Speaker 1: I'm Bethany McLean. This is making a killing interviews with authors, journalists, filmmakers,
Speaker 1: and economists, reframing the stories you've heard in the headlines,
Speaker 1: and explaining why these stories and the lessons learned are
Speaker 1: important for all of us to understand. There's this inversion
Speaker 1: that I have always liked that's safe as risky, and
Speaker 1: risky is safe. It's the idea of a swing and
Speaker 1: a miss. You might be scared to try something new
Speaker 1: and miss, but if you never swing at all, you're
Speaker 1: guaranteed to miss one hundred percent of the time you
Speaker 1: risk at all if you just sit safely on the sidelines.
Speaker 1: This is good life advice to be sure risk brings
Speaker 1: change into our lives. You won't get a raise if
Speaker 1: you never change jobs. You'll never fall in love if
Speaker 1: you don't go out on that first date. It's a
Speaker 1: phenomenal skill to learn which risks are worth taking and
Speaker 1: how to reduce the downside so you can figure out
Speaker 1: how best to save her retirement, which degree is worth
Speaker 1: the tuition, where to buy a house, and on and
Speaker 1: on and on. But how can we also get smarter
Speaker 1: at understanding risk and reward in business? At Fortune five
Speaker 1: hundred companies on Wall Street in global markets. As technology
Speaker 1: changes everything at an exponential rate, how do we run
Speaker 1: risk reward analysis when the world around us is being
Speaker 1: disrupted at this whispering pace. I'm excited that I get
Speaker 1: to talk today with Alison Schrager, author of the new
Speaker 1: book An Economist Walks into a Bravel, which has got
Speaker 1: to be one of the best titles ever. She's a
Speaker 1: retirement finance economist and has spent years talking to risk
Speaker 1: takers in all kinds of businesses. But in a Hollywood
Speaker 1: worthy twist, she's lately been studying risk in the unlikeliest
Speaker 1: of places, talking to sex workers at the Bunny Ranch
Speaker 1: brothel and Nevada. It turns out they are experts at
Speaker 1: pricing and reducing risk. I say it all the time
Speaker 1: in the world of business. Truth is always always stranger
Speaker 1: than fiction. Anyway, Alison has learned that there's a science
Speaker 1: to good risk taking. She says that no one reaches
Speaker 1: the top of their field, be it sex work or
Speaker 1: running a fortune five hundred company without mastering risk. They
Speaker 1: don't jump in and just go with their gut. They
Speaker 1: possess a deliberate, thoughtful strategy so they know what risks
Speaker 1: to take, and how to reduce the downside if things
Speaker 1: don't go their way. I've always loved the quote with
Speaker 1: which Alison begins her book. It's from Peter Bernstein's Against
Speaker 1: the Gods. The revolutionary idea that defines the boundary between
Speaker 1: modern times in the past is the mastery of risk,
Speaker 1: the notion that the future is more than a whim
Speaker 1: of the gods, and that men and women are not
Speaker 1: pass before nature. So how do we master risk? Okay,
Speaker 1: so the Bunny Ranch, we obviously have to start there.
Speaker 1: How did you find these guys? Well, so it's a
Speaker 1: funny story. Oh, I was really struggling with my book proposal.
Speaker 1: I felt like I needed a great risk taker, and
Speaker 1: so I had a drink with a cousin who I
Speaker 1: can't name, and I was like, I, really, do you you
Speaker 1: know anyone who was broken the law? I could really
Speaker 1: use a criminal. Maybe they'll help me because I was
Speaker 1: having problems coming up with stories. I love that I
Speaker 1: could really use a criminal. That's awesome. And he tells
Speaker 1: me that his girlfriend runs an illegal brothel, and well,
Speaker 1: you know, it was really way more information than I needed.
Speaker 1: It was a very specific kind of brothel. And that
Speaker 1: it was out of New York and they specialized in submissives,
Speaker 1: so women who get tied up and then have sex
Speaker 1: with their clients. And because this is a very high
Speaker 1: risk thing, even more risky than normal sex work, what
Speaker 1: she did is she got a thirty percent cut if
Speaker 1: she did all the screening and made them safer. And
Speaker 1: I was like, oh, that's an interesting risk story. So
Speaker 1: I wrote a court story about this woman and met
Speaker 1: her and she's lovely, and the bunny ranch saw it.
Speaker 1: He called me, and they're right, Well, if your bunny
Speaker 1: ranch called you fabulous, if you're gonna be writing this
Speaker 1: is snobbery. I guess it's a hierarchy in every business.
Speaker 1: If you're gonna be writing about brothels, you should be
Speaker 1: writing about our brothel because we're the biggest and most
Speaker 1: important one. I don't write about brothels, but it's an
Speaker 1: interesting call. So I'm gonna take this. And they had
Speaker 1: something they want me to write about. It didn't really
Speaker 1: make sense for me, but then they said something quite interesting,
Speaker 1: which was, well, so we don't upset prices, but what
Speaker 1: the women come in and they negotiate every price and
Speaker 1: every transaction individually and I was like, well, that's interesting.
Speaker 1: You so telling me of women and who are about
Speaker 1: twenty years old negotiating for tens of thousands of dollars
Speaker 1: with men in their sixties. And I why, yes, yes,
Speaker 1: And you know, no one's ever asked about that before.
Speaker 1: You know, they come in not knowing their values. So
Speaker 1: we have a negotiation training program. And you heard this
Speaker 1: a brothel through the lens of an economist. Yeah, so
Speaker 1: I'll say I need to see this. So I went
Speaker 1: out there and met Dennis Hof and spent about a
Speaker 1: week and a half going through their negotiation training program.
Speaker 1: And that's where I developed a relationship to where they
Speaker 1: were going to let me come back and collect pricing
Speaker 1: data because I was struck by how much they charged.
Speaker 1: I wouldn't have expected they could get away with it,
Speaker 1: because I mean, this is not an easy place to
Speaker 1: get to, and like, why just pay a lot less
Speaker 1: and use someone like my cousin's girlfriends. And it's a
Speaker 1: huge markup over the illiegal market, right three stunning and
Speaker 1: you have to go to Nevada. But I discovered it's
Speaker 1: like Dennis was just like, you come here and you
Speaker 1: just don't have to worry about anything, And is that
Speaker 1: the key lesson for you? What was the key lesson
Speaker 1: for you out of this about risk? That you know,
Speaker 1: every market puts a price on risk, and in any market,
Speaker 1: you have to pay to reduce risk. So that's the
Speaker 1: thing that three hundred percent is. So you know, you
Speaker 1: don't have to worry about some sort of Robert Craft situation.
Speaker 1: Come there, you're not going to get caught. You know,
Speaker 1: they even have some innocuous thing you put on your
Speaker 1: credit card, totally discreet, it's completely legal, and you don't
Speaker 1: have to worry about anything. So you pay for that.
Speaker 1: So it's aspect or risk premium that you're paying. But
Speaker 1: the women pay it too, because anyway they're charging it
Speaker 1: was a fourteen hundred dollars an hour on average, they
Speaker 1: actually have to pay fifty percent of the brothel and
Speaker 1: then taxes on top of that. Tell me a little
Speaker 1: bit about Shelby Starr and why she was willing to
Speaker 1: do things this way, Well, because she didn't. She's someone
Speaker 1: who never really wanted to break the law. I mean,
Speaker 1: I met women there who worked in the illegal market,
Speaker 1: and they add bad experiences and that's why they're there.
Speaker 1: But a lot of them just wanted to do this
Speaker 1: job and didn't want to if you worry about dealing
Speaker 1: with law enforcement. They didn't want to have to deal
Speaker 1: with crazy. I mean, they said, my cousin's girlfriend has
Speaker 1: to do this to protect people, because it's super dangerous
Speaker 1: being a sex worker and meeting people online and doing
Speaker 1: some illegal activity with them. So you don't know if
Speaker 1: they're gonna be crazy, if they're going to kill you,
Speaker 1: beat you up, or you're going to be arrested. So
Speaker 1: they just don't want to deal with that. I mean,
Speaker 1: Shelby definitely sees herself and they all do is people
Speaker 1: who are in legitimate businesses and they don't want to
Speaker 1: feel in any way that what they're doing is illegal.
Speaker 1: So it teaches you about managing risk. But it also
Speaker 1: teaches you about this other concept that we think of
Speaker 1: as belonging to the world of finance, but really belongs
Speaker 1: to the much broader world, which is hedging, right exactly.
Speaker 1: So it's a hedge. So in finance, you paid to you,
Speaker 1: you take some risk off the table, and that's a hedge.
Speaker 1: So in finance, it would be like the simplest way
Speaker 1: to think about it is if you were investing in
Speaker 1: the stock market, like a stock index fund, and you
Speaker 1: weren't totally comfortable with market risk, you would put a
Speaker 1: little bit in a treasury bond and so that's a hedge.
Speaker 1: You get a lower return, but you also have lower risk.
Speaker 1: And that's effectively what they're doing. They're they're sharing their
Speaker 1: risk with the brothel and an exchange they or they
Speaker 1: are actually the brothel's taking getting rid of all their risk,
Speaker 1: and an exchange they get at lower pay. It's one
Speaker 1: of the fascinating things about your book that I found
Speaker 1: so interesting is how relevant these topics that we think
Speaker 1: of as financial topics are too ordinary life, right, Yeah,
Speaker 1: And that was the goal, is that you know, the
Speaker 1: risk concepts are everywhere. I mean, finance is the study
Speaker 1: of risk and financial markets, and that's partially just because
Speaker 1: there's tons of data, but they really apply everywhere. I
Speaker 1: actually want to back up and talk a little bit
Speaker 1: about you, because you have a very personal story about
Speaker 1: risk that you tell in the book, but tell us
Speaker 1: about it. Yeah, and I think a lot of us
Speaker 1: have been there and that you know, we often think,
Speaker 1: you know, if something's hard, or if we work hard
Speaker 1: at something, it's not going to be risky. But This
Speaker 1: speaks to why it's so important. And this is the
Speaker 1: easiest risk management piece of advice I can give you.
Speaker 1: We're not easy, but the most important for all the
Speaker 1: fancy things you can do, if you have a very
Speaker 1: clearly well defined goal of the risk you're taking, then
Speaker 1: nothing else is going to do more to ensure that
Speaker 1: a risk works out. And so I went to grad
Speaker 1: school right out of undergrad and it was an economics PhD.
Speaker 1: And you know, I love economics. I always like idolized
Speaker 1: famous economists in a really weird, geeky way when I
Speaker 1: was in high school. That's the line you're not going
Speaker 1: to hear about from everybody. I always lied famous economists.
Speaker 1: I love that. Yeah. No, I was such a nerd
Speaker 1: about it. I was so excited to get into like
Speaker 1: good PhD programs where I ought to work with them.
Speaker 1: I wanted to be one of them, I thought. And
Speaker 1: so I, you know, at twenty three, just jump into
Speaker 1: a quantitative PhD program. Anyway, I hadn't even taken calculus,
Speaker 1: I'd know math training, and it was just awful. I mean,
Speaker 1: it's it's it's it's a tough PhD. All PhDs are tough,
Speaker 1: but economists tend to be particularly unhappy, and I'm told
Speaker 1: my program was particularly unhappy. They even tell me now
Speaker 1: when I see people from my department, theyre always like,
Speaker 1: we're so much nice sort of people than we were
Speaker 1: to you. But it was just a tough experience, and
Speaker 1: especially the first year I was struggling so much because
Speaker 1: I didn't have the math training as a former math major.
Speaker 1: My heart is going out to you. Yeah, Like I
Speaker 1: learned about, you know, a master's worth of math on
Speaker 1: my own at night and still failed every exam. It
Speaker 1: was just never note I empathize. So I figured, well,
Speaker 1: this is hard. So therefore if I just work hard
Speaker 1: and then do not only that an insanely quantitative dissertation
Speaker 1: way more than necessary, then somehow this is going to
Speaker 1: bring me great success and this is all going to
Speaker 1: pay off because it's this hard, then something great's going
Speaker 1: to happen. And then I solve this ridiculously difficult math problem. Anyway,
Speaker 1: I'm not really a math person, or didn't consider myself one.
Speaker 1: And then I went on the job market and was
Speaker 1: on all these interviews to be a professor, and I
Speaker 1: was like, I have no interest in being here. I
Speaker 1: have you know, it's a great job for some people.
Speaker 1: But I realized, you know, this is not what I
Speaker 1: ever wanted. So it's things I invested in this asset
Speaker 1: that never really got me to my goal because I
Speaker 1: wasn't sure what my goal was. But it definitely wasn't that.
Speaker 1: I loved your chapter heading getting what you want requires
Speaker 1: knowing what you want. Yeah, and it's so basic, I guess.
Speaker 1: It's so like the secret but also for a finance
Speaker 1: and you see this all the time in financial markets too,
Speaker 1: is you know when you invest with an for something
Speaker 1: as this in retirement, which is what my background's in,
Speaker 1: is that people everyone's geared towards investing for this huge
Speaker 1: pot of money on day one er retirement, Like you
Speaker 1: often see like what's your number, which is this pile
Speaker 1: of money number, like maybe a million dollars if you're
Speaker 1: lucky to retire with. But that's not your goal. Your
Speaker 1: goal is income and investing for a pot of money
Speaker 1: and investing for income that lasts a lifetime or to
Speaker 1: totally different financial goals and require two totally different strategies,
Speaker 1: and if you invest for one, you're going to be
Speaker 1: a lot riskier with the other. One makes a ton
Speaker 1: of sense, getting what you want requires knowing what you want, right,
Speaker 1: But let's go let's go to that your fascination with
Speaker 1: retirement because okay, that word in and of itself for
Speaker 1: most of us, unless it's some kind of mixture of
Speaker 1: Durad's stultifying boredom, I don't know. But you've always been
Speaker 1: fascinated by retirement. Yeah, I mean, I think one of
Speaker 1: the reasons people are put off by it is it's
Speaker 1: sort of the last thing you do before you die,
Speaker 1: and it's when you sort of give up on being
Speaker 1: that person who contributes. It courses you to confront mortality. Yeah,
Speaker 1: and also that you're like done with your career and
Speaker 1: now you're just sort of being put out to pasture.
Speaker 1: Although retirement, modern retirement need to be that way, but
Speaker 1: for me, it was always this fascinating problem because it
Speaker 1: was this pure distillation of what I thought economics was,
Speaker 1: which is allocation of scarce resources. But it's over your lifetime, right,
Speaker 1: So you make a certain amount of money and you
Speaker 1: have to spend a certain amount of money, and how
Speaker 1: do you do that in the best way? And if
Speaker 1: you take more risk, you have the expectation of more
Speaker 1: So how do you manage that risk. So this is
Speaker 1: very pure, beautiful problem, which honestly, most of the most
Speaker 1: interesting things happen when you're young, because it's a lifetime problem.
Speaker 1: And I think when I was in grad school and
Speaker 1: thinking about the problems, the problem you want to solve
Speaker 1: and what you want to dedicate your career too, it
Speaker 1: just really interested me. And what's the key insight that
Speaker 1: you had that enabled you to think about retirement in
Speaker 1: a different way. Well, you know, it's evolved over the years.
Speaker 1: Even in grad school, I was thinking about risk. My
Speaker 1: dissertation was looking at the move from define benefit to
Speaker 1: define contribution plans, and I wasn't totally sold that define
Speaker 1: benefit plans were that great that I saw all these
Speaker 1: risks they did pose to people, the risk that this
Speaker 1: company is going to go to business and you're gonna
Speaker 1: have to take a huge haircut. I mean this as
Speaker 1: big as you right now in Chicago. Who I believe me,
Speaker 1: I know, Chicago unheedgeable risk for the participants in the
Speaker 1: retirement or even if you back in the day, you know,
Speaker 1: if you change jobs, you would often give up your pension.
Speaker 1: So I was interested in what was risk here to
Speaker 1: find contribution to find benefit. But back then I was
Speaker 1: a macroeconomist, so I was thinking about it in a
Speaker 1: more of a policy way. Then after grad school, as
Speaker 1: I said, after I had blow up my career when
Speaker 1: I realized I didn't want to be a professor, and
Speaker 1: when you're in grad school, that's all you're told you
Speaker 1: could ever be. So I was kind of lost, and
Speaker 1: so I made a really sot of questionable decision to
Speaker 1: be a journalist. Oh that's not a question about decision.
Speaker 1: Who would call that a question about decision company. It
Speaker 1: was like, I mean, granted, every year they tell your
Speaker 1: journalism is dead, but there's two thousand and six, and
Speaker 1: it was actually a really good time to do this
Speaker 1: because it was the very early days of web journalism
Speaker 1: when everyone thought this was going to disrupt the market,
Speaker 1: but also didn't take it seriously at all. So the anyway,
Speaker 1: I had no idea how to write. We sort of
Speaker 1: just let me come in and write for them for
Speaker 1: free on the web because they just were like, well,
Speaker 1: this's the web most of the time. If you decide
Speaker 1: you want to life change, you want to write for
Speaker 1: the economist. That does not work. They want people who
Speaker 1: actually know how to write that's important to them. But
Speaker 1: they let me do it because it was web days
Speaker 1: and they were pretty open. So for all your calibration
Speaker 1: of risk, what role do you think luck place? Because
Speaker 1: to some extent that two thousand and six move, was
Speaker 1: luck right with timing or would you think about it differently?
Speaker 1: I was. It didn't feel like it at the time.
Speaker 1: In retrospect, it was because I learned this amazing skill,
Speaker 1: skills about storytelling, skills about how to communicate economics to
Speaker 1: an audience, which you certainly don't learn at academia. But
Speaker 1: I think everyone gets a certain amount of luck in life,
Speaker 1: but it's about knowing how to capitalize on it and
Speaker 1: knowing how to hedge or be prepared for bad luck too.
Speaker 1: I mean, at the time it looked like bad luck.
Speaker 1: I had no job, I was graduating from an economics
Speaker 1: PhD with no job. I was writing for free for
Speaker 1: the web. I mean, this did not seem like a
Speaker 1: good move. So before we leave the topic of retirement alone,
Speaker 1: I want to come back to it because one I
Speaker 1: want to ask you how big an issue is this today?
Speaker 1: I mean, it's a big issue. I think it's not
Speaker 1: the issue people think it is. I think everyone's convinced
Speaker 1: they just don't have enough money, and that's true for
Speaker 1: a lot of people. I don't want to minimize that,
Speaker 1: but I think that's not the primary issue. The primary
Speaker 1: issue is that people don't and this is one of
Speaker 1: the reasons I really want to do the book have
Speaker 1: the tools to really manage They've been handed a huge
Speaker 1: risk problem with the fine benefit plans. The employer took
Speaker 1: all the risk. Now we've a fairly significant risk problem,
Speaker 1: a problem I've dedicated my whole career too, and still
Speaker 1: haven't found easy answers to on everyone. So people don't
Speaker 1: really have the tools to manage this incredibly complicated risk problem.
Speaker 1: And I think a lot of the personal finance literature
Speaker 1: gets people, leads people stray or sets them up to
Speaker 1: feel like they're failing because they's like, oh, it's easy,
Speaker 1: just do this, but aren't really addressing all the risks
Speaker 1: they're really facing. So we've dumped a problem on people
Speaker 1: and not given them the tools to manage it. In essence, Yeah,
Speaker 1: and it doesn't mean it's not workable. I mean, I
Speaker 1: think the defined contribution for on K structure can really
Speaker 1: work and give people secure retirement. But we have to
Speaker 1: think more holistically about the tools and think of it
Speaker 1: as this lifetime problem rather than well, I don't know,
Speaker 1: you just save and invest and we'll see what happens,
Speaker 1: and perhaps get people to think about it like you do,
Speaker 1: as a beautiful, pure problem rather than it's some force
Speaker 1: of existential dread hanging over all of our heads. Right.
Speaker 1: I wanted to go back to this notion as well,
Speaker 1: of the concept of risk free. I loved this quote
Speaker 1: you use from Gerta. The dangers of life are infinite,
Speaker 1: and among them is safety. Is there actually something as
Speaker 1: simple as risk free? Well? There is. I don't think
Speaker 1: a lot of people appreciate this, certainly not a lot
Speaker 1: of people actually, who even work in finance, that the
Speaker 1: foundation of all risk problems is the risk free asset.
Speaker 1: If you look at any of these complicated formulas that
Speaker 1: people do to price risk, there's always the risk free
Speaker 1: rate in there. So it is in some ways it
Speaker 1: calibrates how much risk you can take. Because if a
Speaker 1: savings bond is going to pay you sent inflation adjusted
Speaker 1: to return, why bother going into the stock market. So
Speaker 1: it determines how much risk you take, and it determines
Speaker 1: how much you put a price on risk. Obviously you
Speaker 1: know there's problems with it and that people sometimes get
Speaker 1: confused about, Like as I said, I assume more education
Speaker 1: was a risk free asset. That's not always true. Or
Speaker 1: people assume a bond can't default when it does, so
Speaker 1: there can be this illusion of either you've defined risk
Speaker 1: free wrong, or you think something's risk for you when
Speaker 1: it's not and that can create other risks. Or I
Speaker 1: just wrote a fun story which I was ripping off
Speaker 1: of the book, which is sometimes I worry that you
Speaker 1: know the risk free value of not leaving your home,
Speaker 1: which is sort of the risk free choice, right, Like
Speaker 1: I'm not gonna bother go home, I'm gonna do stay,
Speaker 1: I'm gonna watch Netflix is supposed to Leaving your house
Speaker 1: is always a risk. You don't know it's gonna happen.
Speaker 1: You might have a bad time, you could get hit
Speaker 1: by a car, or something great could happen. It's just
Speaker 1: like stay at home. It's become so compelling, Like maybe
Speaker 1: this is why millennials apparently aren't having sex anymore. Is
Speaker 1: it's just like why go out on a Tinder date
Speaker 1: when you can watch Game of throwing good questions? But
Speaker 1: is staying at home truly risk free or is that
Speaker 1: an illusion? It's an illusion or in the short run,
Speaker 1: or maybe it is. I mean, maybe you feel like
Speaker 1: I can have a very satisfying life never leaving the
Speaker 1: house and playing video games and watching Game of Thrones.
Speaker 1: But it's important to take risks. It's, you know, it's
Speaker 1: how your life moves forward. It's how you actually will
Speaker 1: meet people and have a career and do all these
Speaker 1: important things that will ultimately make you happier. I mean,
Speaker 1: maybe the fact that people are staying in more and
Speaker 1: have become more attached to these sort of risk free
Speaker 1: sources of stimulation is also why there's higher rates of
Speaker 1: anxiety and depression. It goes back to that Gerta quote,
Speaker 1: I think the dangerous of life are infinite, and among
Speaker 1: them as safety. It's an incredibly compelling quote. So what
Speaker 1: do you think people misunderstand most about risk? I think
Speaker 1: they tend to think of it as this binary thing,
Speaker 1: like either you're a risk taker or you're not, or
Speaker 1: either you take a risk in you're someone who shakes
Speaker 1: up things or you don't. But really risk has this
Speaker 1: huge middle ground that you calibrate to whatever your preferences. Are,
Speaker 1: or whatever your desires are, and once you take a risk,
Speaker 1: you can manage it. I mean, I think people to
Speaker 1: call this a calculated risk, although they don't really able
Speaker 1: to say what that means, which is, you take a
Speaker 1: risk where you're very clear about what it is you want,
Speaker 1: and then you do things to manage the downside. Like
Speaker 1: your sex workers, like Shelby Starr, she takes a risk
Speaker 1: and then she figures out how to hedge against the
Speaker 1: risk she's taking, exactly like becoming a sex workers a
Speaker 1: fairly risky job, even if you're in the bunny ranch
Speaker 1: where you have access to healthcare and they get tested
Speaker 1: for diseases and they don't worry about breaking the law.
Speaker 1: I mean, there's still a huge social stigma. So do
Speaker 1: optimism and pessimism have anything to do with risk reward?
Speaker 1: Whether people consider themselves a risk taker or not? In
Speaker 1: other words, are optimists more inclined to take risk and
Speaker 1: pessimists not? Or does it not break down that simply?
Speaker 1: I don't know if it breaks down that simply. I mean,
Speaker 1: certainly people decide whether or not to take a risk.
Speaker 1: There's I mean, it might not happen as precisely as
Speaker 1: financial economists do, but I think there's some calculation that
Speaker 1: goes on on what the odds these are going to
Speaker 1: work out for me? And if you think it's definitely
Speaker 1: not going to be a problem and it's definitely going
Speaker 1: to go your way, you're more inclined to do it.
Speaker 1: On the other hand, if you only see downside, you're
Speaker 1: less likely. But that might be partially driven by personality
Speaker 1: and outlook, and it also might be driven by the
Speaker 1: cues or personal experiences with risk of how comfortable we
Speaker 1: feel taking on risk. So you mentioned the odds, and
Speaker 1: something else I thought was fascinating about your book is
Speaker 1: how bad we are probabilities. Why are we so bad
Speaker 1: at understanding probabilities when they are so key to life? Well,
Speaker 1: I think it's partially that probabilities are a relatively new invention.
Speaker 1: We take them for granted. And I always say new,
Speaker 1: I mean, like, you know, like late Renaissance, which seems
Speaker 1: like a long time ago, but it seems like a
Speaker 1: long time ago, and the granted scheme of human evolutions. Yes,
Speaker 1: it's a relatively recent invention, so it's not shocking that
Speaker 1: our brains don't conform to it, especially if you compare
Speaker 1: it to say reading. Reading also isn't something we're more
Speaker 1: knowing how to do. It's something we're trained to do
Speaker 1: when we take it for granted. But yet we've been
Speaker 1: trained for centuries exactly. It's opposed to we don't. We've
Speaker 1: come up with probabilities, but we never really trained people
Speaker 1: to think about them, how to make sense of them.
Speaker 1: Yet we expect people to just to be born knowing
Speaker 1: how to do it. But I mean, they're a modern
Speaker 1: construct in the same way the written word is. I
Speaker 1: came across the work of Gigerenzer, who believes that you
Speaker 1: actually really can make sense of uncertainty and risk and
Speaker 1: make conferences about probabilities that actually makes sense. It just
Speaker 1: depends on how it's presented. It's like, if you think
Speaker 1: in terms of frequencies instead of a raw probability, people
Speaker 1: tend to make better choices. Give me the most compelling
Speaker 1: example of how you can think, how you can think
Speaker 1: smartly about probabilities. So when you hear a probability, you know,
Speaker 1: rather than sink, okay, there's a point a one percent
Speaker 1: chance of something happening. That doesn't really have a lot
Speaker 1: of meaning now, but a one in one thousand does
Speaker 1: I mean, you even see this with a lottery. Statistically,
Speaker 1: it's very unlikely you're going to win the lottery. But
Speaker 1: when you get that ticket or you see how the
Speaker 1: lottery markets itself, they always plant in your head this
Speaker 1: whole story. They make the frequency seem larger than it is.
Speaker 1: They trot out the winner. They have that little song
Speaker 1: when you are buying the lottery ticket says you can't
Speaker 1: win if you don't play. So all of a sudden,
Speaker 1: that infintestinal probability seems much larger it is. But you
Speaker 1: could think about it in terms of frequency. They don't
Speaker 1: do this is they could line up all the people
Speaker 1: who played and didn't win. So you have to cut
Speaker 1: through the hopes and fears and desires and get to
Speaker 1: the numbers, and get to the numbers presented in a
Speaker 1: way that makes sense to your brain exactly. Okay, So
Speaker 1: let's think about risk as it's applied to various industries.
Speaker 1: And I loved the story that you tell in the
Speaker 1: book of Ryan Cavanaugh and Hollywood. What does that teach
Speaker 1: us about risk? I think about why risk measurements so
Speaker 1: hard because we make we come up with these probabilities.
Speaker 1: They have to be based on something and they're usually
Speaker 1: based on data because you know, what else do we
Speaker 1: have to go on. Problem is data is always by
Speaker 1: definition from the past, and you know the past changes.
Speaker 1: So Ryan Cavanaugh was this whiz kid type who came
Speaker 1: to Hollywood, and people in Hollywood are it's a big
Speaker 1: financial risk to finance a movie is that you know
Speaker 1: you don't pay starting for years. Most of the movies
Speaker 1: lose money, and you know you have to put all
Speaker 1: the money up up front. So he promised that he
Speaker 1: had an algorithm that would predict movie success. And everyone
Speaker 1: signed on to this because everyone wants to believe this
Speaker 1: the magic algorithm. Yeah, and it was a good timing
Speaker 1: for him, and that the studios were looking for new
Speaker 1: sources of financing and hedge funds were looking for higher yields. Plus,
Speaker 1: people from this finance world tend to like the glamor
Speaker 1: of movies and tend to not make great choices when
Speaker 1: they have that glamor presented to them. So he kind
Speaker 1: of married these two groups by telling these hedge funders secret,
Speaker 1: I can quantify risk the same way you like to
Speaker 1: do it. And the movie students where he's like, I
Speaker 1: can bring in money. So they gave him all their
Speaker 1: best data, and he had this fancy model that he claimed,
Speaker 1: I can put a number on a chance and movies
Speaker 1: gonna make it or not. And he put together slights
Speaker 1: of movies and it worked for a couple of years,
Speaker 1: but then it didn't because the movie industry just keeps changing.
Speaker 1: Data gets stale so fast. In the time I think
Speaker 1: he was in business, DVD sales came from a significant
Speaker 1: source of revenue to just completely disappearing. Or you get fads,
Speaker 1: you have China coming into the market and all of
Speaker 1: a sudden, comic book movies become more important, or you
Speaker 1: have rotten tomatoes that totally undermine all the marketing plans.
Speaker 1: So the data just becomes so stale and almost worthless.
Speaker 1: And so if you're going to use a model like this,
Speaker 1: you always have to stay on top of it. That's
Speaker 1: interesting I was thinking as you were talking about how
Speaker 1: it ties back to our previous discussion about the hopes
Speaker 1: and dreams and fears clouding the numbers, because you can't
Speaker 1: think of a better example than Hollywood, right where the
Speaker 1: glamor of the movies clouds people's judgment about what the
Speaker 1: numbers actually are. But it's also a fascinating industry given that,
Speaker 1: as you note, there's more and more data, but the
Speaker 1: limitations are also more and more imparent in the face
Speaker 1: of all this rapid change, and how do you deal
Speaker 1: with that from the standpoint of risk management, Because it's
Speaker 1: a broader issue than Hollywood, right, it's a facet of
Speaker 1: our world, more and more data and more and more change. Yeah,
Speaker 1: so I think where we're maybe going wrong as thinking
Speaker 1: data just speaks for itself, which is I think when
Speaker 1: the lessons from this Ryan have an off parable. Data
Speaker 1: is a great tool. I'm not saying we shouldn't use data,
Speaker 1: but we have to be mindful of how we use it.
Speaker 1: And right now with movies, we've got the best data
Speaker 1: we've ever had, and that with Netflix and Amazon, they
Speaker 1: have actual data on who very finally tune demographics of
Speaker 1: who starts and stops movies and what do they attract
Speaker 1: in a way we never had before. So the potential
Speaker 1: is there to really do marketing a lot cheaper and
Speaker 1: to really start tellering movies to more what's considered niche
Speaker 1: audiences like women. But you have to be smart and
Speaker 1: how you use your data, and you can't assume that
Speaker 1: the data is just there. You have to be very
Speaker 1: mindful of is this data relevant to what you're interested in.
Speaker 1: I think I use the parallel to using pass trips
Speaker 1: to the airport to calibrate the risk you're going to
Speaker 1: be late to the airport if you go to the
Speaker 1: airport and most of the time it's between twenty five
Speaker 1: and thirty five minutes. You based your airport travel time
Speaker 1: based on your past history. But like, what if they
Speaker 1: build a new highway to the airport that just completely
Speaker 1: changes your route, then all those pass trips aren't really
Speaker 1: useful anymore, even though they're still implanted in your brain
Speaker 1: somewhere as a mental as a mental roadmap. I think
Speaker 1: you used the phrase in the book, which I've always
Speaker 1: loved about models, garbage in, garbage out. Yeah, And that's
Speaker 1: what they said about Ryan's model. I spoke to a
Speaker 1: lot of banks that have their movie business, and they
Speaker 1: were incredibly candid, one about people who go in and
Speaker 1: invest naively in two about just who've been working on
Speaker 1: financial models for years, about all the weaknesses in Kavano's model.
Speaker 1: Speaking of weaknesses and models, that's something I've actually been
Speaker 1: fascinated with for a while, and I was just reading
Speaker 1: about a finance guy named Emmanuel Derman math Quondai who
Speaker 1: started to question models in the wake of the financial crisis,
Speaker 1: And he said this to confuse a model with the
Speaker 1: world of humans as a form of idolatry and more dangerous.
Speaker 1: And I started to think after the financial crisis that
Speaker 1: the phrase risk management was actually an oxymoron, sort of
Speaker 1: like jumbo shrimp, because the finance industry clearly didn't do
Speaker 1: a very good job of managing risk in the run
Speaker 1: up to the crisis. So what did they get wrong?
Speaker 1: Where did their models go wrong? Well, a couple of
Speaker 1: different ways. My mentor was Robert Merton, who was the
Speaker 1: creator of a lot of these models and also known
Speaker 1: for a hedge fund that didn't go well himself. That
Speaker 1: was long term capital management, wasn't it, Yes, hedge fund.
Speaker 1: He always says that when you're assessing the viability of
Speaker 1: a model, there's two things you have to keep in mind.
Speaker 1: One is the quality of the model. Sometimes it's just
Speaker 1: a bad model. Two is the right model being used
Speaker 1: for the right job. It's like, are you using a
Speaker 1: screwdriver to do a job that a hammer should? And
Speaker 1: three is the person using it know what they're doing,
Speaker 1: and I think with a financial crisis you can see
Speaker 1: a lot of all of those happening. Sometimes people use
Speaker 1: models that just weren't great. Sometimes people use perfectly good
Speaker 1: models but just in the wrong way. And sometimes they
Speaker 1: just applied it to the wrong problem. And this is
Speaker 1: why I think you need one more education about what
Speaker 1: these models are and what they can do, and also
Speaker 1: more awareness of what their natural limitation are. That doesn't
Speaker 1: mean you shouldn't use them. I feel like a lot
Speaker 1: of people who critique financial models, I'm not quite sure
Speaker 1: what their point is is that should we just not
Speaker 1: do anything, Should we just not bother to measure or
Speaker 1: manage risk? Should we just guess? Yeah? Because I like
Speaker 1: to think of risk models as like a roadmap if
Speaker 1: you're taking a trip. I mean, it does not guarantee
Speaker 1: a safe journey. You might still get into a car accident.
Speaker 1: But what it does is it helps you understand how
Speaker 1: different factors relate to each other, what makes what enhances
Speaker 1: your risk, what things might get in your way. But
Speaker 1: it doesn't have every tree in there. You know, you
Speaker 1: have to make abstractions, you have to make choices, But
Speaker 1: it can really help the odds that you're gonna have
Speaker 1: a successful journey, but it doesn't really make any guarantees.
Speaker 1: So I mean, and this is why you have to
Speaker 1: be careful of its limitations and also make sure you're
Speaker 1: using it right. Switching gears a little bit, what do
Speaker 1: you think technology CEOs could stand to learn from sex workers.
Speaker 1: I think it's about hedging, like you see them, and
Speaker 1: I mean, maybe it's because they've been getting capital with
Speaker 1: having to earn a profit that they just feel like
Speaker 1: it seems like that they aren't really think being very
Speaker 1: thoughtful about risk. I mean, you look at Facebook, and
Speaker 1: you look at their early days, and they were so careful,
Speaker 1: Like even the rollout was so slow and deliberate because
Speaker 1: they were heating the lessons from friends stir that just
Speaker 1: tried to meet their demands and overstretch themselves and had
Speaker 1: a bad product. So Facebook would it only roll out
Speaker 1: very slowly anyway, They weren't meeting the demand of all
Speaker 1: their customers, which is kind of risky when you're trying
Speaker 1: to build a social network and getting a core group
Speaker 1: of users quickly is important. Right, It's a conundrum. Yeah,
Speaker 1: but they hedged by being like, well, we'll give up
Speaker 1: that upside and maybe not catching on so we can
Speaker 1: make sure we have a stable, reliable product. Nothing about
Speaker 1: that behavior seems true of the company today. I mean,
Speaker 1: you think in the last five years they kept getting
Speaker 1: caught sharing people's data and saw that regulators and individuals
Speaker 1: were very uncomfortable with it, yet they still kept doing it.
Speaker 1: So same CEO, What do you think changed? Why does
Speaker 1: somebody who carefully calibrates risk at one moment of their
Speaker 1: career becomes somebody who does not. Well, maybe if you
Speaker 1: have success in the past, do you think it's gonna
Speaker 1: be the future. I spoke to a lot of criminals
Speaker 1: and they all said the same thing. They're all like, well,
Speaker 1: you know, I started breaking law of super nervous, but
Speaker 1: then I didn't get caught, So I thought that then
Speaker 1: I wouldn't get caught in the future. So they just
Speaker 1: kept taking more and more risk, and eventually, as they
Speaker 1: took more risk, they also exposed themselves to a higher
Speaker 1: likelihood of getting caught. And then they got caught and
Speaker 1: they were all shocked. What about Elon Musk So I
Speaker 1: think of him as a major risk taker, right, And
Speaker 1: I was thinking of this ts Elliott quote that you
Speaker 1: actually use in your book, which I love as well.
Speaker 1: Only those who risk going too far can possibly find
Speaker 1: out how far one can go, And that seems to
Speaker 1: be musk. But in your view, how is he calibrating it?
Speaker 1: Is he going too far or is he finding out
Speaker 1: how far he can go? I don't think he's internalized
Speaker 1: that he can go too far, you know, And I
Speaker 1: suppose I would have to agree with that. I mean,
Speaker 1: it's a problem I would like to have. But maybe
Speaker 1: being that successful that early make you feel like somewhat invincible.
Speaker 1: There's something else that people are talking about a lot
Speaker 1: in the world of innovation that I think is really interesting,
Speaker 1: which is this ongoing debate about core versus edge. How
Speaker 1: do you decide when to focus on the core of
Speaker 1: your business versus how you figure out about going out
Speaker 1: on the edge? And if you leave the core unchanged,
Speaker 1: then sometimes that can make you get taken over by
Speaker 1: somebody else, right, But if you go too far out
Speaker 1: on the edge, then that can mean you lose focus
Speaker 1: on the core. Does anything in the way you think
Speaker 1: about risk help answer that question? Well, yeah, I mean
Speaker 1: you want to probably do a little bit of both,
Speaker 1: because you know, if you focus too much on the core,
Speaker 1: you risk losing your edge and becoming obsolete. I don't
Speaker 1: know if this is good or bad, but it's just different.
Speaker 1: I think what we're seeing a lot with companies now
Speaker 1: is that they're outsourcing their edge. Oh what do you
Speaker 1: mean by that? Well, if they're letting other smaller companies
Speaker 1: do the innovation than they buy them. Ah, And do
Speaker 1: you think that's ultimately a flawed way of going about things?
Speaker 1: Imprissiple know, but I think in practice often it is
Speaker 1: like anyone I know who's had an edgy startup, and
Speaker 1: I've been part of that too, and then you're bought
Speaker 1: by a big company. It's never off. It's a very
Speaker 1: hard transition because you have a corporate culture that's become
Speaker 1: very like, sort of adverse to innovation, and then you
Speaker 1: bring this innovative product in and it's a hard miss
Speaker 1: and the corporate culture smothers the edge exactly. Oh that's interesting.
Speaker 1: I haven't thought about it that way before, but that
Speaker 1: makes sense. So there's another quote in your book that
Speaker 1: I love that goes to the heart of some of
Speaker 1: what we're talking about. Our capacity for self delusion. And
Speaker 1: you wrote, I am irrational and I know it, so
Speaker 1: to talk about that and talk about the role that
Speaker 1: self knowledge plays and how you manage risk, how you
Speaker 1: think about risk. Well, I mean there's all this literature
Speaker 1: about people have these behavioral biases and so they don't
Speaker 1: manage as well. We talked before about how you tend
Speaker 1: to be over or under confident and for probabilities accurately.
Speaker 1: But there's also a loss of version, which is we
Speaker 1: hate losing so much. When we're faced with loss, will
Speaker 1: take way bigger risks and lose even more often, and
Speaker 1: that's not good. And I think I wrote about this
Speaker 1: poker player named Phil Helmuth who is a complete lunatic,
Speaker 1: like I mean, really it sounds like a complete lunatic,
Speaker 1: no self control, although he said he wasn't happy with
Speaker 1: the way I portrayed him. Although I was, like, you
Speaker 1: named your bio poker Brett talk about the limitations of
Speaker 1: self knowledge. But he is actually very self aware in
Speaker 1: a lot of ways, and this is why he's been
Speaker 1: a successful poker player, is you know. Anyway, he's famous
Speaker 1: for having these tantrums after he loses, or kind of
Speaker 1: being this bully especially, I mean, he's not afraid to
Speaker 1: brate amateur small poker players. Anyway, He's one of the
Speaker 1: superstars in his industry. But when you're actually play poker,
Speaker 1: if you let the emotions get the better of you
Speaker 1: and get too aggressive when you're down, I mean, he
Speaker 1: really hates to lose. You would think when he was
Speaker 1: down he'd be desperate to get back up, but he's not.
Speaker 1: He's a very consistent player and he's very patient. He
Speaker 1: only pays twelve percent of his hands, which is much
Speaker 1: lower than average. Wow, that juxtaps and does not seem
Speaker 1: to go together you temper tantrums and the patients. But
Speaker 1: he's trained himself, and he has all these tricks like
Speaker 1: he knows he's irrational. He knows these are traps that
Speaker 1: he falls into and everyone falls into. So it becomes
Speaker 1: his edge and that he's a completely different person in
Speaker 1: his style play. So that gets to another key question
Speaker 1: I wanted to ask, by understanding your own approach to risk,
Speaker 1: or by understanding risk more broadly, can you actually improve yourself?
Speaker 1: And the answer in the case of Philhelmuth would appear
Speaker 1: to be yes, Yes, I think anyone can. It's also
Speaker 1: true people talk a lot about how humans are irrationally
Speaker 1: of all these risk biases. But when people say that
Speaker 1: it's not really entirely true, because we change as we
Speaker 1: get older, As we learn more, older people tend to
Speaker 1: behave more like classical economists expect people to. Or there's
Speaker 1: research that people who take risks all the time tend
Speaker 1: to be more rational. So learning and awareness make a
Speaker 1: huge difference. What would you tell your kids about taking risks?
Speaker 1: That they should take them, that they should feel good
Speaker 1: about taking them. Yep, back to that girl to quote again, right,
Speaker 1: the evolution of safety is sometimes the most dangerous think
Speaker 1: or however, I'm mangling it. Yeah, especially because people say,
Speaker 1: you know, I don't know this is true. I think
Speaker 1: every generation is the same, more than people give them
Speaker 1: credit for. But they keep saying that kids don't take
Speaker 1: risks anymore. But I'm not sure if that's really true.
Speaker 1: So I think that is an important message. Well, thank
Speaker 1: you for coming in and chatting with me. Thanks for
Speaker 1: having me. As Alison and I talked, I kept thinking
Speaker 1: that in unexpected ways, finance does have a lot to
Speaker 1: offer life. We could all learn to be smarter at
Speaker 1: managing risk. But of course, some of the key questions
Speaker 1: you need to answer in order to employ Alison's strategies
Speaker 1: are pretty deep. Philosophical ones, what is it that you want?
Speaker 1: And if there's danger in safety, is there really such
Speaker 1: a thing in life as a risk free rate from
Speaker 1: which you can benchmark? So maybe in the end, life,
Speaker 1: with all its uncertainty and messy human dimensions, has more
Speaker 1: to teach finance. Making a Killing is a co production
Speaker 1: of Pushkin Industries and Chalk and Blade. It's produced by
Speaker 1: Ruth Barnes and Rosie Stoffer. My executive producers are Alison
Speaker 1: mcclein no relation in Making Casey. The executive producer at
Speaker 1: Pushkin is Nea Loebell. Engineering is by Jason Gambrell. Our
Speaker 1: music is by Jed Flood. Special thanks to Jacob Weisberg
Speaker 1: at Pushkin and everyone on the show. I'm Bethany McLean.
Speaker 1: Thanks so much for listening. Find me on Twitter at
Speaker 1: Bethany mac twelve and let me know who you've enjoyed
Speaker 1: hearing from.