:53:36,080
Speaker 1: that's an extremely worthy.

863
00:53:35,719 --> 00:53:40,880
Speaker 2: Goal, really quite intriguing. So I love this line in

864
00:53:40,920 --> 00:53:45,440
Speaker 2: the book get drunk on wine poetry or virtue in

865
00:53:46,080 --> 00:53:50,080
Speaker 2: Decisions about decisions. Tell us what that means, wine poetry

866
00:53:50,200 --> 00:53:50,760
Speaker 2: or virtue.

867
00:53:50,840 --> 00:53:54,239
Speaker 1: Okay, So that's from a poem by Bodelaire, which is

868
00:53:54,280 --> 00:54:01,200
Speaker 1: the improbable spirit guide of the book, And the title

869
00:54:01,200 --> 00:54:05,640
Speaker 1: of Bodelayer's poem is get Drunk, and that for a

870
00:54:05,680 --> 00:54:08,360
Speaker 1: law professor to celebrate a poem with that title is

871
00:54:08,400 --> 00:54:13,640
Speaker 1: a little unlikely, but I'm going to own it. Where

872
00:54:13,719 --> 00:54:18,319
Speaker 1: what bodal Layer says by get Drunk is basically, you know,

873
00:54:18,440 --> 00:54:23,040
Speaker 1: take life by the horns and be thrilled by it.

874
00:54:23,080 --> 00:54:28,720
Speaker 1: And there's also something about human diversity that what makes

875
00:54:28,760 --> 00:54:34,239
Speaker 1: you get thrilled. Maybe wine good. Don't over use it,

876
00:54:34,760 --> 00:54:37,720
Speaker 1: but go for it if that's what it gets you thrilled,

877
00:54:37,880 --> 00:54:41,200
Speaker 1: or if it's poetry, go for that, or if it's

878
00:54:41,280 --> 00:54:47,360
Speaker 1: virtue good works, that's admirable, of course, and if it

879
00:54:47,440 --> 00:54:51,200
Speaker 1: also is for you like wine, hooray. Now, of course

880
00:54:51,239 --> 00:54:54,160
Speaker 1: we want to say I think that maybe a little

881
00:54:54,160 --> 00:54:55,680
Speaker 1: more in the way of good works and a little

882
00:54:55,760 --> 00:54:58,600
Speaker 1: less in the way of wine is a good thing.

883
00:54:58,680 --> 00:55:02,160
Speaker 1: But that's a buzz kill on my part. And the

884
00:55:02,200 --> 00:55:04,799
Speaker 1: point of this part of the book is when we're

885
00:55:04,800 --> 00:55:10,440
Speaker 1: making about decisions, about decisions, think about what makes life fabulous.

886
00:55:11,000 --> 00:55:15,120
Speaker 1: That's really important. And I think the behavioral types, including yours,

887
00:55:15,160 --> 00:55:20,720
Speaker 1: truly often maybe overweight a little bit what makes life long,

888
00:55:21,440 --> 00:55:25,480
Speaker 1: and underweight a little bit what makes life fabulous. So

889
00:55:25,560 --> 00:55:32,040
Speaker 1: the first generation of behavioral work is really healthier, wealthier, safer,

890
00:55:33,400 --> 00:55:38,239
Speaker 1: more prosperous, and those are really important, but also kind

891
00:55:38,320 --> 00:55:44,160
Speaker 1: of better days. And Baudelaire get drunk. He's all over that.

892
00:55:44,600 --> 00:55:52,640
Speaker 2: So I interpreted Baudelaire as consumption, art, and intellect. Those

893
00:55:52,680 --> 00:55:55,840
Speaker 2: are the three broad topics which seem to cover a

894
00:55:55,440 --> 00:56:00,320
Speaker 2: lot of human behavior. But let's stick with happiness. You

895
00:56:00,800 --> 00:56:04,920
Speaker 2: reference some surveys that show people are less happy than ever,

896
00:56:05,640 --> 00:56:08,799
Speaker 2: even though by any objective measure, whether you're looking at

897
00:56:08,800 --> 00:56:13,560
Speaker 2: crime or healthcare or longevity except for the past couple

898
00:56:13,560 --> 00:56:19,960
Speaker 2: of years post pandemic, or poverty, or literacy, or just

899
00:56:20,000 --> 00:56:25,120
Speaker 2: go down the list by just about any measure, Americans

900
00:56:25,200 --> 00:56:27,680
Speaker 2: and humanity as a whole are better off than they

901
00:56:27,719 --> 00:56:32,359
Speaker 2: were twenty forty sixty years ago. Why do surveys say

902
00:56:32,440 --> 00:56:35,920
Speaker 2: people are unhappy? Is there a problem with this survey?

903
00:56:36,560 --> 00:56:39,239
Speaker 2: Is it twenty four to seven social media? Or do

904
00:56:39,320 --> 00:56:42,160
Speaker 2: we just not know how good it is?

905
00:56:42,719 --> 00:56:48,719
Speaker 1: It's a fantastic question. So let's think about two things. First,

906
00:56:49,960 --> 00:56:53,600
Speaker 1: day to day experience. Are people thinking that was a

907
00:56:53,640 --> 00:56:57,920
Speaker 1: great day, Monday was terrific, Tuesday was good, Wednesday not

908
00:56:58,080 --> 00:57:02,120
Speaker 1: so much. That's one thing. The other is not day

909
00:57:02,160 --> 00:57:04,840
Speaker 1: to day experience, but what kind of lives are people having?

910
00:57:05,320 --> 00:57:09,240
Speaker 1: Are they going to the doctor a lot? Are they learning?

911
00:57:09,680 --> 00:57:14,160
Speaker 1: Are they being treated with respect? People care about two

912
00:57:14,200 --> 00:57:17,960
Speaker 1: things that happiness doesn't capture. One is how meaningful their

913
00:57:17,960 --> 00:57:21,640
Speaker 1: life is, and the other is how much psychological richness

914
00:57:21,760 --> 00:57:24,440
Speaker 1: or let's call it diversity in their life they have.

915
00:57:24,760 --> 00:57:26,840
Speaker 1: So they might have a meaningful, happy life, but they

916
00:57:26,920 --> 00:57:28,960
Speaker 1: might be doing the same thing over and over again.

917
00:57:29,240 --> 00:57:31,680
Speaker 1: People don't like that. A lot of people don't like

918
00:57:31,760 --> 00:57:35,040
Speaker 1: that so much. They want to do something else. So

919
00:57:35,680 --> 00:57:41,560
Speaker 1: happiness meaning psychological richness, and it's important to say that

920
00:57:41,640 --> 00:57:44,280
Speaker 1: day to day happiness is really important. But it isn't

921
00:57:44,320 --> 00:57:49,680
Speaker 1: everything now with the surveys suggesting that some people and

922
00:57:49,800 --> 00:57:53,520
Speaker 1: some populations, maybe America is less happy now than it

923
00:57:53,680 --> 00:57:57,000
Speaker 1: was a certain point. I don't know whether it's an

924
00:57:57,000 --> 00:58:04,600
Speaker 1: expressive statement that pandemic time terrible or political polarization I'm

925
00:58:04,640 --> 00:58:09,200
Speaker 1: not liking that, or whether instead it's actually my life

926
00:58:09,280 --> 00:58:11,600
Speaker 1: isn't so good. So I don't think we've gotten to

927
00:58:11,680 --> 00:58:15,800
Speaker 1: the bottom of what the data actually shows about the

928
00:58:15,880 --> 00:58:19,720
Speaker 1: happiness part. If it is the case that people actually

929
00:58:19,800 --> 00:58:24,040
Speaker 1: are less happy, if that's true, that's a very serious,

930
00:58:26,280 --> 00:58:29,280
Speaker 1: not good thing, and we want to figure out why.

931
00:58:29,320 --> 00:58:32,360
Speaker 1: When I was in the White House under President Obama,

932
00:58:32,440 --> 00:58:36,440
Speaker 1: we did as the government always does, do cost benefit reports,

933
00:58:36,800 --> 00:58:40,960
Speaker 1: cost and benefits of regulations, and we added stuff on

934
00:58:41,800 --> 00:58:46,120
Speaker 1: happiness unsubjective well being. In the UK government, they're very

935
00:58:46,120 --> 00:58:50,600
Speaker 1: concerned about this, and I do think it's an important

936
00:58:50,600 --> 00:58:53,960
Speaker 1: field of endeavor to try to figure out are people

937
00:58:54,240 --> 00:58:57,680
Speaker 1: thinking life is great or is not so great? And

938
00:58:57,760 --> 00:59:02,680
Speaker 1: is that translated into depression and anxiety, etc.

939
00:59:03,520 --> 00:59:06,040
Speaker 2: So let me push back a little bit on the

940
00:59:06,120 --> 00:59:10,040
Speaker 2: use of surveys and Amazon's mechanical turk and all these things.

941
00:59:10,560 --> 00:59:15,000
Speaker 2: So the granddaddy of this in my field is when

942
00:59:15,120 --> 00:59:18,439
Speaker 2: you are setting up a portfolio for an investor, Hey

943
00:59:18,480 --> 00:59:21,280
Speaker 2: tell us about your risk tolerance. So you conservative, are

944
00:59:21,320 --> 00:59:26,520
Speaker 2: you moderate? Are you aggressive? What's your investment posture? And

945
00:59:26,920 --> 00:59:30,680
Speaker 2: whatever they tell you is a lie because all they're

946
00:59:30,720 --> 00:59:33,480
Speaker 2: really telling you is here's how the market has done

947
00:59:33,480 --> 00:59:36,120
Speaker 2: over the past ninety days. And if it's gone down,

948
00:59:36,360 --> 00:59:39,640
Speaker 2: I'm very risk averse, and if it's gone up, I'm

949
00:59:39,760 --> 00:59:43,760
Speaker 2: very aggressive. Every time I see a survey, I can't

950
00:59:43,760 --> 00:59:45,480
Speaker 2: help but think how much you're going to spend on

951
00:59:45,560 --> 00:59:49,120
Speaker 2: Christmas gifts this year? What is the direction of the economy?

952
00:59:49,320 --> 00:59:51,080
Speaker 2: Are we on the right track or on the wrong track.

953
00:59:51,240 --> 00:59:55,520
Speaker 2: I love the surveys right after the presidential election, where

954
00:59:55,640 --> 00:59:59,320
Speaker 2: what's the city of the economy? Suddenly the Democrats were here,

955
00:59:59,360 --> 01:00:01,920
Speaker 2: the Republicans where there, their guy loses it flips and

956
01:00:01,960 --> 01:00:05,440
Speaker 2: then the next election the same thing happens. So what

957
01:00:05,600 --> 01:00:09,400
Speaker 2: is the value of surveys when people really don't know

958
01:00:09,480 --> 01:00:13,400
Speaker 2: what they think, hardly know what they feel, and have

959
01:00:13,480 --> 01:00:15,160
Speaker 2: no idea what's going to happen in the future.

960
01:00:15,800 --> 01:00:19,680
Speaker 1: That's also a fantastic question. I'm doing surveys right now

961
01:00:19,800 --> 01:00:23,640
Speaker 1: that is right now on whether people like algorithms, And

962
01:00:23,680 --> 01:00:26,720
Speaker 1: so I'm asking people, would you choose an algorithm or

963
01:00:26,800 --> 01:00:30,000
Speaker 1: a person with respect to an investment decision, or an

964
01:00:30,040 --> 01:00:33,040
Speaker 1: algorithm or a person with respect to a vacation where

965
01:00:33,040 --> 01:00:35,480
Speaker 1: you're going to go, or algorithm or a person with

966
01:00:35,560 --> 01:00:38,600
Speaker 1: respect to health decisions? And I'll tell you what makes

967
01:00:38,640 --> 01:00:42,720
Speaker 1: me think that the very preliminary results you're gonna be

968
01:00:42,720 --> 01:00:46,760
Speaker 1: the first person to hear it are not useless. That

969
01:00:46,960 --> 01:00:51,400
Speaker 1: if you tell people things about the algorithm which give

970
01:00:51,480 --> 01:00:54,360
Speaker 1: people clarity on the data on which the algorithm is

971
01:00:54,400 --> 01:00:56,200
Speaker 1: relying and like there's a lot of it.

972
01:00:56,080 --> 01:00:59,280
Speaker 2: Like the MRI or cat skins that the algos clearly

973
01:00:59,320 --> 01:01:00,400
Speaker 2: do better than the humans.

974
01:01:00,480 --> 01:01:02,960
Speaker 1: Yeah, it's in the direction of that what I did.

975
01:01:03,440 --> 01:01:07,760
Speaker 1: Then the percentage of people who embrace the algorithm jumps dramatically.

976
01:01:08,440 --> 01:01:11,200
Speaker 1: And if you tell people things about the human alternative,

977
01:01:11,640 --> 01:01:13,720
Speaker 1: like this is a doctor who's been a specialist in

978
01:01:13,800 --> 01:01:17,440
Speaker 1: this for thirty years, then the interest in the human

979
01:01:17,480 --> 01:01:22,200
Speaker 1: being increases significantly. So the direction of the results in

980
01:01:22,240 --> 01:01:26,080
Speaker 1: the survey about which you would rely is consistent with

981
01:01:26,160 --> 01:01:30,920
Speaker 1: thinking people are attentive to whether the algorithm is just

982
01:01:31,000 --> 01:01:33,840
Speaker 1: a thing or whether it's got a terrific data set,

983
01:01:34,040 --> 01:01:37,040
Speaker 1: and whether the person is just a person or someone

984
01:01:37,040 --> 01:01:40,600
Speaker 1: who has thirty years of experience in the vacation sector.

985
01:01:40,880 --> 01:01:43,920
Speaker 1: Let's say, so that survey and stop of mind for

986
01:01:43,960 --> 01:01:49,400
Speaker 1: me because I'm working on it now, seems instructive and TBD.

987
01:01:49,600 --> 01:01:51,480
Speaker 1: This might be a book in the fullness of time.

988
01:01:52,680 --> 01:01:53,960
Speaker 2: I would expect nothing less that.

989
01:01:54,240 --> 01:01:59,000
Speaker 1: With respect to happiness, let's consider three things, shall we

990
01:01:59,520 --> 01:02:03,480
Speaker 1: efforts to measure people's experience in real time, So, like,

991
01:02:03,640 --> 01:02:06,040
Speaker 1: on a scale of one to ten, right now, I'm

992
01:02:06,280 --> 01:02:10,080
Speaker 1: approximately ten because I'm really enjoying talking to this, talking

993
01:02:10,080 --> 01:02:13,280
Speaker 1: about this. I find that ten, of course I would.

994
01:02:13,320 --> 01:02:16,920
Speaker 1: But I find people's answers how happy are you right now?

995
01:02:17,280 --> 01:02:20,080
Speaker 1: How anxious are you? How stressed I are you? How

996
01:02:20,280 --> 01:02:24,920
Speaker 1: angry are you? Angry? Zero, stressed me right now? Two,

997
01:02:25,560 --> 01:02:26,840
Speaker 1: anxious me right now?

998
01:02:26,880 --> 01:02:27,280
Speaker 2: One?

999
01:02:27,720 --> 01:02:31,439
Speaker 1: And these are all credible in real time. That's one

1000
01:02:31,480 --> 01:02:34,520
Speaker 1: way of doing it that seems pretty good at getting

1001
01:02:34,520 --> 01:02:36,880
Speaker 1: how people are. If people are in the midst of

1002
01:02:36,960 --> 01:02:40,920
Speaker 1: dealing with a really angry and difficult young child, people

1003
01:02:41,600 --> 01:02:45,479
Speaker 1: give answers, I'm really not having a great time right now,

1004
01:02:45,520 --> 01:02:49,120
Speaker 1: and that's credible about their emotional state. Then there's at

1005
01:02:49,160 --> 01:02:52,160
Speaker 1: the opposite spectrum, how satisfied are you with your life?

1006
01:02:53,160 --> 01:02:56,440
Speaker 1: And these are crude because it might be that if

1007
01:02:56,480 --> 01:02:59,040
Speaker 1: people had like a really good date the night before,

1008
01:02:59,280 --> 01:03:05,360
Speaker 1: they'll say and so. But there is stability on these things,

1009
01:03:05,600 --> 01:03:09,400
Speaker 1: and they're within nation differences that are interesting and seems

1010
01:03:09,400 --> 01:03:12,480
Speaker 1: to be telling us something. So there's a lot of

1011
01:03:12,520 --> 01:03:16,480
Speaker 1: work on whether life satisfaction is kind of crude but

1012
01:03:16,600 --> 01:03:20,680
Speaker 1: directionally informative. I tend to think yes. And then there

1013
01:03:20,680 --> 01:03:24,160
Speaker 1: are things in between where you ask people at the

1014
01:03:24,240 --> 01:03:27,160
Speaker 1: end of the day, and Danny Konneman is pioneered this

1015
01:03:27,400 --> 01:03:30,680
Speaker 1: called the day reconstruction method. You ask people how were you?

1016
01:03:31,200 --> 01:03:34,360
Speaker 1: This is less demanding for the experimenter than trying to

1017
01:03:34,360 --> 01:03:37,720
Speaker 1: ask people every second how are you? And if you

1018
01:03:37,760 --> 01:03:39,880
Speaker 1: ask people that enough, they're going to say, I'm really

1019
01:03:39,960 --> 01:03:43,160
Speaker 1: irritated because you keep asking me how I am? So

1020
01:03:43,400 --> 01:03:45,520
Speaker 1: condom an asked at the end of the day, how

1021
01:03:45,560 --> 01:03:47,920
Speaker 1: were you when you were taking care of your kids?

1022
01:03:47,960 --> 01:03:50,600
Speaker 1: How were you when you were on social media? How

1023
01:03:50,600 --> 01:03:52,560
Speaker 1: were you when you were at work? How were you

1024
01:03:52,600 --> 01:03:55,920
Speaker 1: when you were commuting? And the results are pretty credible.

1025
01:03:56,240 --> 01:03:59,640
Speaker 1: People really don't like commuting, and they really do like

1026
01:03:59,720 --> 01:04:03,480
Speaker 1: let's intimate relations.

1027
01:04:02,200 --> 01:04:03,200
Speaker 2: To say the very least.

1028
01:04:03,320 --> 01:04:07,400
Speaker 1: That people are very, very positive about that.

1029
01:04:07,400 --> 01:04:10,600
Speaker 2: That's quite fascinating, Which leads us to talk about the

1030
01:04:10,640 --> 01:04:14,440
Speaker 2: book you wrote on Star Wars, The World according to

1031
01:04:14,520 --> 01:04:19,520
Speaker 2: Star Wars. This became a New York Times bestseller, great reviews.

1032
01:04:20,120 --> 01:04:23,360
Speaker 2: What led Harvard law professor to write a book on

1033
01:04:23,400 --> 01:04:24,080
Speaker 2: Star Wars?

1034
01:04:24,560 --> 01:04:29,160
Speaker 1: My son, who was six or seven, got obsessed with

1035
01:04:29,280 --> 01:04:33,280
Speaker 1: Star Wars and we watched it together, and I thought,

1036
01:04:33,680 --> 01:04:35,840
Speaker 1: you know, I like Star Wars. At that point, I

1037
01:04:35,880 --> 01:04:38,920
Speaker 1: wasn't crazy about Star Wars, and I thought, what is

1038
01:04:38,960 --> 01:04:43,600
Speaker 1: it about Star Wars so that my young boy would

1039
01:04:43,640 --> 01:04:47,000
Speaker 1: go nuts for it? When it's a long time ago?

1040
01:04:47,680 --> 01:04:53,400
Speaker 1: And so I got focused on its enduring appeal, and

1041
01:04:53,680 --> 01:04:55,880
Speaker 1: then I thought the idea of writing a book about

1042
01:04:55,920 --> 01:05:00,400
Speaker 1: it was too crazy not to go forth with No

1043
01:05:00,560 --> 01:05:02,920
Speaker 1: publisher for a long time had even a little bit

1044
01:05:03,000 --> 01:05:06,320
Speaker 1: interested at really, so I almost thought I was going

1045
01:05:06,400 --> 01:05:10,440
Speaker 1: to publish it myself as something. I talked to my

1046
01:05:10,720 --> 01:05:13,560
Speaker 1: literary agent about publishing myself, which I've never done before,

1047
01:05:13,640 --> 01:05:15,920
Speaker 1: because I enjoyed it so much. And then at the

1048
01:05:16,160 --> 01:05:20,400
Speaker 1: last minute, a prominent publisher thought, we'll give this one

1049
01:05:20,440 --> 01:05:20,840
Speaker 1: a try.

1050
01:05:21,760 --> 01:05:26,280
Speaker 2: We'll circle back to that concept of people in industries

1051
01:05:26,320 --> 01:05:29,360
Speaker 2: not knowing what works. But right in the beginning of

1052
01:05:29,400 --> 01:05:32,200
Speaker 2: the book you drop a number that is mind blowing.

1053
01:05:32,760 --> 01:05:37,720
Speaker 2: The Star Wars franchise has earned forty two billion dollars worldwide.

1054
01:05:38,160 --> 01:05:41,600
Speaker 2: That's an insane number. How has a movie earned that

1055
01:05:41,760 --> 01:05:42,400
Speaker 2: much money?

1056
01:05:43,160 --> 01:05:44,760
Speaker 1: It's probably a lot higher now.

1057
01:05:45,120 --> 01:05:47,840
Speaker 2: And the well you have the Mandalorian and Boba Fett

1058
01:05:47,880 --> 01:05:52,960
Speaker 2: and all of the streaming versions and countless countless animated things,

1059
01:05:53,960 --> 01:06:00,640
Speaker 2: plus the Disney rides. It really is its own industry completely.

1060
01:06:00,880 --> 01:06:05,400
Speaker 1: And one thing is that success breeds success. The other

1061
01:06:05,480 --> 01:06:10,560
Speaker 1: thing is that it's amazing. So the George Lucaswans especially,

1062
01:06:10,800 --> 01:06:13,440
Speaker 1: I say, apologies, Disney.

1063
01:06:13,040 --> 01:06:15,680
Speaker 2: People, You're okay, You're right with that, You're okay with that,

1064
01:06:16,320 --> 01:06:17,320
Speaker 2: and thank you for that.

1065
01:06:17,520 --> 01:06:21,560
Speaker 1: And you know, he did something incredible, so it had

1066
01:06:21,600 --> 01:06:26,360
Speaker 1: a foundation, but he also benefited from a lot of

1067
01:06:27,520 --> 01:06:29,840
Speaker 1: serendipity that helped.

1068
01:06:30,040 --> 01:06:33,080
Speaker 2: So let's talk a little bit about a concept I

1069
01:06:33,120 --> 01:06:37,120
Speaker 2: love from William Goldman, who wrote Princess Bride and he

1070
01:06:37,240 --> 01:06:40,160
Speaker 2: was the script doctor on All the President's Men and

1071
01:06:40,200 --> 01:06:43,240
Speaker 2: Butch casting a Sundance Kid. He just a legend in

1072
01:06:43,280 --> 01:06:47,960
Speaker 2: Star Wars and his concept is nobody knows anything, certainly

1073
01:06:48,000 --> 01:06:51,840
Speaker 2: not about the future, about what might resonate with the public.

1074
01:06:52,120 --> 01:06:55,200
Speaker 2: All the studios originally passed on Star Wars, they passed

1075
01:06:55,240 --> 01:06:59,040
Speaker 2: on Raiders of the Lost Arc, almost all the publishers

1076
01:06:59,080 --> 01:07:04,000
Speaker 2: rejected Rowling. You referenced the Sugarman documentary, which was really

1077
01:07:04,080 --> 01:07:08,120
Speaker 2: quite fascinating. So it really leads the question what makes

1078
01:07:08,720 --> 01:07:12,640
Speaker 2: a form of entertainment have this sort of cultural resonance.

1079
01:07:12,680 --> 01:07:17,800
Speaker 2: You mentioned Lucas got lucky. Still, it's more than just

1080
01:07:17,920 --> 01:07:20,320
Speaker 2: dumb luck. There's got to be some level of quality.

1081
01:07:20,320 --> 01:07:21,320
Speaker 2: There has to be great.

1082
01:07:21,520 --> 01:07:24,439
Speaker 1: So another example, I'm writing a book right now called

1083
01:07:24,520 --> 01:07:28,040
Speaker 1: How to Become Famous, and it's about exactly this, and

1084
01:07:28,400 --> 01:07:31,560
Speaker 1: it was inspired by The Beatles, where the Beatles. Everybody

1085
01:07:31,600 --> 01:07:35,440
Speaker 1: turned down to the Beatles. They wrote letters to Brian Epstein,

1086
01:07:35,520 --> 01:07:38,480
Speaker 1: the Beatles guy agents, saying the boys.

1087
01:07:38,240 --> 01:07:40,080
Speaker 2: Won't go Guitar music is over.

1088
01:07:40,400 --> 01:07:45,400
Speaker 1: Yeah, and the Beatles themselves said, you know, we're in

1089
01:07:45,440 --> 01:07:48,800
Speaker 1: big trouble. We can't get a record deal. They became

1090
01:07:48,800 --> 01:07:53,720
Speaker 1: the Beatles. Did they come close to failing? Maybe? Okay,

1091
01:07:53,800 --> 01:07:57,800
Speaker 1: So clearly you're right you need quality. But consider the

1092
01:07:57,880 --> 01:08:01,960
Speaker 1: following fact that John Keats often thought to be the

1093
01:08:01,960 --> 01:08:05,240
Speaker 1: most beautiful poet when the English language died at the

1094
01:08:05,280 --> 01:08:07,479
Speaker 1: age of twenty five. He was very ambitious. He thought

1095
01:08:07,480 --> 01:08:11,000
Speaker 1: he failed, and he put on his grave something like

1096
01:08:11,120 --> 01:08:14,840
Speaker 1: he whose life was written in water. And Jane Austen,

1097
01:08:15,000 --> 01:08:18,400
Speaker 1: maybe the most beloved novelist, was not thought to be

1098
01:08:18,479 --> 01:08:20,720
Speaker 1: the greatest novelist of her time. She wasn't thought to

1099
01:08:20,720 --> 01:08:24,080
Speaker 1: be the greatest female novelist of her time. How she

1100
01:08:24,240 --> 01:08:29,599
Speaker 1: became Jane Austen is very complicated. The story, the story

1101
01:08:29,640 --> 01:08:34,559
Speaker 1: of John Keats and Jane Austen is across generations. I

1102
01:08:34,600 --> 01:08:39,680
Speaker 1: think the story of the Beatles and Star Wars within

1103
01:08:39,800 --> 01:08:44,720
Speaker 1: a compressed period where something catches a wave. Now it

1104
01:08:44,720 --> 01:08:47,120
Speaker 1: has to be great to catch a wave. If it's

1105
01:08:47,479 --> 01:08:49,880
Speaker 1: just someone who doesn't know how to serve, they're going

1106
01:08:49,960 --> 01:08:53,000
Speaker 1: to fall, so it has to be great. But what

1107
01:08:53,120 --> 01:08:56,080
Speaker 1: happened with Star Wars. We can talk a bit about

1108
01:08:56,120 --> 01:08:59,639
Speaker 1: the merits, but I think what really happened was social

1109
01:08:59,720 --> 01:09:03,519
Speaker 1: infa luences, which is not to diminish the amazingness of

1110
01:09:03,760 --> 01:09:07,639
Speaker 1: the Star Wars movies. But people wanted to go see

1111
01:09:07,680 --> 01:09:10,479
Speaker 1: Star Wars because everyone was going to see Star Wars,

1112
01:09:10,840 --> 01:09:14,800
Speaker 1: and that happened early on so that people thought not

1113
01:09:14,960 --> 01:09:18,120
Speaker 1: to see Star Wars is to miss out. It's like,

1114
01:09:18,160 --> 01:09:20,680
Speaker 1: who do I think I am on this earth not

1115
01:09:20,760 --> 01:09:23,439
Speaker 1: to go see Star Wars. I remember that, by the way,

1116
01:09:24,240 --> 01:09:29,200
Speaker 1: And that wasn't because it was fantastic, though it was fantastic.

1117
01:09:29,240 --> 01:09:32,360
Speaker 1: It was because other people thought it was fantastic. Taylor

1118
01:09:32,400 --> 01:09:35,560
Speaker 1: Swift is a correct example. I think Taylor Swift is

1119
01:09:35,600 --> 01:09:40,320
Speaker 1: completely amazing, but her amazingness does not account for the

1120
01:09:40,360 --> 01:09:44,160
Speaker 1: fact that she's so famous. It's that people love her,

1121
01:09:44,680 --> 01:09:47,840
Speaker 1: and even people who don't love her are interested in

1122
01:09:47,880 --> 01:09:50,880
Speaker 1: her or pretend to love her. I'm here to say

1123
01:09:50,920 --> 01:09:53,120
Speaker 1: I'm not pretending to love her. I really loved her.

1124
01:09:53,320 --> 01:09:56,080
Speaker 1: I thought her music was great even before she was

1125
01:09:56,400 --> 01:09:59,519
Speaker 1: quite what she is now, because Neil Young, who's one

1126
01:09:59,560 --> 01:10:02,040
Speaker 1: of my hero said, Taylor Swift is the real deal,

1127
01:10:02,360 --> 01:10:04,160
Speaker 1: and I thought, I have to listen to Taylor Swift.

1128
01:10:05,320 --> 01:10:09,120
Speaker 1: So this is all around us, and there are people

1129
01:10:09,200 --> 01:10:14,160
Speaker 1: who are not like George Lucas, or not like Taylor Swift,

1130
01:10:14,240 --> 01:10:18,240
Speaker 1: or not like the Beatles, who maybe were about as amazing,

1131
01:10:18,880 --> 01:10:22,599
Speaker 1: but something didn't happen for them, and we've never heard

1132
01:10:22,640 --> 01:10:26,879
Speaker 1: of them, or we will hear of them after Tomorrow.

1133
01:10:27,720 --> 01:10:31,559
Speaker 2: There's a fascinating section in Derek Thompson's book How It's

1134
01:10:31,600 --> 01:10:37,040
Speaker 2: Happened about how the Impressionists were essentially more or less ignored.

1135
01:10:37,280 --> 01:10:39,559
Speaker 2: I think vango and never sold a painting in his lifetime,

1136
01:10:39,960 --> 01:10:44,320
Speaker 2: but one of their members, who came from wealthy family,

1137
01:10:45,040 --> 01:10:48,160
Speaker 2: left a whole run of these Impressionist paintings with the

1138
01:10:48,479 --> 01:10:52,000
Speaker 2: edict that left it to the French government and this

1139
01:10:52,120 --> 01:10:54,160
Speaker 2: has to be displayed on the museum and if not,

1140
01:10:54,560 --> 01:10:58,800
Speaker 2: you can't have them, And very unhappily the French government did,

1141
01:10:58,920 --> 01:11:03,120
Speaker 2: and suddenly it became a sensation. But for that who

1142
01:11:03,160 --> 01:11:06,160
Speaker 2: knows money man a Picero go down. The whole list

1143
01:11:06,920 --> 01:11:09,280
Speaker 2: may not be part of the pantheon that we look

1144
01:11:09,280 --> 01:11:10,640
Speaker 2: at today completely.

1145
01:11:10,960 --> 01:11:13,800
Speaker 1: I love Derek Thompson's book and I think that's a

1146
01:11:13,840 --> 01:11:17,880
Speaker 1: fantastic example. So one way to think about it is

1147
01:11:17,960 --> 01:11:23,160
Speaker 1: that the phenomenon of power laws is highly relevant to

1148
01:11:23,240 --> 01:11:28,080
Speaker 1: success and failure, where we tend to think of things

1149
01:11:28,120 --> 01:11:31,160
Speaker 1: as linear with respect to growth. But that's not true

1150
01:11:31,200 --> 01:11:33,479
Speaker 1: for video games, it's not true for films, it's not

1151
01:11:33,520 --> 01:11:36,280
Speaker 1: true for novels, it's not true for art. It's a

1152
01:11:36,320 --> 01:11:39,719
Speaker 1: power law. This is very slightly technical for yours truly

1153
01:11:39,760 --> 01:11:42,840
Speaker 1: the English major, not technical for you, the Bath guy.

1154
01:11:43,320 --> 01:11:46,160
Speaker 1: But if we understand the phenomenon of power laws on

1155
01:11:46,200 --> 01:11:51,799
Speaker 1: how they work, then we'll get real clarity on spectacular success,

1156
01:11:51,960 --> 01:11:53,200
Speaker 1: including that of Star Wars.

1157
01:11:53,520 --> 01:11:56,040
Speaker 2: Very much. I want to take all sort of phenomena,

1158
01:11:56,360 --> 01:12:02,200
Speaker 2: So let's talk bring Star Wars back to behavioral economics.

1159
01:12:03,000 --> 01:12:05,840
Speaker 2: You note in the book whenever people find themselves at

1160
01:12:05,920 --> 01:12:10,080
Speaker 2: some sort of a crossroad within Star Wars, the series

1161
01:12:10,320 --> 01:12:14,040
Speaker 2: proclaims you are free to choose. This is the deepest

1162
01:12:14,120 --> 01:12:17,559
Speaker 2: lesson of Star Wars, which kind of reminds me of

1163
01:12:18,080 --> 01:12:21,439
Speaker 2: you and Thaylor's work in Nudge in terms of setting

1164
01:12:21,560 --> 01:12:25,720
Speaker 2: up choice architecture. Was that a conscious explanation?

1165
01:12:26,520 --> 01:12:30,960
Speaker 1: Well, Taylor and I were very focused on preservation of

1166
01:12:31,000 --> 01:12:34,879
Speaker 1: freedom and continue to be, And some of our friends

1167
01:12:34,960 --> 01:12:39,080
Speaker 1: on the left are mad at us because we're pro freedom.

1168
01:12:39,680 --> 01:12:42,280
Speaker 1: That's probably a self serving way to describe it, but

1169
01:12:44,040 --> 01:12:47,240
Speaker 1: I'm sticking with it. The thought of some of our

1170
01:12:47,280 --> 01:12:49,720
Speaker 1: friends on the left is that we need much more

1171
01:12:49,720 --> 01:12:52,400
Speaker 1: in the way of corrosion and mandates, and of course

1172
01:12:52,800 --> 01:12:55,639
Speaker 1: they have a role, But Taylor and I are very

1173
01:12:55,640 --> 01:13:04,240
Speaker 1: big on investor freedom, consumer freedom, America, exclamation Point, Star

1174
01:13:04,320 --> 01:13:09,879
Speaker 1: Wars is similar. It's art, it's not, you know, social science.

1175
01:13:10,439 --> 01:13:14,519
Speaker 1: And as between art and social science, at least my

1176
01:13:14,600 --> 01:13:18,200
Speaker 1: current mood, I go for art and I love them both.

1177
01:13:18,280 --> 01:13:22,519
Speaker 1: But Lucas is an artist and it's his soul that's speaking.

1178
01:13:22,920 --> 01:13:25,519
Speaker 1: And I don't know how conscious he was about this, though.

1179
01:13:25,520 --> 01:13:29,000
Speaker 1: I can tell you a little story if you want, okay.

1180
01:13:29,040 --> 01:13:34,360
Speaker 1: So freedom is the theme. Darth Vader, who's the worst

1181
01:13:34,360 --> 01:13:38,600
Speaker 1: person in the universe, maybe the second worst at the

1182
01:13:38,640 --> 01:13:44,240
Speaker 1: crucial moment, exercises his freedom because he believes that saving

1183
01:13:44,320 --> 01:13:48,240
Speaker 1: his son is more important than fidelity to the Emperor,

1184
01:13:48,320 --> 01:13:52,120
Speaker 1: and he sacrifices everything that's his choice, and that saves him.

1185
01:13:52,360 --> 01:13:55,160
Speaker 1: So it's in some ways a spiritual, even a Christian

1186
01:13:55,160 --> 01:13:59,440
Speaker 1: book about freedom, and this is what makes it, I think, transcendent.

1187
01:14:00,000 --> 01:14:02,240
Speaker 1: The story is that after I did the book, the

1188
01:14:02,280 --> 01:14:04,559
Speaker 1: one person who I was most terrified to see was

1189
01:14:04,560 --> 01:14:07,760
Speaker 1: George Lucas, whom I knew a tiny, tiny, tiny bit.

1190
01:14:08,360 --> 01:14:10,519
Speaker 1: And I was at a big event with maybe three

1191
01:14:10,600 --> 01:14:13,240
Speaker 1: hundred people, and there in the distance was George Lucas,

1192
01:14:13,600 --> 01:14:15,120
Speaker 1: and he started walking toward me.

1193
01:14:15,640 --> 01:14:17,519
Speaker 2: Line you see him coming towards you.

1194
01:14:17,760 --> 01:14:20,960
Speaker 1: Yeah, and I thought he was walking fast but suadenly,

1195
01:14:21,000 --> 01:14:24,000
Speaker 1: and I thought, please God, let Harrison Ford be right

1196
01:14:24,040 --> 01:14:27,400
Speaker 1: in back of me. Please God, let someone whom he

1197
01:14:27,479 --> 01:14:29,559
Speaker 1: knows me in back of me. Please God, let him

1198
01:14:29,600 --> 01:14:31,960
Speaker 1: not will be walking toward me. But he's continuing to

1199
01:14:32,000 --> 01:14:34,559
Speaker 1: walk toward me. And it's about two hundred yards and

1200
01:14:34,680 --> 01:14:36,479
Speaker 1: now he's one hundred and fifty yards away. Now he's

1201
01:14:36,479 --> 01:14:38,800
Speaker 1: one hundred yards away. And I thought, maybe I can

1202
01:14:38,880 --> 01:14:41,479
Speaker 1: be like some character in Star Wars where I can

1203
01:14:41,520 --> 01:14:45,200
Speaker 1: make myself meld into the floor.

1204
01:14:45,240 --> 01:14:47,240
Speaker 2: This is not the law professor you're looking at.

1205
01:14:48,800 --> 01:14:50,680
Speaker 1: I thought, can I do a mind trick so he

1206
01:14:50,720 --> 01:14:53,799
Speaker 1: doesn't know it's me? Or can I make myself really tiny?

1207
01:14:54,080 --> 01:14:56,759
Speaker 1: Or can I make myself pure liquid? But he's walking

1208
01:14:56,800 --> 01:14:59,800
Speaker 1: toward me. And then he said the most terrifying words

1209
01:14:59,800 --> 01:15:04,200
Speaker 1: I've ever heard from a human being, which is he said,

1210
01:15:04,200 --> 01:15:07,360
Speaker 1: I read your book. And I thought, oh my gosh,

1211
01:15:07,360 --> 01:15:10,400
Speaker 1: that's going to happen. And then he paused, and he said,

1212
01:15:10,439 --> 01:15:14,200
Speaker 1: without any sense of pleasure, he said I liked it.

1213
01:15:15,120 --> 01:15:18,160
Speaker 1: And then he said, without any sense of pleasure, no smile,

1214
01:15:18,360 --> 01:15:22,120
Speaker 1: he said it's good. Then he paused and said, with

1215
01:15:22,200 --> 01:15:25,840
Speaker 1: no smile at all, he said, you got what I

1216
01:15:25,880 --> 01:15:28,880
Speaker 1: was trying to do. And then he paused, and he said,

1217
01:15:28,920 --> 01:15:31,160
Speaker 1: start a smile, and he said, but the other books

1218
01:15:31,200 --> 01:15:34,200
Speaker 1: on Star Wars, they're terrible. And then he got a

1219
01:15:34,240 --> 01:15:36,360
Speaker 1: big smile and got really happy, and he said, and

1220
01:15:36,400 --> 01:15:39,880
Speaker 1: you made mistakes. I loved him so much that he

1221
01:15:40,000 --> 01:15:42,599
Speaker 1: wasn't going to flatter me. I wasn't going to say anything.

1222
01:15:42,640 --> 01:15:44,800
Speaker 1: You know, you read a good book. But he was

1223
01:15:45,600 --> 01:15:48,320
Speaker 1: as nice as he could be, and he has become

1224
01:15:48,360 --> 01:15:51,160
Speaker 1: a friend. And we talked a bit about the book,

1225
01:15:51,240 --> 01:15:54,360
Speaker 1: and he said, at one point, you have no idea

1226
01:15:54,439 --> 01:15:57,840
Speaker 1: how much work I put into the prequels. And I said,

1227
01:15:58,160 --> 01:16:01,240
Speaker 1: do you know you're talking to I wrote a book

1228
01:16:01,280 --> 01:16:03,080
Speaker 1: on this. I know how much work you put on

1229
01:16:03,160 --> 01:16:06,080
Speaker 1: the prequels. In the prequels, and he smiled. And then

1230
01:16:06,560 --> 01:16:10,080
Speaker 1: he described one of my alleged mistakes. And I'm not

1231
01:16:10,120 --> 01:16:12,120
Speaker 1: going to disclose what it was because that would be

1232
01:16:12,200 --> 01:16:15,280
Speaker 1: violating a confidence. But I don't believe it was a

1233
01:16:15,320 --> 01:16:19,120
Speaker 1: mistake at all. I think he was retrofitting something in

1234
01:16:19,160 --> 01:16:21,360
Speaker 1: the genesis of the Star Wars, which.

1235
01:16:21,000 --> 01:16:22,760
Speaker 2: He has been known to do, which a lot of

1236
01:16:23,000 --> 01:16:28,080
Speaker 2: Very often he engages a little revisionist literary history.

1237
01:16:27,800 --> 01:16:31,360
Speaker 1: And I think that's great for a great artist writer.

1238
01:16:32,080 --> 01:16:34,599
Speaker 1: This was a private conversation where he had no stake

1239
01:16:34,680 --> 01:16:39,040
Speaker 1: in anything, but we argued a little bit. I thought,

1240
01:16:39,120 --> 01:16:43,880
Speaker 1: this is pretty surreal that I'm telling George Lucas about

1241
01:16:43,880 --> 01:16:46,960
Speaker 1: the genesis of the Star Wars movies. That I'm believing

1242
01:16:47,000 --> 01:16:50,439
Speaker 1: myself rather than George Lucas, and that might have been

1243
01:16:50,479 --> 01:16:51,479
Speaker 1: motivated recently.

1244
01:16:51,720 --> 01:16:54,679
Speaker 2: Hey, if George Lucas said your book on Star Wars

1245
01:16:54,840 --> 01:16:57,040
Speaker 2: was good and the rest of him or not, that's

1246
01:16:57,120 --> 01:16:59,800
Speaker 2: a giant win. Can't do much better than that.

1247
01:17:00,040 --> 01:17:03,479
Speaker 1: I think what he I like to think it's.

1248
01:17:03,280 --> 01:17:06,720
Speaker 2: Pretty clear that the book, so this is a Your

1249
01:17:07,280 --> 01:17:13,840
Speaker 2: regular books are academic and deeply researched, and they're not lightweight. This,

1250
01:17:13,960 --> 01:17:16,280
Speaker 2: on the other hand, is a fun I don't want

1251
01:17:16,280 --> 01:17:19,120
Speaker 2: to say it's a lightweight read, but it's an easy read,

1252
01:17:19,800 --> 01:17:22,240
Speaker 2: and it's clear a lot of thought and depth went

1253
01:17:22,280 --> 01:17:27,040
Speaker 2: into it to say, what is the genesis of Star Wars?

1254
01:17:27,080 --> 01:17:29,639
Speaker 2: Not just the Joseph Campbell Man of a Thousand Faces,

1255
01:17:29,720 --> 01:17:33,479
Speaker 2: but what are the philosophical motivations of Lucas? What is

1256
01:17:33,479 --> 01:17:37,040
Speaker 2: he trying? You know, the relevance about Nixon moving to

1257
01:17:37,120 --> 01:17:41,400
Speaker 2: authoritarian and the freedom like it's clear thought went into

1258
01:17:41,439 --> 01:17:43,000
Speaker 2: this and he picked that up.

1259
01:17:43,120 --> 01:17:44,760
Speaker 1: Yeah, thank you for that. Thank you.

1260
01:17:44,960 --> 01:17:47,000
Speaker 2: So I only have you for a few minutes. Let

1261
01:17:47,000 --> 01:17:50,160
Speaker 2: me ask throw you a couple of curveball questions and

1262
01:17:50,160 --> 01:17:53,160
Speaker 2: then we'll quickly do our speed round our favorite questions.

1263
01:17:53,720 --> 01:17:58,679
Speaker 2: So you were a professor at University of Chicago, where

1264
01:17:58,920 --> 01:18:03,840
Speaker 2: Richard Posner was also a professor. He once was the

1265
01:18:03,880 --> 01:18:08,519
Speaker 2: most cited law professor in the US until you came along.

1266
01:18:08,640 --> 01:18:11,200
Speaker 2: Tell us a little bit about your relationship with Bosner.

1267
01:18:11,360 --> 01:18:16,439
Speaker 1: It was very good so early on. He was a giant,

1268
01:18:16,880 --> 01:18:21,320
Speaker 1: and he was very skeptical of some of the things

1269
01:18:21,360 --> 01:18:27,080
Speaker 1: I thought, but he was very engaged and very collegial,

1270
01:18:27,280 --> 01:18:31,320
Speaker 1: so it was all substance, not personal, and I just

1271
01:18:31,439 --> 01:18:35,160
Speaker 1: learned so much from him. His comments on my papers,

1272
01:18:35,200 --> 01:18:39,720
Speaker 1: which he thought were bad papers, were instructive comments, and

1273
01:18:39,760 --> 01:18:44,000
Speaker 1: they made them less bad papers. Engaging with his thinking

1274
01:18:44,320 --> 01:18:50,720
Speaker 1: was a gift to me. And I think as skeptical

1275
01:18:51,000 --> 01:18:55,160
Speaker 1: as I was of maybe ninety percent of what he thought,

1276
01:18:55,240 --> 01:18:58,160
Speaker 1: I ended up agreeing with maybe forty percent of what

1277
01:18:58,240 --> 01:19:03,160
Speaker 1: he thought. It was he was I think he wouldn't

1278
01:19:03,400 --> 01:19:05,920
Speaker 1: want to think of himself as a mentor of mine.

1279
01:19:06,040 --> 01:19:10,280
Speaker 2: But he was, So let's address some of the things

1280
01:19:10,280 --> 01:19:16,120
Speaker 2: he thought of law and economics. Initially was considered fairly

1281
01:19:16,280 --> 01:19:21,519
Speaker 2: radical and an extra legislative back door to affect the

1282
01:19:21,600 --> 01:19:25,440
Speaker 2: judicial process. Tell us a little bit about his philosophy,

1283
01:19:25,479 --> 01:19:30,760
Speaker 2: which in small measure he recanted after the financial crisis.

1284
01:19:31,120 --> 01:19:37,639
Speaker 2: He said, my core belief is the company's own desire

1285
01:19:37,640 --> 01:19:41,000
Speaker 2: to preserve their reputations should have prevented them from doing

1286
01:19:41,080 --> 01:19:43,640
Speaker 2: what took place during the financial crisis. I don't know

1287
01:19:43,680 --> 01:19:46,320
Speaker 2: how much of a if that's a full recant or

1288
01:19:46,960 --> 01:19:51,240
Speaker 2: just a post financial crisis without happened. But tell us

1289
01:19:51,280 --> 01:19:52,200
Speaker 2: about his theories.

1290
01:19:52,720 --> 01:19:58,160
Speaker 1: So, I think the largest contribution Posner made was to think,

1291
01:19:58,439 --> 01:20:02,679
Speaker 1: what are the consequences of law for people? And how

1292
01:20:02,680 --> 01:20:05,800
Speaker 1: can we be empirical about that? So is the law

1293
01:20:05,840 --> 01:20:09,320
Speaker 1: contributing to well being? Is it leading to economic growth?

1294
01:20:09,880 --> 01:20:14,000
Speaker 1: Is it destroying wealth? Is it helping consumers and investors?

1295
01:20:14,080 --> 01:20:17,800
Speaker 1: Or is it hurting them? And that insistent focus on

1296
01:20:17,800 --> 01:20:20,880
Speaker 1: one of the consequences of law that was for me

1297
01:20:21,040 --> 01:20:24,360
Speaker 1: then and I'm smiling now, it was like a breath

1298
01:20:24,360 --> 01:20:26,000
Speaker 1: of fresh air. When I was in law school. We

1299
01:20:26,000 --> 01:20:28,800
Speaker 1: never asked about that as what was analogous to what?

1300
01:20:29,479 --> 01:20:32,960
Speaker 1: And Poser just said, what does this mean for people?

1301
01:20:33,280 --> 01:20:35,840
Speaker 1: In a way that had no sentimentality to it. It

1302
01:20:35,920 --> 01:20:41,000
Speaker 1: had numbers, and that's amazing. Then there was the idea

1303
01:20:41,080 --> 01:20:44,360
Speaker 1: that the common law is efficient. So we thought the

1304
01:20:44,439 --> 01:20:46,920
Speaker 1: law of private property, contract and tour it in England

1305
01:20:46,960 --> 01:20:49,880
Speaker 1: and America just is efficient. That's how he made his reputation.

1306
01:20:50,680 --> 01:20:54,559
Speaker 1: I don't think that survived, but it's not crazy false,

1307
01:20:55,920 --> 01:21:01,640
Speaker 1: It's not wildly inefficient, and it's pretty efficient. So I

1308
01:21:01,680 --> 01:21:06,040
Speaker 1: think that that was a fundamental contribution. His kind of

1309
01:21:06,120 --> 01:21:12,200
Speaker 1: Chicago is skepticism about a role for government regulation and such.

1310
01:21:12,280 --> 01:21:14,720
Speaker 1: I think that was really a third order idea. The

1311
01:21:14,800 --> 01:21:20,519
Speaker 1: more fundamental is think about the consequences. I don't know

1312
01:21:20,520 --> 01:21:24,400
Speaker 1: what to think about recantation by him. It may be

1313
01:21:24,560 --> 01:21:28,680
Speaker 1: that just under the spell of a horrible economic downturn,

1314
01:21:28,760 --> 01:21:31,040
Speaker 1: he thought there were some things I thought that weren't right.

1315
01:21:31,680 --> 01:21:35,240
Speaker 1: But more fundamental was his focus on evidence and data

1316
01:21:35,760 --> 01:21:38,920
Speaker 1: than is thinking that I am a Chicago school person

1317
01:21:39,360 --> 01:21:43,799
Speaker 1: and on behavioral economics my own focus. He really did shift,

1318
01:21:43,840 --> 01:21:45,760
Speaker 1: and he wrote me a note saying he shifted in

1319
01:21:45,760 --> 01:21:48,040
Speaker 1: the early days Taylor and I gave a talk at

1320
01:21:48,120 --> 01:21:52,479
Speaker 1: Chicago in which he was fiercely skeptical, and he wrote

1321
01:21:52,479 --> 01:21:56,720
Speaker 1: about behavioral economics in a way that was full of dismissiveness,

1322
01:21:56,720 --> 01:21:59,360
Speaker 1: and he ended up being I think the word a

1323
01:21:59,439 --> 01:22:03,120
Speaker 1: convert is is accurate, and that's because he thought the

1324
01:22:03,120 --> 01:22:04,639
Speaker 1: evidence supported it. Well.

1325
01:22:04,640 --> 01:22:09,600
Speaker 2: When you look at the original pre behavioral model of economics,

1326
01:22:10,200 --> 01:22:15,000
Speaker 2: the fundamental premise is false. Humans are rational profit mocktimizers.

1327
01:22:15,280 --> 01:22:19,000
Speaker 2: We're not. And if your foundation is false, well how

1328
01:22:19,040 --> 01:22:21,519
Speaker 2: I can that building on top of it go? All right,

1329
01:22:21,520 --> 01:22:23,080
Speaker 2: so I only have you for a few minutes, Let's

1330
01:22:23,120 --> 01:22:26,320
Speaker 2: jump to our favorite questions, our speed round that we

1331
01:22:26,520 --> 01:22:29,759
Speaker 2: ask all of our guests, And let's start with what's

1332
01:22:29,800 --> 01:22:32,160
Speaker 2: been keeping you entertained? What are you either listening to

1333
01:22:32,320 --> 01:22:33,760
Speaker 2: or watching these days?

1334
01:22:33,760 --> 01:22:37,479
Speaker 1: The show on Netflix called Vortex, which I love, love,

1335
01:22:37,560 --> 01:22:41,080
Speaker 1: love love. It's French. It's about time travel and it's

1336
01:22:41,120 --> 01:22:45,040
Speaker 1: about romance, and it's about the economy, and it's about heroism,

1337
01:22:45,280 --> 01:22:48,360
Speaker 1: and it's about the future in the past, and it's

1338
01:22:48,479 --> 01:22:50,160
Speaker 1: not to be missed. Vortex.

1339
01:22:50,600 --> 01:22:53,120
Speaker 2: We'll definitely check it out. Do you speak French or

1340
01:22:53,720 --> 01:22:57,560
Speaker 2: are you just a Francophile or is your pro Trepity Pompo.

1341
01:22:58,720 --> 01:23:02,559
Speaker 2: If you haven't seen cole my agent strong recommend it's

1342
01:23:02,600 --> 01:23:08,320
Speaker 2: absolutely delightful. So you've mentioned several mentors who helped guide

1343
01:23:08,360 --> 01:23:09,000
Speaker 2: your career.

1344
01:23:10,479 --> 01:23:15,200
Speaker 1: I would single out a recently deceased law professor named

1345
01:23:15,280 --> 01:23:20,800
Speaker 1: Lloyd wine Reb, who taught a course at Harvard on

1346
01:23:21,040 --> 01:23:24,360
Speaker 1: law and philosophy, and undergraduate course which I took on

1347
01:23:24,400 --> 01:23:28,360
Speaker 1: a kind of flyer, and it alerted me to a

1348
01:23:28,439 --> 01:23:31,880
Speaker 1: world I had no idea it existed. So I would

1349
01:23:31,880 --> 01:23:34,000
Speaker 1: single out Lloyd Winereb.

1350
01:23:35,000 --> 01:23:36,720
Speaker 2: What are some of your favorite books? What are you

1351
01:23:36,720 --> 01:23:37,559
Speaker 2: reading right now?

1352
01:23:38,320 --> 01:23:42,919
Speaker 1: My favorite book of all time is Possession by as

1353
01:23:42,960 --> 01:23:46,760
Speaker 1: By It it's the greatest work of fiction in the

1354
01:23:47,240 --> 01:23:51,880
Speaker 1: English language. Wow, And I reread it every few years

1355
01:23:51,960 --> 01:24:00,360
Speaker 1: and it's completely great. I'm reading right now John's Stuart

1356
01:24:00,360 --> 01:24:04,880
Speaker 1: Mills The Subjection of Women, which because I'm writing about

1357
01:24:04,880 --> 01:24:08,400
Speaker 1: liberalism as a political theory and where it came from,

1358
01:24:08,600 --> 01:24:13,800
Speaker 1: and Mill on equality and liberty is relevant.

1359
01:24:13,960 --> 01:24:17,840
Speaker 2: Let's say, to say the very least, what sort of

1360
01:24:17,840 --> 01:24:21,040
Speaker 2: advice would you give to a recent college grad interest

1361
01:24:21,120 --> 01:24:25,000
Speaker 2: in a career in either law or behavioral finance.

1362
01:24:26,680 --> 01:24:32,040
Speaker 1: Find things you love and focus on them, because even

1363
01:24:32,080 --> 01:24:35,400
Speaker 1: if you don't succeed spectacularly, at least you will have

1364
01:24:35,560 --> 01:24:40,320
Speaker 1: loved not succeeding spectacularly. And if you focus on the

1365
01:24:40,360 --> 01:24:43,080
Speaker 1: things you really enjoy and love, the chance that you'll

1366
01:24:43,120 --> 01:24:44,960
Speaker 1: succeed skyrockets.

1367
01:24:46,040 --> 01:24:48,360
Speaker 2: And our final question, what do you know about the

1368
01:24:48,400 --> 01:24:57,799
Speaker 2: world of law, constitution, nudges, sludges, noise, behavioral finance today

1369
01:24:58,200 --> 01:25:00,880
Speaker 2: that you wish you knew forty or so years ago

1370
01:25:00,920 --> 01:25:02,840
Speaker 2: when you were first getting started.

1371
01:25:02,840 --> 01:25:08,040
Speaker 1: Well, I wish I'd known about the horror of sludge

1372
01:25:08,479 --> 01:25:16,439
Speaker 1: understood as administrative burdens, waiting time, long forms, in person

1373
01:25:16,520 --> 01:25:21,000
Speaker 1: interview requirements, things that make it so that if you're

1374
01:25:21,160 --> 01:25:23,439
Speaker 1: kind of doing well in life, but you need help

1375
01:25:23,479 --> 01:25:25,880
Speaker 1: in one kind of one kind or another, it's really

1376
01:25:25,920 --> 01:25:28,799
Speaker 1: hard to get it. Or if you're struggling in life,

1377
01:25:28,920 --> 01:25:32,320
Speaker 1: let's say you're old, or you're sick, or you're poor,

1378
01:25:32,800 --> 01:25:39,080
Speaker 1: or you're struggling, you're lonely. The various administrative burdens we

1379
01:25:39,120 --> 01:25:43,479
Speaker 1: impose on people, they are like a wall that's our

1380
01:25:43,520 --> 01:25:49,320
Speaker 1: society erects often inadvertently take down that wall.

1381
01:25:49,680 --> 01:25:53,760
Speaker 2: Mister whomever, quite fascinating cask, thank you for being so

1382
01:25:53,920 --> 01:25:58,680
Speaker 2: generous with your time. We have been speaking with Cass Sunstein,

1383
01:26:00,040 --> 01:26:03,920
Speaker 2: whose career is just legendary in the fields of law

1384
01:26:04,560 --> 01:26:09,439
Speaker 2: and publishing and behavioral finance and public service. I don't

1385
01:26:09,439 --> 01:26:12,320
Speaker 2: know what else to say other than thank you. If

1386
01:26:12,360 --> 01:26:15,200
Speaker 2: you enjoyed this conversation, be sure and check out any

1387
01:26:15,280 --> 01:26:19,000
Speaker 2: of the five hundred previous discussions we've had over the

1388
01:26:19,040 --> 01:26:24,240
Speaker 2: past eight years. You can find those at iTunes, Spotify, YouTube,

1389
01:26:24,320 --> 01:26:27,880
Speaker 2: wherever you find your favorite podcasts. Sign up from my

1390
01:26:28,000 --> 01:26:31,600
Speaker 2: daily reading list at rid Halts dot com. Follow me

1391
01:26:31,880 --> 01:26:35,080
Speaker 2: on Twitter at rid Halts, although that account was hacked

1392
01:26:35,120 --> 01:26:38,680
Speaker 2: and in the meantime I'm using at Barry Underscore rit

1393
01:26:38,720 --> 01:26:41,760
Speaker 2: Halts until I get it back. Follow all of the

1394
01:26:41,760 --> 01:26:46,519
Speaker 2: Bloomberg family of podcasts on Twitter at podcasts. I would

1395
01:26:46,520 --> 01:26:48,679
Speaker 2: be remiss if I did not thank the crack team

1396
01:26:48,680 --> 01:26:52,360
Speaker 2: that helps with these conversations together. My audio engineer is

1397
01:26:52,560 --> 01:26:56,479
Speaker 2: Justin Milner, My producer is Paris Walld, My project manager

1398
01:26:56,520 --> 01:27:00,719
Speaker 2: is Atika val Bron. My researcher is Sean Russo. I'm

1399
01:27:00,840 --> 01:27:04,800
Speaker 2: Barry Retoltz. You've been listening to Masters in Business on

1400
01:27:04,920 --> 01:27:06,880
Speaker 2: Bloomberg Radio