The Success Equation: Untangling Skill and Luck | Michael Mauboussin | Talks at Google

MALE SPEAKER: Hello everyone. Thank you all for coming. And people joining remotely are welcome too. Today we have a very, very special speaker, someone whose work I deeply admire and whom I am fond of. He was thinking about how best to present it. Should I say he is CEO of Credit Suisse? Should I say that he is an adjunct professor at Columbia? Generally, not all professionals are good academics or appreciate academia. And not necessarily all academics are good professionals. Today at lunch our speaker said: I love teaching and writing. And those are the two things he likes the most.

So I think that's the best way to present it. He is a teacher and writer. So, without further ado, ladies and gentlemen, please join me in welcoming Michael Mauboussin. MICHAEL MAUBOUSSIN: Thank you. And good afternoon. It is a real pleasure to be here and I look forward to today's debate. Today's topic is an endlessly fascinating topic about

skill

and

luck

. I'll tell you my own

luck

y story. When I was a senior in college I had no idea what I wanted to do with my life, but I did know I needed a job. I went to school in Washington, DC, and one of the companies that came to hire was Drexel Burnham Lambert.

Now this is probably before most of your time. But at the time it was a pretty popular investment bank. At the on-campus interview, he did so well that they invited me to New York for the big day. So I take off my best suit, shine my shoes and go to New York. On the day of the big interviews, we sit in a conference room and they say, here's the basic setup. You will have six interviews with various people in our training program and you will have 10 minutes with the boss. So of course you want to look good all day.

But during those 10 minutes make sure you have your A-game. So I review my six interviews. They are going reasonably well. I have my 10 minutes. I'm led into this guy's huge office and I see a Washington Redskins trash can peeking out from under his desk. Now I've been going to school in Washington. The Redskins were good back then. So I said to the guy without thinking, "That's one hell of a trash can." This hits the boy in the emotional seat. And my 10-minute interview turns into 15 minutes of him talking about the virtues of athletics and how the football team is a metaphor for life.

And I basically go like this all the time. Then I go back to school. A couple of weeks later I received the coveted letter in the mail: you have been offered this job. This is great. I am gainfully employed, moving to New York and starting my job. About three months later, one of the older kids in the program takes me aside, puts his arm around me, and says, Hey, kid, you're doing good. Everything's fine. I just want you to know. But I have to tell you something now. All six people he interviewed voted against hiring him.

Very reassuring, right? Then I thought: Well, why am I here? And he said, well, the older guy came in and overruled all of his decisions. And he insisted that we hire you. That's why I like to say that my career started with a trash can. And that was pure luck. And luckily there are no Laszlo Bock algorithms involved in that either. Otherwise, he would still be unemployed. Today's topic is

skill

and luck. I really want to talk about three things. First I want to define my terms a little bit and talk about what I call the three easy lessons: three things you can learn pretty quickly.

In the middle part, the meat of the discussion, I want to talk about the complexity of skill and luck: what they look like and how they change over time. And finally I will finish a little with what to do about it, but also with why it is so difficult for us in our lives to understand the role of luck. One way to think about this is to think of a continuum of activities. On the far left... pure luck, no skill. You could think of lotteries or roulettes. On the far right... pure skill, no luck. Maybe running races or playing chess would be there.

It is very important to define the terms. So let me take a moment to do that. I am going to define skill taken from the dictionary, which is the ability to apply one's knowledge easily in execution or performance. Then you know how to do something and when you are asked to do it, you can do it on cue. Now, as you can imagine, luck is much harder to define. In fact, it extends to moral philosophy quite quickly. But I am going to say that luck exists when three conditions are met. Number one is that it operates for an individual or organization.

That happens to you, your favorite team or your company. The second is that it could be good or bad. And I don't want to suggest that it is symmetrical because we will see in a moment that it certainly is not symmetrical. But there is a plus sign and a minus sign possible. And third, it is reasonable to expect that a different result would have occurred. So if we were to rewind the time tape and play it again, it is reasonable to expect that a different result would have occurred. Now, what I've done here for fun is organize professional sports leagues based on a season of performance.

So this is where they really lie on the luck/skill continuum. That's the NBA, Barclays Premier League Soccer, Major League Baseball, the NFL and finally the NHL. Now I'll mention another thing about luck and skill, which is pretty funny. There is a really interesting test I learned from poker people to find out if there is any skill in an activity. And that is, ask if you can lose on purpose. If you can lose on purpose, there must be some skill. If you can't lose on purpose, it's basically all luck. So that's another little litmus test to figure it out.

I'm going to go over this very quickly, mainly in preparation for the upcoming discussion. You could imagine that your outcome in life is based on a distribution of luck and a distribution of skills. So I take the two results and combine them, and that will be my result. So on the far left it would be a luck distribution, and you only get zeros for skill. So only luck will make the difference. On the right, they will be drawn from a distribution of skills and lucky zeros. So only skill matters. And then everything in life is going to be a distribution for each one.

So we're drawing these distributions. So this very simplistic setup allows me to continue with what I call the three easy lessons. Lesson number one: Whenever you see an extreme outlier, and by the way, most of the outliers we see are positive outliers because negative outliers almost always die, whether literally or metaphorically. So whenever you see a positive outlier, it's always a combination of great skill and a lot of luck. And if you think about it for a moment, it really has to be true. It is a right hand draw of both distributions. Now there are many ways you could demonstrate this.

One of the convenient ways is the world of sports. This, of course, is Joe DiMaggio, who hit in 56 consecutive games in 1941. DiMaggio was a career .325 hitter, one of the best of all time. And it turns out that if you look at all the players with 30 or more hitting streaks, his career batting average is over .300. They are approximately one and a half standard deviations above the mean. Put this very differently: not all skilled players have streaks, but all streaks are had by skilled players. And it makes sense, right? Because skill is the prerequisite, and then luck adds up to become an outlier.

The second observation, second lesson, has to do with reversion to the mean. By the way, mean reversion is a fascinating concept because I think most people have a sense of knowing what it means. But if you really look at most people's behavior, they don't act like they understand mean reversion. So let me get technical. You all understand this. Mean reversion says that an outcome that is far from the average will be followed by an outcome with an expected value closer to the average. Good? Now here's the classic example, which you may remember from your statistics class years ago: the heights of parents and children.

Very tall parents have tall children. But the children's heights are close to the average of all children. And in the same way, short parents have short children. But the children's heights are close to the average of all children's heights. Now, this is the one thing that I think is very interesting and practical about this reversion to the concept of mean and the skill/luck continuum, and that is that it turns out that where you are on the luck/skill continuum defines the rate of reversion to the half. not only that it occurs, but also the rate of reversion to the mean.

So, for example, if you are on the purely lucky side of the continuum, there is complete reversion to the mean. In other words, the expected value of the following outcome is some measure of the average, the mean, or, in some cases, the mode. If you are on the pure ability side of the continuum, there is no mean reversion. We run a sprint against Usain Bolt and he wins. We run again, he wins again. There is no return to the mean. So if you know where your activity is, you will automatically have an idea of ​​the mean reversion rate.

So if you're thinking about things like business performance or markets or whatever, it's a very, very useful and useful heuristic. Now, the third lesson is the one that has probably aroused the most interest in this book. It's a concept I call the paradox of ability. I want to make it clear that it is not my idea, but I gave it that name. And the paradox of skills says that in activities where both skill and luck contribute to outcomes, it is often the case that as skill increases (skill improves) luck becomes more important. So how can we have more skills so that luck matters?

Now I learned about this concept from Stephen Jay Gould, a very eminent biologist who wrote a lot about the theory of evolution and also liked to write about baseball. And one of my favorite essays that he wrote was about Ted Williams, who you see here. Ted Williams was the last player to hit over .400 in the Major Leagues. He also did it in the year 1941. So the question Gould was thinking is why has no one been able to replicate this feat for the next 70 years? He said maybe it's because players play at night, maybe because there are relief pitchers now, maybe...none of these things really worked.

The answer was that it turns out that the standard deviation of the ability of major league players has become narrower, that is, the difference between the best players and the average players is smaller today than it was a generation ago. So if he accepts that batting average is some skill plus some luck combined, if the standard deviation of skill decreases, that means the standard deviation of batting average should decrease. And that is, in fact, precisely what we have seen. Thus, the standard deviation of batting average in the 1940s was 0.032. And the standard deviation of the current batting average is about .027.

Put another way, Ted Williams was a four standard deviation event in 1941. And if you were a four standard deviation event in 2011, say 70 years later, you would hit .380. Now, .380 is obviously fantastic. But it does not exceed that .400 threshold. Now this is what I want to emphasize. It turns out that the paradox of ability is everywhere we look. This is very visibly true in the investment world, where the standard deviation of excess returns has been steadily declining for 50 years. It is true in the world of sports, as I have already mentioned. It is also true in the business world, for example, the quality of products.

It's much more uniform now than it was a generation or two before. I want to leave you with this thought. The distinction I make here is that absolute skill has never been higher. And that's true everywhere we look. But relative skill has never been so limited. That means more is left to chance. And I want to keep that in mind. In our modern world, more luck is being left to chance than in years past. We'll come back to that in a moment. So those are the three easy lessons. The skill paradox says that as absolute skill improves, if relative skill decreases, you will actually leave yourself more to chance.

The skill paradox actually makes a very specific prediction: in areas where there is no luck, we should see two things happen, if my description is correct. One is an absolute improvement, in this case reaching some physiological limit. I'm using athletic effort here. And the second is grouping because I'm suggesting that the right queue is coming in. That means that the performance is gathering people. So there are many ways I can show that this is actually happening. A convenient example is the men's Olympic marathon time. So the white line is the time of the guy who won the gold medal.

And it's possibleSee that guy ran, in 2012, about 23 and a half minutes faster than the guy who won the gold medal in 1932. So it's not a big surprise. But if you are a runner, your pace per mile increases by approximately one minute over 80 years. The most interesting line is the blue one, which is the difference between the guy who won the gold medal and the guy who came in 20th place. Now, mind you, these are supposed to be some of the best and fastest runners in the world. competing on the same stage. That difference was 39 minutes in 1932.

Now, let's look at that for a second. This is the Olympic marathon. The guy has won the gold medal, he's showered, he's eating a sandwich and this other guy is still on the field finishing up. That time has been reduced to about five or seven minutes today. And I think we can say with confidence that if we meet in the future, it will be in an even shorter period of time. This is how we see this convergence of times in swimming, rowing and running. And as you know, we now need incredibly sensitive devices just to know when players come out and when they finish so we can really measure which one is the fastest at that particular event.

Let me change the subject a little and now talk about skill/luck. I've been representing them with these normal distributions, but of course that's not the case. Skill...let me start with that. It's actually quite easy to describe. Skill tends to follow an arc. That's why we call it the arc of skill. Again, let me start with a physical effort. This is sport. So, for example, if you ever played a sport when you started when you were young, you don't have much competition. But as you practice, you get better and better. At some point you reach peak performance as you get stronger.

And then you go down the other side. So there is this very definable arc. By the way, in sports the best way to predict this is with slow twitch versus fast twitch muscles. It turns out that in sports with a lot of fast-twitch muscles, players peak when they are young. So the sprinters... 22, 23. The basketball players... around 20 years old. And then if there's more slow contraction, it typically peaks around age 20. So, baseball players...twenties. Some soccer players...also in their twenties. By the way, if you are a golfer, you have good news. Golf tends to plateau around age 30 to 35, then declines again after age 35.

So here's Tiger Woods, now 38 years old. So no matter what other problems he is facing now, Father Time is knocking on his door too. Here is the real arc of skill for tennis. These are quite interesting data. This is the winner of the men's Grand Slam tournaments in the Open era. From 1968 to 2013. At the top you can see the distribution. And it turns out that fashion is 24 years old. And it turns out that over the last 40 years or so, the average is actually 24 years. So, 24 years old is basically the sweet spot for winning men's tennis tournaments.

Now the bottom panel... it's a little hard to see from where you are... but the bottom is actually the age of every winner of every tournament since then, with a line at 30 years old. And what we know is in the last In 40 years, that is, 160 events, only four players over the age of 30 have won a Grand Slam. Only four players. By the way, we had a bit of hype about this just a couple of weeks ago with Roger Federer trying to win Wimbledon. He is 32 years old. He will turn 33 in the fall. There's just no one who's won Wimbledon over 30 since Arthur Ash did it in 1975.

So it was really... it just shows how incredible Federer is. But at his age, with 17 Slams, what happens is that physically you slow down a little bit. And that difference is the difference between you and a younger generation. So we will see if in men's tennis this new young generation begins to take over. This is a very, very pronounced pattern that we see in all sports and, you know, profitable. Now your reaction to that is probably something like, oh, that's interesting. Maybe it will help you in your fantasy sports league, or something. But what you care about is cognitive performance, right?

Psychologists study cognitive performance. And, by the way, it follows the same arc. And it turns out that psychologists generally agree that cognitive performance is a function of two types of intelligence: so-called crystallized and fluid intelligence. And you can see from the image that this is a bad news or good news situation. Let's start with the bad news: fluid intelligence. So fluid intelligence is effectively your ability to deal with novelty. So if I present you with a series you've never seen before, how good are you at figuring out what happens next? It turns out we have a couple of people who are probably at their best in the room.

Fluid intelligence tends to peak in your 20s and then goes downhill throughout life. So if you're out of college, basically, you're probably on a downward spiral in terms of your fluid intelligence. Crystallized intelligence (which is the good news, of course) is something that is exactly what it sounds like. It is your cumulative knowledge. It is your wisdom, understanding of facts, etc. That practically, except for cognitive decline, practically grows throughout life. So you can see that. And it is the combination that defines this general arc. So here are the basic tradeoffs. Now I just want to mention a couple of numbers and maybe ask you to participate.

I'm going to ask you a couple of numbers about peak age performance for certain tasks. So the first one I'll mention is that there was a study done on institutional money managers. So these are portfolio managers. And the question I'll ask you is: at what age do you think institutional money managers deliver the best excess returns? So at what age do institutional money managers deliver the best excess returns? Throw out some numbers. What do we hear? AUDIENCE: 72. MICHAEL MAUBOUSSIN: 70? God, I love that answer, by the way. I'm holding on to that one. AUDIENCE: 55. MICHAEL MAUBOUSSIN: 55. AUDIENCE: 45.

AUDIENCE: 40. MICHAEL MAUBOUSSIN: 40. 40 40. 45. Very specific. OK. So the answer... well, the real and exact answer is the range of 40 to 44. But let's call it 42 as average. Guys, some of those guesses are really good. However, let's keep that 70. I can modify that one. So, institutional money managers...around 40. The other one that was even more interesting to me was a paper by David Laibson and some colleagues at Harvard University. And this is where they used massive data sets for it. They went out and studied. Now, these are not people like you, but people from the real world, out there.

At what age do people make the best household financial decisions? So, at what age do people get the best terms on their auto loans? At what age do people get the best interest rates on their mortgages? That kind of things. So there were about a dozen categories and tens of thousands of people participating. At what age do people make the best household financial decisions? What number, how old do you think it is? AUDIENCE: 30 to 35. MICHAEL MAUBOUSSIN: 30 to 35. AUDIENCE: 55 to 60. MICHAEL MAUBOUSSIN: What was that? AUDIENCE: 55 to 60. MICHAEL MAUBOUSSIN: That's good. I like that. That's good. Someone else?

Yeah? AUDIENCE: 40 to 45. MICHAEL MAUBOUSSIN: So, 40 to 45. This is where I think that the older... my thinking was that older is better, in part because older people tend to have more assets than younger people, on average. , which is true. And really, for most people, there's usually no problem with cognitive decline even when you reach age 60. Turns out the age was 53. 53. And there's a standard deviation around that. But almost everyone in the average categories tended to converge at age 53. So the question I was asking myself is, why isn't it 60, for example? And it turns out that the cartoon version of that is that just as when we get older it becomes harder for us to physically react to things, as we get older we tend to become cognitively lazier as well.

We tend to become cognitively lazier. So I'm going to ask you to participate in this. Please don't shout the answer. You simply respond mentally or write it down if you wish. Here is a very simple question to try to illustrate this point. So here is the setup. Jack is looking at Anne. Anne is looking at George. Jack is married. George is not married. This is the question I would like you to answer. He is a married person looking at a single person. Alright? So Jack is married. He is looking at Anne. Anne is looking at George.

He is not married. The question is: is a married person looking at a single person? So there are three possible answers. A, yes. B, no. C, cannot be determined based on the information you provided to me. So let me ask this question. What is the first answer that comes to mind? AUDIENCE: C. MICHAEL MAUBOUSSIN: C, right? By the way, how many people C... the first answer comes to mind, right? So, of course, that's the wrong answer. But it's the first answer that comes to mind, right? Now let me see if I can replicate Mindset C. Look, I have two pairs to work with.

There are no tricks. I have two pairs to work with. Jack and Anne... I know Jack's marital status. The light bulb went on right there. You know Jack's marital status. You don't know Anne's marital status. So I can't say based on that pair. My second couple is Anne and George. I know George's marital status, but I don't know Anne's marital status yet. So I can't say. I'm basically stuck. Now the answer is A, yes. And the reason is that Anne only has two possible marital statuses. Either she is married or she is not married. And of course, that's exhaustive.

So let's assume for a moment that she is not married. Jack is looking at Anne. If a married person is watching...then the answer would be yes. And now change her status and say that she is married. Anne is looking at George. Does a married person look at a single person? The answer would be yes. Good. So no matter what I assume about her relationship status, the answer has to be yes for one of my partners. What happens now? By the way, I went through it in a hurry on purpose. At first everyone answers C. And the only way to get the right answer is to check your work.

Now, if I had given them all the time, I assure you that they would all have gotten the correct answer. But what happens is, as you get older, you're less likely to revise your work; You are literally less likely to proofread your work. The first answer that comes to mind... And if you want to use the language, that question is meant to evoke in your system... a... quick response. And unless you recruit your system (two) to answer that question correctly, you'll get the wrong answer. That is what happens. That's the cartoon version of what happens as we get older.

We follow general rules and heuristics. Now let me move on to the topic of luck, which is actually quite a bit richer than the topic of skill. And when we talk about luck, there's a very clear dividing line between activities that are largely independent and then path-dependent activities, where what happened before affects what happens after. And what I'm going to argue in these path-dependent processes is that there is an inherent lack of predictability and an inherent inequality. But let me first start with a simpler case: these luck-independent outcomes. In fact, in the book we use a baseball player named Adam Jones.

His batting average in 2011 (by the way, he's an All Star now) his batting average was .280. So we created a rotating model... 28% of the time, we got it right. 72% of the time, outside. We simulate 10,000 stations. We compare it with the real statistics of Adam Jones. And hitting isn't perfectly independent, but it's close enough for all intents and purposes, right? So basically it is a process close to independence; It's not perfect, but it's close enough. And there are other things in business that we can model after other things in life where that element of luck will be pretty easy to model and almost never perfect, but usually pretty good.

The most interesting case is that of path dependence. This is my second question of the day and it's much easier than the first. What is the most famous painting in the world? AUDIENCE: "Mona Lisa." MICHAEL MAUBOUSSIN: "Mona Lisa," right? That's a clue since everyone here is looking at the painting. There is the "Mona Lisa." By the way, how many people have seen the "Mona Lisa" in real life? Yeah? And what was your impression of the "Mona Lisa"? AUDIENCE: Small. MICHAEL MAUBOUSSIN: Little one. Anything else besides small? Any other descriptors? Inspiring? No, small. That's what people usually say.

Well, I have to say that my wife, Michelle,is here today. And last summer we took our entire family... we have five children... and our mother-in-law. So the eight of us went to Paris. And we, of course, went to the Louvre. We got a guide and spent a day there so our children would be as disappointed as we were a generation before. So we have perpetuated that tradition. Now you all know the basic story of the "Mona Lisa." It was painted by Leonardo da Vinci, of course, in the early 16th century. He was in Italy for several years in the early 16th century, but he's been in France continuously since about 1517.

So let's round it up and say 500 years, basically, in France. Now, the question that is interesting to ask is: why the "Mona Lisa"? By the way, the "Mona Lisa" gets 85% of the market share when that question is asked around the world. 85% of people spontaneously say the "Mona Lisa." By the way, I was curious about this because I was in Singapore, in Hong Kong, and I was giving a version of the talk. And I asked the people there, just as I asked you, and the answer came out just as quickly and decisively. So that really seems to be the case.

So the question is, why is the "Mona Lisa" the most famous painting in the world? And if you were an art critic you might say something like, well, there were only 11 or 12 da Vincis in the world. But it beautifully represented the movement in the background. He used oil paints. And you can always resort to the enigmatic smile of Mona Lisa. But notice that none of those things are truly unique to the painting and many of them are actually quite self-referential. So what if I told you that for most of "Mona Lisa's" existence, it wasn't the most famous painting in the world?

And I want to give you two proofs about it. The first is from 1750. The painting was in Versailles – about 20 kilometers from Paris – in the Royal Palace. And they said, we're going to take the 110 best pieces from Versailles and take them to Paris for an exhibition. So the 100 best pieces. "Mona Lisa" doesn't make the cut. This is from the year 1750, so it is a 250-year-old painting. Doesn't make the cut. In 1797, it went to the Louvre, when it was inaugurated as a museum, where it remains today, of course. And around 1850... it's 1849, specifically... We'll call it 1850. The Louvre conservators brought in experts to value each of the paintings, partly for insurance purposes.

And what you see is that the "Mona Lisa" was not even considered Da Vinci's most valuable painting. And his value was actually considered to be a fraction of that of the most famous Raphaels of the time. So what happened between 1850 and 2014 when everyone says that the "Mona Lisa" is the most famous painting in the world? Some of you may have heard this story, but it is wonderful. In the summer of 1911, an Italian painter (apparently patriotic) decides that the "Mona Lisa" should be returned to Italy. So he sneaks into the Louvre on a Sunday night (it was closed on Mondays at the time), slept, woke up, and on Monday morning he walked in, took the painting off its hanger, put it in his jacket, and walked out. the door.

Now, when I asked, for those of you who saw the "Mona Lisa," I said, what was your description, what was your reaction? Most of you said small. And that was a big plus because it's only 22 by 30. So it's a pretty small painting. And that fit nicely inside this guy's coat as he snuck around with the paint. Now this guy... lots of theories about this guy. He was a bit crazy guy. But he basically went back to his apartment, put it in a trunk, slid it under his bed and basically left it there. And no one heard any of it.

Now immediately... by the way, we are at a time when the Parisian press was taking off, four million newspapers in circulation... where our beloved "Mona Lisa" becomes a national story in France and finally a story international. So this great Da Vinci painting is missing. Well, two years later, this guy says, well, job's over and decides to get rid of this "Mona Lisa." He then writes a letter to a Florentine antique dealer. And he says, hey, my name... I have the "Mona Lisa." I want to bring him back to Italy. Will you buy it for me? And the guy says, hey, if it's true, validate it, authenticate it, of course.

So the guy goes to Florence. Sure enough, they meet in a hotel and authenticate the "Mona Lisa." They arrest the guy, apparently surprised, while the police drag him away. And they negotiate with the French government. And then, in a couple of weeks, he returns to France in early 1914 (that is, almost 100 years exactly) to great fanfare. He has his own room and 100,000 people come to see him. So in some way the popularity of this painting begins. And from there, of course, it becomes part of pop culture. On the left, you see the famous Marcel Duchamp parody where he has a small mustache and beard.

And if you know how to read French, he quickly reads those letters, it's a bit of a scandalous phrase about the "Mona Lisa." And on the right, he hits American shores with Nat King Cole's number one song in the 1950s called "Mona Lisa," wins an Academy Award, and so on. By the way, no one really knows how much the "Mona Lisa" is worth. The last time he came to the United States was in the early 1960s. He was brought by Jacqueline Kennedy. It was then subjected to insurance appraisal. So there is something interesting. There is something called "The Financial Analyst Journal" that writes about different financial topics.

And one of the things they had in a recent edition was the art chain, meaning the prices of art over the last 100 years. So, just for fun, I took that 1962 issue and attached it to the art chain. And you have to adjust for inflation and so on. But if you do the math, the best estimate of the value of the "Mona Lisa" in 2014 dollars is $2.5 billion. And that would be about 10 times what you pay for any painting. Now, of course, it's invaluable. Who knows what it's worth and certainly if it would be achieved in the real world.

I have no idea. But just to give you an idea of ​​that exercise for fun. Now the problem with my "Mona Lisa" story is that I hope it's entertaining, but there's no way to prove it's right and there's no way to prove it's wrong. Good? What I did was attach a series of facts to a story, to a statement, that made this painting so famous. But I want to share with you the result of an experiment that lends credibility to how unpredictable punches really are. This is an experiment done by three sociologists at Columbia University called the Music Lab.

The lead researcher on this is a guy named Duncan Watts. And what they did apparently has to do with musical tastes. About 14,000 people participated. So here is the basic exercise. You're a college kid sitting in your dorm room, you get an email, and it says, Hey, we care what you think about music. Enter our site. You see 48 songs by unknown bands. So they validated, they made sure that no one had heard of these songs or these bands. And they say, browse, listen to songs and then rate them. Five stars, I love it. One star, I hate it.

And if you really like it, you can download it for your iPod. That's the basic setup: love it, hate it, download it. Now, unbeknownst to the subjects, they entered: 20% entered what they call the independent condition. The order of the songs is random. Love it, hate it, download it, but you'll get to see what no one else did before you. So you are effectively alone in the record store. The other 80%... of course, that's control. The other 80% went to 10% of each of eight social worlds. You could think of these, literally, as parallel universes. Initial setup identical to the control, but now you could see what other people did before you.

You could see what songs they downloaded and what songs they said they liked. In an extreme version of the experiment, they had a leaderboard: the most popular songs, the most downloaded at the top. So the question is: does the pattern of what people do before you influence what you say you like and what you end up doing? And the answer is quite unequivocally yes. Now I want to be very clear. The best songs in the independent condition had a much better chance of

success

in the social world. And the bad songs actually did well. So quality mattered.

I want to be very clear about this. But if you're in the top third, probably the top half of the independent control condition, pretty much anything can happen. There was one song that I thought brilliantly captured the experiment. It's a song called "Lockdown" by a band called 52metro. And it was number 26 in independent status. So this is one of 48. It's number 26. It's basically the definition of average. Right in the middle. In one of the social worlds, it was the number one hit. And in one of the other social worlds, it was the number 40. So the point is, if we go back in time and replay it, we'd probably have the same thing?

Would "Harry Potter" be "Harry Potter"? Would "Star Wars" be "Star Wars"? Would YouTube be YouTube? The answer is very unlikely that the same things would succeed in the same order of magnitude that they did. It is inherently very difficult to predict the winners. By the way, as a side note, I will mention that I teach a course at Columbia Business School. And this year... we always bring an executive guest. And this year we brought in a guy who was an executive at Time Warner. He now he runs Turner Broadcasting. And he must have told the students half a dozen times during his session: We have no idea what will be a hit.

You know, we obviously worked very hard on this. We try to think of formulas. We really have no idea what is going to work. They have a huge television studio and a huge film studio. They are very incentivized to solve it, but it is very difficult to do so. Now, the other thing I mentioned is this inherent inequality. Economists call this convexity, which says that for a small change in quality, there is a huge change in profitability. The example, the product, I want to use it for this and by the way, you might think, there are many examples.

So by the way, the x-axis here is just going to be skill or quality, whatever you want to say. The y-axis is kind of a reward: it could be market share, it could be profits, whatever. A trivial example is that if you win the US Open tennis tournament, you make twice as much money as the second-place finisher, even though on any objective scale of skill you are sitting next to each other and much better off than the population in general. Now, the product I want to mention in this context is a product called Stephen King. And you all know his name, obviously: a remarkably

success

ful novelist for many, many years.

His first commercially successful novel came out in 1973, called "Carrie." And from that moment on, it was underway. He published about one book a year with great commercial success. But it turns out that Stephen King actually wrote more than one novel a year. So he has these extra novels piled up on the side. Then he goes to his editors and says, hey, we're doing great. What if we publish more than one book a year? And the guy says, Stephen, Stephen. One book per author per year. This is how we do it in the publishing world. So he says, you know what?

Anyway I'm going to publish under a pseudonym. And the pseudonym was Richard Bachman. Richard because there was a Richard Stark novel on his desk at the time, and Bachman because Bachman-Turner Overdrive was playing on the radio. Some of the older people know that band and that song, "You Ain't Seen Nothing Yet." Then Richard Bachman begins to dedicate himself to publishing companies. And this is what will happen over the next six years. Stephen King... great commercial success. Richard Bachman...commercial failure. Stephen King... great commercial success. Richard Bachman...commercial failure. And this happens book after book. Richard Bachman's last book came out in the early 1980s and was called "Thinner." There was a guy in a bookstore in Washington, DC who was reading this book.

And he said, this guy, Richard Bachman, writes a lot like Stephen King. He develops the characters of him like Stephen King. You know, I think this might be Stephen King. He believes this and goes to the Library of Congress and looks up who has the copyright to Richard Bachman. And it turns out... Stephen King. Then he realizes this. And he calls him and says, Mr. King, I realized you're Richard Bachman. By the way, King immediately opens up and says, hey, congratulations. You solved it. I will give you the first interview and so on. Then it was all over.

Now, what is notable and important for the context of the story is that from the moment it was revealed that this book was not written by Richard Bachman, but by Stephen King, sales multiplied by 10. Same book. And by the way, for those of you, this may sound familiar. This actually happened last summer. Is anyone following all this with JK Rowling? JK Rowling wrote a book under the pseudonym Robert Galbraith. In fact, we have Amazon ratings. We followed up on them. It was actually very cool. So keep going. Alright. It's making progress, but it's certainly not a bestseller.

ANDthen this book was actually written by JK Rowling and it shoots to number one. So I imagine, as a publisher, it would be very difficult to retain that information because eventually you can have a hit song, a hit book. Now, the last section I want to end with and then we can have a broader discussion is why we have a hard time understanding luck and ultimately what to do about it. Now if I tell you that the future has skill and luck, everyone here understands it. There is no worry. You completely understand that concept. But once an event occurs, something that happens in all of us (it happens effortlessly and quickly by the way), which is your mind, creates a narrative to explain that outcome and then you archive that narrative in your mind.

And while you do that, two things happen. One is called hindsight bias, and it consists of starting to believe that you knew what was going to happen with a greater probability than you really knew. And the second concept, which is related, is called progressive determinism, which consists of beginning to believe that what happened was the only thing that could have happened. By the way, it is very natural because now you have the result and all the facts surrounding it. And your mind says aha! Yes, it had to be like that. How could I have considered other alternatives?

Now you might say, why do you have this weird image? This guy on the left is Michael Gazzaniga, a well-known neuroscientist. He studied with Roger Sperry and won the Nobel Prize. And he's probably best known for his work with so-called "split-brain patients." Now, these are people who suffer from debilitating epilepsy. They have failed all their treatments. And as a last ditch effort, they go in and cut the corpus callosum, the bundle of nerves between the two hemispheres of the brain. By the way, the first thing I must tell you is that it is actually a very beneficial surgery.

People feel much better after this. But the second thing is that it sets up an incredibly interesting experimental condition in which experimenters can input information into the right hemisphere and then ask the patient what's going on. So the right hemisphere has very little language. So, for example, they could show a door key through the left eye to the right hemisphere. And then they tell the patient to point to the image that makes the most sense. And they point to the door, no problem, it's easy to do. Then they ask them why they point to a door?

Now notice, your left hemisphere has a problem because you can talk, you don't know anything about the key because you can't access the information in the right hemisphere. But you can see that they are pointing to a door. So what does the left hemisphere do... again, it effortlessly and quickly creates a meaningless story to explain what the person is doing. And by the way, if you read this research on split-brain patients, it's absolutely... it's pretty funny because these people basically go out of their way to explain what they're doing. This ability is so pronounced that neuroscientists call it an interpreter.

The interpreter is part of all our brains, it resides in our left hemisphere. If I give you an effect, you will come up with a cause. And by the way, it's like an itch that demands to be scratched. I throw an effect at you, you come up with a cause. Now here is the end of our discussion. The interpreter knows nothing of the fate...he never received the memo. So if he sees a positive result, he assumes that something good has happened. If you see a negative result, you assume something bad has happened. And even see it yourself.

Even check it out yourself. If you are looking at a situation that is probabilistic (and you know in advance the range of possible outcomes), keep in mind that once the outcome has been resolved, you automatically create a story to explain why it happened and somehow rule out all of those situations. . other possibilities. So what I think is so fascinating in this broader discussion is whether the paradox of skill being true (absolute skill is never greater, relative skill is never less) means that luck determines more outcomes. That's colliding with a mind that really struggles to understand the role of luck.

I hope this is something that can be helpful as you reflect on these basic issues. Now what is related to that is our love for stories. There is an interesting book written a couple of years ago by Jonathan Gottschall called "The Storytelling Animal." A quote from this. He says: "A storyteller mind is allergic to uncertainty, randomness and coincidence. It is addicted to meaning. If the storyteller mind cannot find meaningful patterns in the world, it will try to impose them." Now here's the last line that I think is the key. "In short, the storytelling mind is a factory that produces true stories when it can, but makes lies when it can't." And that, I think, is the key concept for us.

If we see results generated by luck, we will try to impose our own stories to try to make sense of them, just as split-brain patients did before. Now you might say, well, is that so important? Let me give you an example from the business world that is fascinating in this regard. Some academics did research on all the books on how to be a great company. "Good to Great", "In Pursuit of Excellence", "Built to Last". You've heard of these. Maybe you have a couple of them on your shelf. In these books there are around 700 companies that are mentioned in these books.

And the question these researchers asked was: How many companies are on these books because they're lucky, and how many are there because they're really clever? So I'll spare you all the details. But what they basically do is take 50 years of data, create a transition matrix for the return on equity, and then simulate thousands of times. Therefore, it allows them to create a template for classifying so-called common cause versus special cause variation. Basically, what does the written system tell me should happen? What is special about the system? By the way, what they discovered when they applied it to these companies is that, in fact, some companies actually demonstrate skill, just like in money management.

Most money management performance can be explained by luck. But there really are some: it takes a differential skill to explain the real results. The same thing. But when they applied this template to the 700 companies mentioned in these books, they found that only 12% of the companies could be reliably coded as skilled. The other 80% thought it was probably there by luck. Now, do you remember that a while ago I mentioned that when there is a lot of luck in an activity, a rapid reversion to the mean is expected? Go grab your copy of "Good to Great" and check out the index for the company mentioned there.

And you'll see that almost from the moment the ink was dry, the performance of many of those companies changed, just as a lot of luck predicted. Here's an example of guys who sell tens of millions of copies of books that claim to give you an

equation

, a set of rules or attributes that will allow you to be successful, when the basis of their research is basically based on luck, for the most part. . . Now let me conclude with two final thoughts. How do we improve? How do we improve? First let me talk about the role of skill.

If you're on the right end of the continuum (the culture and skills side), the answer is deliberate practice. By the way, I don't know if anyone saw the New York Times today... the science section of the New York Times. There was an interesting article by Benedict Carey on Zach Hambrick's work at Michigan State University about how much of true performance is talent and how much is hard work and deliberate practice. And by the way, I wrote about this in my book. I thought the world had leaned too far toward this cartoon version of Malcolm Gladwell (10,000 hours to be great at anything) and too far away from the underlying talent.

So I wrote a little about this. But that is the goal of the article. Hambrick's work shows quite clearly that there is clearly differential talent and that talent plays an important role in success. That said, if it's a primarily skill-based activity, deliberate practice is really a key component to success. So that's 10,000 hours outside of your capacity where you get great feedback. Now here is the key. In this type of activities, the result of that participant is a very clear indicator of his ability. If I want to know if you are a good tennis player or a good pianist, and I know what I am doing, I can listen to you or look at you.

And I can tell if you're good at it. And then I can give you feedback to improve. As you move to the luck side of the continuum, as you can imagine (by the way, investing is a good example of this), that connection between outcome and skill breaks down, or at least partially breaks down. So, for example, you could go to Las Vegas and play blackjack and play your cards foolishly and win for a while or play them smartly and lose. In the long term that will not be true, but it will be true in the short term.

So there is no connection between the quality of your game and your actual results. As a result, the luckier you are, the more you need to focus on the process. And I'm not going to dwell too much on this. But the process, I maintain, should have three essential components, one that I'm going to call analytical, which is finding the edge. And then, once you have an advantage, calculate how much to bet on that advantage. Second, I'll call it behavioral, which is understanding the common biases we all tend to fall into and trying to incorporate methods to manage or mitigate them into your processes.

And the third I'm going to call organizational. This is primarily known as agency costs, where agents and principles may have different interests. But we all work in organizations, none of which are perfect. The question is: does my organization help or hinder the quality of my decisions? So how you improve your skill, I think, to some extent is a function of where you are on that luck/skill continuum and that dictates how you think about it. Now, whenever I tell people that I wrote a book about skill and luck, they say, oh, yeah, luck. I know everything about this.

Yes, luck is where preparation meets opportunity. Or the harder I work, the luckier I get. Yes, I see they said that. Hey, we don't have to go to this guy. I already know the whole story. It is luck that meets preparation. Now, if you accept my definition from the beginning, none of those things are really luck. In other words, you could think about it another way: what is under your control and what is not? If it is under your control, it will be skill to a certain extent. Only if it is out of your control can it be luck.

In some ways you can't improve your luck. All you can do is try to manage your luck. Let me give you two very simple examples. On the left, it would be a simple case where if you are in a competitive interaction, say a sports match for example, and you are the strongest player, what do you want to do if you are the strongest player simplifies the game. And by simplifying the game (the dimensions of the game), that means your skill will almost certainly surpass your competitor's. If you are the underdog or weaker player, what you want to do is complicate the game by adding battlegrounds.

Again, you will still be the weaker player. But it dilutes the strength of the stronger player. So, some examples: Well, the war is clear, right? If you are the weaker army, you don't want to go head to head. You want to use guerrilla tactics. In the business world, you don't want to compete head-to-head with an incumbent. He wants to use disruptive innovation, of the Christensen type. In the world of sports, you don't want to...again, you use tricks, etc., different strategies. So those are the ways to try to tilt the odds a little bit, especially if you're the weaker player, or tilt them in your favor if you're the stronger player.

On the right is this idea of ​​small bets. This is something you probably do a lot as an organization. Many years ago I was an analyst who followed food companies, large packaged food companies, in the United States. And they always lamented that we spent a lot of money on advertising and marketing. And we know that half is wasted, but we don't know which half it is. So we can't stop doing it. And what happened, of course, is this concept of A/B testing. So now we can try two different things and find out. For example, an Internet retailer might say that here are two different websites that people access.

Which one will sell more things? That's why we constantly test and improve. In a sense, you're not actually managing luck. What we are doing is clearing the clouds of uncertainty and focusing on causality much more effectively. But I kind of threw that away as if I was just dismissing luck to some extent. With that I stop. There are three things I hope you learn from this. The first is to define skill and luck as something very important. So those definitions are essential. But in the first section, something in particular that I want you to make clear is the idea of ​​the paradox ofability.

That even when skill absolutely improves, it often in many domains becomes relatively narrower, leaving more to chance. The second thing I want to leave you with is the shape of luck, and especially these path-dependent processes. And in particular, one must be very cautious when trying to predict the outcome of these path-dependent processes. And again, as you know, also based on technology. Many things depend on aspects such as network data, etc. And those are inherently very difficult areas to predict winners. And then the third area is what to do about it. And the main thing I want to leave you with is this idea of ​​the interpreter, that is, we have a part of our brain that is constantly looking for causes for every effect that it sees, and to be very aware and keep that under control. as you consider the results around you.

With that, let me stop, and I will be very happy to entertain. It comes with a white microphone. I will be happy to answer any questions or comments. Do we have a few minutes? Yes, we have a few minutes, right? MALE SPEAKER: Yes, we'll open it up for questions. And thanks for the talk. MICHAEL MAUBOUSSIN: My pleasure. Oh thanks. AUDIENCE: So just looking at the financial community as sort of the average American, we don't see a lot of certainty or predictability. It seems to us that there is a lot of luck. So is there a lack of skill there?

Is the skill not growing? What is happening? MICHAEL MAUBOUSSIN: Very interesting question. I think the answer is the notion that everything is a huge amount of luck - if I put it back on the continuum, I would put investing on the luck side. But again, I don't think it's accurate to say it's luck. So back to the paradox of ability. I think the way to say this is that most professional institutional investors are very skilled. They have gone to great schools. They have incredible information, computing power, access to information, etc. But the problem is that everyone does the same thing.

So his ability is extremely uniform. So I think it's a classic example of the paradox of ability. If I took you back to the 1960s, with the technology you have access to as a financial money manager, you would run circles around everyone. But now everyone has the same thing. So I think it leaves a lot more to chance. So it seems to be luck, I think, for the most part. That's all. Now I will mention one thing as a measure of it. I showed them the marathon runners and how the difference between first and twentieth has been decreasing over time.

In fact, we did something very similar for money managers. So what we do is we take the standard, which is basically the standard deviation of the excess returns, and that's what the bell-shaped distribution of returns looks like. And what's happened over the last 50 years is that it's gotten thinner. So the difference between the best and the average is smaller today than it was a generation or two ago. Again, it's very random, but it's not because there isn't a lot of skill. In fact, it is the opposite. The skill is very high but very uniform, that's the way to think about it.

And by the way, that also applies to athletics: sports. You could say that even the World Cup... these matches are... you can see that the odds are not that high one way or the other. This would certainly not be a reflection of the players' skill, which is extraordinary. And certainly, if we put any of those teams on the court against teams from 20 years ago, they would be surrounded. The skills are equal. And as a consequence, luck determines the results. Big question. A very important one. AUDIENCE: You mentioned the relationship between age and cognitive ability. And you said you picked around 40, 50 or something like that.

Warren Buffett is over 80 years old. Do you think he's not as good as he was? This is the first question. That's a fun question. The second: As you said, it's getting harder and harder to know who is a good value investor. But if you had to find one, what would be the way to do it? MICHAEL MAUBOUSSIN: Impressive. Two excellent questions. Warren Buffett is, I think... look, I think he's been pretty extraordinary in all the ages that he's had. So that's one thing. I think he's doing less traditional money management, like we would say managing a stock portfolio.

Obviously, he's now allocating a lot more capital to Berkshire Hathaway. So I think the game has changed a little bit. But I suspect, I hate to say this, but I suspect that he might have a hard time competing with a 40-year-old version of himself, I guess. AUDIENCE: He also has a lot more money. MICHAEL MAUBOUSSIN: And he has a lot more money to deploy. Yes it's correct. That also impedes his performance. The second part... your second question was? AUDIENCE: How to find... MICHAEL MAUBOUSSIN: Oh, yeah. Value Investors. Look, I think I'll... I'll mention a couple of things about this.

First of all, I think the characteristic of many great value investors... let me put it this way. There's a quote I love from a guy named Seth Klarman from Baupost, who is one of the great value investors. And he says value investing is, in essence, the marriage of a contrarian streak and a calculator. So what does he mean? I think the contrarian streak is the first element, which is the ability to go against what everyone else is doing. So Buffett has this great line. He is afraid when others are greedy and greedy when others are afraid.

Let me say this: investing is inherently a very social exercise. Very social. And it takes a very unusual person who is willing to do the opposite of what everyone else does. So that's the first element. The problem is that being contrary for the sake of being contrary is not a very good idea because sometimes the consensus is correct. In other words, if the movie theater is on fire, run out the door. No... So the calculator is the second component, which says that as a result of everyone adopting a view, a gap opens up between price and value.

And that becomes an opportunity to invest. So I think great value investors are people with certain characteristics – personality characteristics. In part, they are people who usually don't care what others think about what they think. And that is very rare in the normal population. Most people are very sensitive to what others think of them. And then often the organization... this question about Buffett is a great question. Berkshire Hathaway is configured as an organization very conducive to quality decision making. It is not influenced by many other pressures. Day-to-day business pressures, for example. So those would be things I would look for.

The main thing is this ability to operate independently of what others think. AUDIENCE: Many people get the same thing or the same thing and get a similar view. Instead of a lucky person. Or if we look from the outside and see that this is a collective vision. So will it really be collective opinion that will determine this and not luck? MICHAEL MAUBOUSSIN: Yes, exactly. Super interesting question. So let me make sure I'm on the same page as you. But I would just say that we talk about this a lot in the context of market efficiency. And it even relates to this comment about being a contrarian.

Well, we can use more formal language like complex systems. But let's use simpler language like "The Wisdom of Crowds." So when are crowds wise and when are they crazy? Crowds tend to be when three conditions are met. And Surowiecki wrote about this in his book. One is the diversity of the underlying agents. That is why we need diversity of points of view. The second is a properly functioning aggregation mechanism. So everyone has information but I can bring it together in one place. And the third is incentives, which are basically rewards for being right and sanctions for being wrong.

So when those three things are in place, I can show that I actually get very efficient economic results similar to what the textbooks predict. But what happens is that when one or more of those conditions are violated, these inefficiencies appear. And I think that's why I mentioned those contrary streaks. So when we all believe the same thing, we lose diversity and the crowd turns into crowd madness. You can read the annals. I mean, there are famous... South Sea Bubble and Tulip Mania, I mean, there are famous... Internet now, maybe housing. I mean there are some... this doesn't happen a lot.

But it happens and it can be very epic in proportion. So what I would say is: can we look for these so-called diversity breakdowns? And for some reason, everyone is choosing one side of the business over the other. It could be mainly psychological factors. But they could also be technical factors. I have a portfolio that has leverage. And now I'm getting a margin call. So I have to sell things because I have to. So it's not because I want to, but I have to. So those things can also contribute. So that's an excellent question. It is a very rich question.

But that is the idea. So you say, well, how do skill and luck fit into that? I'm not so sure. That's where big investors take advantage. And that's where the contrarian streak is and the calculators that take advantage of these diversity breakdowns in practice. AUDIENCE: My name is Bruno. And my question is: I think it's safe to say that most of us in this room try to be outliers, we try to be the best. And in that sense, it seems to me that the bottom line is that you should try to be in the group of people who are the best.

But as you said, that group is getting bigger and the standard deviation is getting smaller. And after that I just have to hope for luck. And if that's the case, how do you deal with this feeling of helplessness that I did my best, but it's still not in my control? MICHAEL MAUBOUSSIN: I mean, I don't know if you'd say he's impotent. I think in some ways he's liberating. Do the best you can. So I would say it this way. I would go back and say that if you look at the most successful people in the world (however you want to measure it), I think almost without exception you will find that they were incredibly lucky at some point.

And by the way, in chapter one of the book, I start with a story of... you all know this. It is a famous story in technology by Bill Gates and Gary Kildall. Do you know the story of Gary Kildall? Does everyone know this? You don't know Gary's story. You already know that, back, right? So if I'm wrong, fix this for me. IBM launches a PC in 1980; started the project in 1980, 1981. And they need an operating system. It turns out that the president of IBM and Bill Gates' mother are on the same United Way board of directors. So that's a first start.

She says, oh, my little Bill. He knows something about those computers. You should go see it. IBM teams fly to Microsoft in Seattle to see Bill Gates. By the way, they were building cards for the Apple II. It had nothing to do with operating systems. And Bill Gates... they say, do you have an operating system? We are building this PC. Everything is secret. Sign all these papers. And Bill Gates says: no, I don't do that. This guy, Gary Kildall, in California, is the one who does this. Gary Kildall's company had an 80% market share in software for Intel chips. 80% market share in 1980.

Dominant. And he is considered the best programmer of the 1970s. So they take Gary down. In this part of the story, no one really knows what happened. But he basically somehow fired IBM. As if he didn't show up or take them seriously. They sort of came to an agreement, but they didn't really. Anyway, the guys at IBM were very unhappy. And he refused to sign all of his papers. Then they go back to Microsoft. And they say, Bill, this guy didn't really help us. And Bill says, I'll hook you up. So he goes across town and basically buys an imitation of Kildall's product.

Imitation for $50,000. And he called it MS DOS and said: I'm going to sell it to you, IBM. But I'm not going to sell it... I'm going to give you the license. That was genius. And from there... right now. So the PC is now preparing to boot. When you bought PCs back in 1981, you actually bought the IBM machine and then bought the operating system separately. It wasn't loaded. Turns out, as Kildall finds out, little Bill Gates is going to sell some operating system. He then calls IBM and says: I thought we had a deal. They say, yeah, yeah, okay, we have a deal.

But it turns out that IBM retained the rights to set product prices. So when the first PC went on sale, MS DOS cost $60. And Gary Kildall's product was $240. So which one do you think they bought? And that's the end of the story. That's how Gary Kildall ended... and the story is an amazing story. By the way, Gary Kildall was from Seattle. And it turns out that the story ends in a bar, a biker bar, in Monterey, California. Nobody really knows what happened. But he got drunk, got into a fight or simply fell, hit his head on the bar and died at the age of 53.

And so it's very... you could say, could I have twisted that story a little bit and Gary, would Kildall be Bill Gates? Bill Gates would still be very successful, I amsure. But he wouldn't be Bill Gates, would he? So those are the examples. That's why I say, is that liberating or not? I don't know if that is it or not. But what I want to say is that you must do everything you can to succeed. But there is no one who is an outlier who is unlucky. It almost, by definition, cannot be. If there's luck in what you're doing, it's almost... now probably not so much in things like athletics or music or whatever, or if there's some quantifiable way to measure people's performance.

Less true. But when these social processes are activated. You know, JK Rowling. You know this story. Harry Potter was rejected by nine or ten publishers before someone reluctantly wanted to publish it. They are the best-selling novels of all time, right? In fact, I feel liberated by it. I think it's the other way around. I feel the opposite, if I do, and this is what I tell my kids all the time. If you work as hard as you can, the result doesn't really matter. You have done everything you can do. The destinations. The destinies of the gods.

AUDIENCE: My name is Timmy and he had a quick question. We talked about business and sports, but I just want to talk a little bit about leadership. And as someone who is in a high position at a company like Credit Suisse, I'm sure that when they recruit talent and things like that, they're looking at people who maybe have had success in leadership, maybe through their own skills. But are there moments, when you talk about the performer, where we always want to say it's skill, where you're seeing something that maybe isn't pure skill and maybe it's luck?

And how do you go about hiring them or thinking about whether they're going to... MICHAEL MAUBOUSSIN: Well, I don't think you want to hire people who have been lucky, but I think it's actually a great business. This is a really difficult question. And I have some ideas about leadership that are a little separate. But let me mention a couple of things about how to be careful with this. There's a guy at Harvard Business named Boris Groysberg who wrote a book called "Chasing Stars." And what he really studied was the idea of ​​recruiting stars from other organizations to join his.

And as it turns out, this turns out to be very, very bad practice. Most people don't; Their skills do not translate from one organization to another very effectively. It's less true in athletics, but it's even true in athletics. It's certainly true in the business world. So, for example, they followed 22 GE executives, who are obviously trained to be the best in leadership. And they found that when they went to other organizations that were very similar to GE, they tended to get good results. But if they went to other organizations that were not the same as GE, they tended to fail a lot.

We see this a lot in the business world. It's difficult to me. What is leadership? To me, the kind of things that are important in a leader is someone who... and there are different ways to lead. But I think a lot of it is quite intellectual. I think for me it's about thinking things strategically. It's about being based on facts, data, and setting a tone or direction for an organization that people are excited to follow. So that's the kind of thing. But is very difficult. I think it's very difficult to fix it. And the last thing I'll say is... and it goes back to the interpreter, who drew correctly...

By the way, there is a great book that I would recommend to everyone called "The Halo Effect" written by a guy. named Phil Rosenzweig. It is a very short book: 175 pages. But an incredibly important set of lessons. And what he's saying is basically, when things are going well, the CEO walks on water. And when things go wrong, the CEO himself, by the way, may have no idea what he's doing. I think we make up stories to fit the narrative or the facts, which are not always well placed. AUDIENCE: I found it very interesting when you mentioned that the talent gap between the person in first place and the person in 20th place is narrowing.

And in my head I was thinking, what about the pay gap between the two? If we compare the best and the 20th 50 years ago with now, now the gap is huge, although the talent gap has narrowed. It's a kind of paradox. And it did seem frustrating. MICHAEL MAUBOUSSIN: I've actually thought about writing about this. I think it's absolutely fascinating. And I think that's very true, by the way. So I think there's a huge, huge reward, an ever-increasing reward, for cognitive surplus. And I think technology has greatly accelerated that. So in the book I mention this. There is a very famous article written over 30 years ago by Sherwin Rosen called "The Economics of Superstars." And what he argued in that article was that more and more people who are just a little bit better than others are getting disproportionate rewards.

And it's more like a tennis tournament. You will get double the money. You all know these facts, right? If you look at people with very advanced degrees (master's degrees vs. college degrees vs. people without degrees), the benefits. But even within those subsectors there are still very, very substantial wage differences. I think there will just be... and by the way, I don't know how we can stop this. These books have influenced me a lot lately. "Average is Over" and "The Second Machine Age" by a couple of (I think they came here, actually) economists from MIT. And those stories are... there are positive aspects of technology helping everyone's life.

I think in some ways it's really cool. Therefore, we all have access to education and entertainment that we may not have had profitably in times past. But it seems that the benefit for cognitive abilities is actually skewed. I don't know what stops or how it stops. Well, with that, thank you very much for your time and attention. And have a good day. I appreciate it.