#521： The 5 Universal Laws of Success
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Summary
In this episode of the Art of Manliness podcast, we talk with Albert Laszlo Barbashi, a professor of network science at the Center for Complex Network Research at the University of Chicago, about his new book, The Formula: The Universal Laws of Success, and how network science helped him uncover the hidden connections that lead to success.
Transcript
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Brett McKay here and welcome to another edition of the Art of Manliness podcast.
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So ever since we were little kiddos, we've been told that talent and hard work pays off.
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But as we've gone to adulthood, we've all seen instances where people who are equally
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or even less talented than we are or even less hardworking than we are still got the
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raise, the buzz, the promotion or the recognition that we so keenly wanted for ourselves can
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Well, my guest today says that instead of getting jaded, you need to understand that
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hard work and talent, while necessary, aren't sufficient for success.
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His name is Albert Laszlo Barbashi, and he's a professor of network science and the author
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of the book, The Formula, Universal Laws of Success.
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We dig on our conversation discussing how Laszlo's work in network science helped him uncover the
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Laszlo then explains the difference between performance and success and how it's possible
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We then dig into the five universal laws that Laszlo and his researchers have found that
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cut across achievement of success in every field, along with practical takeaways, you
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can start implementing in your life to experience more success yourself.
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After the show's over, check out our show notes at aom.is slash formula.
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So you just recently published, not too long ago, a book called The Formula, The Universal
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Laws of Success, The Science Behind Why People Succeed or Fail.
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Now, the story of how this book came to be is really interesting because for a living, what
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you've spent most of your career doing is studying complex networks.
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In fact, you run the Center for Complex Network Research.
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For those who aren't familiar with that, what exactly do you do there?
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So I'm a network scientist, officially, professor of network science, and we study all kinds
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And the reason we do so is because virtually all our social and biological existence depends
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You know, are we embedded in the social network and professional network?
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All professional opportunities depend on access to the right network.
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But even with our very biological existence depends on, let's say, chemical, biochemical
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network within ourselves and genetic network within ourselves.
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And our consciousness depends really on the wiring of our brain.
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So we don't think often too much about it, but really the fact that we are alive and can
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exist and do what we do is all depends on myriads of networks.
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And network science aims to study and understand these type of networks.
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So we study at the same time the biological networks, like genetic networks, but also the internet,
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social networks, and eventually, in the last few years, networks that determine your success.
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How did you go from looking at, say, a biological network, say, in our brain or within our genes,
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to studying how successful people become successful?
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One of them was really by an accident, which is I had a fabulous student who is now a professor
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But at that time, he was just coming off a project about disasters, that is, to try to
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understand how people change their behavior when they experience some kind of disaster
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And we use mobile phones to track human behavior and try to understand whether we could detect
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something odd happening in your neighborhood, just the way you behave and use your phone.
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And it was a fabulous project, and we wrote a great paper about it, yet journal after journal
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So one day, one of the students who was on the project, Das Jun Wang, came to me and said,
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And he said, whatever, but not another disaster.
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And I kind of said, OK, well, how about success?
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And we kind of laughed about it, but then we looked at each other and said, hmm, this
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Because we're network scientists, and we have spent quite a bit of time by then over a decade
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Well, we hardly ever asked the question how you, as a node, is experiencing the network
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that you are part of, and whether the network will actually help you succeed in certain areas
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So kind of that night, from this kind of random direction or discussion, came a new subject of
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How do we quantify success, what's the role in networks, and how do we really describe
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performance and success in the language of science?
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So yeah, basically, it's like, the question was like, tell me why, figure out why our paper
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OK, well, so let's talk about sort of typical, before we get into what you guys uncovered with
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your research, let's talk about how people in general think about success.
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Like, when you ask people, just on the street, or maybe a colleague, like, how do you become
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What are some of the most common answers or assumptions that we have about that?
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I'm so glad you asked that, because I was very surprised that when I went around and asked
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people really that question, I realized that most people are really shy to talk about the
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measures of success that the society considers success, from money to citations to visibility.
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But they talk about their personal successes, like their pride in their children, the satisfaction
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of achieving something in their life, of being where they are, and so on.
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And we were at that time curious about how you quantify success, because anything we do
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has to be quantified and measurable, and so on.
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And for us, it was a big dilemma, how do we describe that?
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And for us, actually, we realized that we have to make a very big distinction between performance,
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which is what you do, and how you feel about what you do, and so on, and success.
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And this is interesting that we have to distinguish that, because in the common language, they're
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And we do so because we learn early on in school that performance leads to success, hence
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if you are successful, you must have performance.
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If you have performance, you will be successful.
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But from a data perspective, we realize that these are very different quantities, because
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performance is something that you do, how fast you run, what kind of research papers
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you write, what kind of deals you put together as a businessman, what kind of paintings you
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Success, however, is mostly about what does the community see from that performance, and
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whether they acknowledge it or not, and whether they reward you, and how they reward you for
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In other words, your performance is about you, but your success is really about us, about
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the community that acknowledges and rewards you for that performance.
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Which, from a data perspective, was very interesting, because probably as we go on, we will realize
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in this discussion that performance often is hard to measure, but success is easily measurable
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because it reflects the community's opinion about you, hence there are multiple data points
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So, you can measure success in ways like, okay, number of citations a journal article
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gets, for example, a number of books a book sells, the number of, I guess, time, I guess
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nowadays, not albums sold, but downloads of a song.
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Yes, and it's important to understand that there is not a single measure of success.
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But depending on what you do, there are different measures of success in the community.
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As you said, for a scientist, that's impact, that is often measured in terms of citations.
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For an author, it's audience, how many people listen to them?
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For a politician, it may actually be fame, you know, because that kind of translates into
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So, for each area, one has to find the right performance measure and the right success measure.
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But one of the things I discuss in the formula is that despite the fact that there are multiple
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measures of performance and multiple measures of success, fundamentally, the laws that describe
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the relationship between performance and success are rather universal and apply to all different
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Well, you give a great example in the book, just sort of showing the difference between
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Now, people are in America, they've probably had Red Baron pizza.
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The Red Baron was this famous ace pilot during World War I.
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Snoopy made him famous in the Charlie Brown comic.
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But you also, so there's someone who was successful because he performed well, but also people knew
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But you also highlight, there's also another World War I ace that had pretty much the same
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performance level as the Red Baron, but no one knows about him.
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And that really kind of shows to me how kind of different success could be.
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So indeed, the Red Baron, or Juan Ricofer, what was his name in the First World War, was
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a very famous ace pilot who has had really every measure of success one can imagine.
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And he is very well known to us because he holds officially the record number of planes shut
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And because of that, movies were written about him, books have been written about documentaries
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And he was a person who was not shy to hide his success.
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It's called Red Baron because at a certain moment, he went against of the principle that
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we have today in aviation to build planes that are invisible.
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So everyone knows that it's him and it's coming.
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So what is interesting when you look at the data is that while he was on the German side,
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His name is Rene Falk, who was just as good at fights, actually, as himself.
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Not only that, that he himself counts that about 120 planes that he shut down, which is much
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higher than Rondi Koffer, about 70 have been confirmed and most likely he has actually shut
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But most importantly, he himself has never been shut down and never even be scratched by
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a bullet, while the Red Baron has been shut down three times during his career.
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And third time, he even lost his life in the battle.
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So yet, all the movies are about the Red Baron and you hardly hear about Rene Falk.
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This is one of the reasons I wrote the formula is for people and myself to understand why is
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it that with virtually indistinguishable performance, some succeed and some are just
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And as I was reading that chapter, it made me think of there's some artists, writers
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in particular, who their performance level was phenomenal at the time when they were alive.
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Made me think of Herman Melville with Moby Dick.
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Same with The Great Gatsby by Scott Fitzgerald.
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We consider them like, you know, masterpieces, great American novels.
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But it was, that didn't happen until after they died.
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Like, they didn't become a success the way you define it until after they died, even though
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they masterfully wrote it when they were alive.
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And I probably didn't devote enough time in the formula.
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Is this, the idea of posthumous success, right?
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That people would recognize what I do when I, how great is what I do after I die.
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And despite of all the examples that you mentioned, and I could add more, like Van Gogh
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and Nietzsche, the data is pretty clear about it.
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It's extremely rare that someone is recognized after their death.
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When you go back in the encyclopedia and you look at the people whom we admire and remember
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There's a so-called genius literature who focuses on that, mostly rooted in psychology.
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What they realized is that 99% of the people whom we consider important for us today from
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the past were very, very important to their contemporaries.
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And they have gotten all the recognition that was possible at that moment of their career.
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So, and so, you know, if you think from Michelangelo to, you know, Leonardo, from Beethoven to
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Bach and others, they were revered in their times.
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And there are very, very few, less than 1% of the individuals who were recognized after
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But those 1% present such a powerful storyline for us that we end up writing most of our
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And therefore, they occupy a bigger space in our brain than those who really did not follow
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So, if I look at the data, my recommendation to you and to your audience, if you want to
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be successful, don't count on the next generation to recognize that.
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Make sure that you follow the patterns that I described in the formula and get your recognition
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So, perception by the community is one of the necessary factors in order to be success.
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That could lead people to the conclusion, well, if I just, if I'm just famous, if I'm known
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Well, there are certain forms where fame is the goal.
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And, you know, pretty much the celeb culture is really in that particular category.
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And I know often people ditch the celeb culture to say, oh, all they do is they want to be famous
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The truth is that those people work very, very hard to continue staying in the kind of
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in the tension of the community or the world at large.
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And perhaps the reason why we think less of them is because we don't perceive that they're
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In most other areas, people who are famous, from Einstein to, let's say, Lady Gaga, they
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became famous through some activity that they have done, some professional activity that
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we as a community or as a society really appreciate.
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So, there's this dictonomy, right, between that.
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So, you can become famous for the sake of famousness or as a result of something good that you've
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And as a value system, we obviously appreciate better of those who have just done their job
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and then we recognize them and made them famous.
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Well, that leads nicely to the first law of success you lay out.
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The first law really kind of addresses the relationship between performance and success.
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But when performance can't be measured, networks drive success.
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And there's lots of information packed into that.
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Because on one hand, it actually acknowledges the fact that in areas where performance is
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Unfortunately, there are very few areas where performance is accurately measurable.
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And what we have shown in my research lab and I discuss in the formula is the fact that
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when really you can measure performance, like how fast you can run or whether you're winning
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or losing your tennis games, then all measurable success quantities are purely derived from that
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But the problem comes is that most people in this society live, work and live in areas where
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performance is not as easily measurable as it is the case for a runner.
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Whether you are teaching at the school or university or whether you're putting together deals for
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a business or whether you have painting, you are in areas where performance is very,
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So then the question is, when performance is not measurable, what determines success?
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And as I discussed in the first law, networks do.
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I mean, what can people do to, I don't know, guide their life or their career decisions knowing this law?
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So perhaps let's kind of pull out a little bit the network piece, right?
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And one of the areas where performance is not measurable at all.
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And as I discuss in the book, one area that is clear in that case is art.
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Well, in front of me, it's purely a microphone.
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If you would see the same microphone on the pedestal exhibited in MoMA under a wide,
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So art is modern art or contemporary art is one of those areas where you cannot just look
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at the object from in isolation from the art world and decide what is it worth.
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Its worth is determined by who was the artist who put it out there?
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And what institutions were engaged with that artist?
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And we have taken this to such an extreme that we mapped out the art world in the last four
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And we were able to show that we can map out the invisible network that determines the success
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And that network is extremely predictable, has extreme predictive power.
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If you give me your favorite artist in the last five exhibits, I can fast forward his
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or her career 20 years into the future and tell you whether he or she will make it or not.
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Because art is one of those spaces where performance is impossible to measure and it's only the
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You have to engage with the network that determines the value in the art.
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In the case of the art, those are the institutions, galleries, curators, and so on.
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So coming back to the original question, what does that mean?
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The first question I would ask, sit down and think to yourself, are you in a career path
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where you have an objectively measurable performance?
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And in that case, indeed, the key, the path to success is to improve your performance, run
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If, however, you are in an area where performance is not accurately measurable or not measurable
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at all, then beyond a certain point, improving performance does not give you more results.
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You need to start paying attention to those influence and power networks that determine success.
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So does that mean you have to work on building up your network?
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Yes, but it's not as simple as simply mindless networking.
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And myself as a network scientist, it's kind of odd to say that's not what you should be
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What you need to do is to understand what is that network that determines success in your
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Like in the case of the art world, it's not the network between the artists.
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The network that really matters is the institutions, the curators, small curators, as well as the
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So just hanging around with lots of artists is not the path to success in the art world.
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Kind of understanding these forces that determine how artists and artwork moves within the institutions
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And all areas have their own respective network.
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We're in the process, for example, to start a project to map out the networks and the forces
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And we already see the multiple networks that are important there from actually getting access
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to the resources all the way to kind of getting funded, the people that you bring in your
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So I'm giving this example and the art example to people understand that really there is no
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And depending on what you do, maybe a completely different network that is responsible.
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The first step of the process, understand, map it mentally out, and then try to think,
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what do you need to do to position yourself well within that network?
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Well, let's go to your world, the world of academia.
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Like what would be the network that you need to develop to say, get that paper published
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Actually, academia is somewhere between art and sciences because performance doesn't matter.
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And why it doesn't matter is that if you and I actually write down the formula for,
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let's say, predicting the success of tennis players, then the formula can be tested on
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the data and the community can decide whether your formula or my formula is better.
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And, you know, if yours is better, then you will actually carry the success and my formula
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But networks are still important because not everything is worthwhile or it's possible to
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There are so-called disciplines and within disciplines, there are kind of, you know, breaking
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areas and in a little bit, there's a community decision of what are the areas that really
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And you could get fabulous results in areas that no one really cares.
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And therefore, really, you will not have an impact.
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So in science, performance and networks together matter.
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The networks determine what is worthwhile to explore.
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And then within that area, there's a clear performance measure, whether your theory or
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your formula or your prediction is better than mine.
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So to recap the first law, the first law is if an activity can be measured, performance
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But if it can't be measured, then the network is going to matter more.
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The second law really talks about the fact that performance and success are very, very
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Performance is bounded, but success is unbounded.
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The runners are determined really, their performance is determined by their speed.
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And we have a chronometer and we can measure it.
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And of course, we know that the fastest man on earth is Usain Bolt.
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What is interesting about him when I look at his performance is that when he wins a race,
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he doesn't really win by outrunning significantly his competition.
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He runs at most 1% faster than the loser of that particular competition.
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And particularly when I look at his speed, he's not running 10 times faster than I do.
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So when we measure performance like speed of running or any other really objectively measurable
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human performance, what we realize is that there are not a huge variability between the
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That is, the best is not really much, much better than the second best, but only slightly
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This is what we call that performance is bounded.
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And one of the consequences is that no matter how good you are in terms of performance, you
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will never be much better than your competition.
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And there will be others who are so closely similar to you in performance that is almost
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Now, put to that the other piece, the fact that in many areas, performance is not possible
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So now, if performance is bounded, and you can't even measure performance in an objective
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way, it means that no matter what you do, you can count on that there are several people
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who are indistinguishably good as you are at your job.
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Now, this is not to say that we cannot distinguish good from bad.
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But what is difficult is to do is to distinguish the good singer from the good singer, the good
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And that's a humbling result because it really tells me that it doesn't matter what I do.
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I cannot really be the absolute best in a measurable way at what I do.
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I have to coexist with many others who are comparable to me.
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It means that when we look at the success measures, how much money, the number one error
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versus number two, how many citations the best scientist earns compared to the second
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one, and so on, the differences are not tiny, but it can be orders of magnitude.
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And indeed, this is kind of well known that the income distribution is not uniform, and
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the top people actually are not just earning 1% more than the second one, but often a factor
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That is very hard to distinguish those at the top.
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That is, the number one are not just slightly better rewarded, but often orders of magnitude
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Where you're talking about success is unbounded.
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So mathematically, it means that every time that we measure performance, it follows a
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bounded distribution, like a Gaussian or exponential distribution.
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But every time we measure performance, whether it's citations, non-downloads of songs, or
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money aired, it follows a power law distribution.
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In the economics literature, this is often called the Pareto's law from the 19th century
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economists in Italy who realized the so-called 80-20% rule that 20% of the individuals earned
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That is true even today, except it became more extreme, particularly in the US.
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You know, like 80% of the money in the US is probably earned by the top 2-3% of the population.
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We're going to take a quick break for your word from our sponsors.
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Because it's going to be kind of depressing, right?
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Because like, well, I'm just as good as that guy who's getting all the book sales and money.
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Like, what do I need to do to compete with that guy?
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Or can you even compete with that superstar who's at the top end of the power law?
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And the key actually is to really understand that beyond a certain point, the competition
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is not based on performance because those performance differences are not visible.
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And then you need to pay attention to other effects, namely to the third law.
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Preview success times fitness equals future success.
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So let's again and take it apart and what it means.
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Preview, the law starts with simply saying success drives success.
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That is, the more you have, the more you will get proportionate to what you already have.
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I have discovered or encountered that this the first time about 20 years ago when we're
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And we try to understand why is it that certain web pages have millions of links, while the
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vast majority of the web pages have a few dozen at most.
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So what's the mechanism by which a certain web page like Google or Yahoo running away with
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And we realize that mathematically to describe that, you have to assume that success leads
00:29:13.140
That is, the more links you have on the world wide web web page, the more you will get tomorrow.
00:29:17.980
The more friends you have, the more friends you will make tomorrow and so on.
00:29:25.080
It's called in the scientific literature as preferential attachment, saying that effectively,
00:29:31.840
if you have more, you are preferentially chosen by the new who kind of tried to vote and again
00:29:37.300
But of course, if only rich gets richer, which is what preferential attachment says, the question
00:29:46.300
So what's the mechanism by which coming from behind, you could actually become that hub or
00:29:57.120
The fitness is really telling us that nodes have different abilities or individuals have
00:30:05.700
And once again, we discovered first in the case of the world wide web, trying to understand
00:30:10.740
how can a latecomer web page turn into the most connected page, like Facebook was a relatively
00:30:16.380
latecomer on the world wide web, yet within a few years after its appearance, it became
00:30:22.080
the single biggest hub of the world wide web, overcoming even Google.
00:30:27.940
And we realized that there is another concept, which is the fitness.
00:30:31.800
And once again, fitness is a collective measure that the community assigns to a particular
00:30:37.180
node or individual, and effectively tells you how much, how interesting you are for us.
00:30:43.400
Fitness is an individual tells you, if I meet you, do I want to keep your phone number?
00:30:49.500
Fitness is a web page tells me, if I go to your web page, do I want to save that link to
00:30:55.740
And in all areas, there is a measure of fitness, and effectively describes the community's perception
00:31:03.360
of how useful that individual, that product, that web page is.
00:31:08.820
And the reason why fitness is important is because the reach gets richer phenomena is really
00:31:16.960
That is, visibility means that I can easily find you.
00:31:20.620
Well, once I find you, I make a decision if I want to know you and connect to you, and that's
00:31:26.700
Hence, a low fitness, big note could actually lose or throw slower and lose its edge.
00:31:32.880
But if a high fitness note comes into the world wide web, it very fastly can acquire new links
00:31:40.480
and can overcome the earlier web pages, like Facebook has overcome Google.
00:31:46.060
So at the end, what we learn is that really, your success is determined by your previous success,
00:31:51.320
which is your visibility, how easy it is for me to find you, times your fitness is telling
00:31:56.860
me, once I find you, what is the likelihood that I will actually connect to you or work
00:32:04.720
I like that distinction between fitness and visibility because that visibility factor can
00:32:10.500
be manipulated in unethical ways where you get lots of visibility really fast, right?
00:32:16.400
So there's examples of authors who will buy their books in bulk, right?
00:32:22.820
But what you're saying, okay, that might give you visibility in the short term, but once
00:32:27.020
people start reading it, they actually find out, well, it's not a very good book, you're
00:32:32.740
And I'm glad you raised that example because we, in my lab, actually analyzed the book success.
00:32:37.960
We have purchased sales data from BookScan and we looked at decades long of what makes
00:32:45.740
And we actually do see books that are pushed up on the New York Times bestseller list.
00:32:51.240
That is when they appear there, they appear on the bestseller list and generally they sell
00:32:59.780
This is a traditional case of the situation that you described.
00:33:02.700
Very strong marketing and often purchase, massive purchases to kind of create the numbers to make
00:33:10.000
But then when people actually get that book, they realize, no, I don't really want to read
00:33:15.580
that and they would not recommend it to anyone else, which actually is an interesting question
00:33:22.520
Does the New York Times bestseller list or appearing there will help you sell books?
00:33:31.200
I discussed that in the formula that the answer is no.
00:33:34.860
Appearing on the New York Times bestseller list will not actually boost your sales.
00:33:42.340
For a vast majority of the books, at least it will not.
00:33:45.340
The only time it will do so, if you are a new time author and you've never been in a bestseller
00:33:53.560
And then in that case, it acts kind of like a marketing tool that people will find about
00:33:58.880
your book about and then therefore they may by chance buy it.
00:34:02.720
In most cases, the New York Times bestseller list is not selling books.
00:34:06.520
It's simply reflecting the community's interest in your book.
00:34:14.180
What I've always found fascinating is the study and the research on the wisdom of crowds.
00:34:20.220
Well, the crowd, if you get them together, are going to come up with the best.
00:34:24.900
But there's a study you talk about in the book where, I think it was at Stanford, where
00:34:30.720
In one case, people, they couldn't see what other people were downloading.
00:34:35.240
And in that case, people typically rated the songs the same in quality.
00:34:39.120
But then once people could see what other people were downloading, what ended up being the most
00:34:43.760
popular downloaded song changed based on the particular group that they were in.
00:34:49.680
So there's a social network effect going on there.
00:34:54.780
And this is very interesting because cultural markets can be very volatile.
00:35:01.980
And we often attribute to the variable quality.
00:35:04.680
But what this study has shown that was done by Matt Sagalnik and Duncan Watts at that time,
00:35:10.760
they were working at Yahoo, is that really the volatility is often not in the quality of the song,
00:35:21.220
And effectively, when people were not shown the ranking of the song,
00:35:26.800
and they were asked to rank songs just simply pure based on the performance,
00:35:31.000
they would come up with a relatively stable ranking that reflects the community's perception,
00:35:36.880
But indeed, in the moment, they were actually shown of how many other people have liked that song.
00:35:46.960
So they had eight different parallel experiments that different groups of people ended up in the different rooms.
00:35:55.320
And the outcome of the eight experiments was drastically different.
00:35:59.120
And there was no agreement between the eight groups of which one is the best song.
00:36:04.320
What is interesting about it is that we live in a society
00:36:07.140
where we rely on other people's opinion in many of our decisions.
00:36:11.260
We go at Amazon and see how many people liked a particular product,
00:36:14.880
how many comments it has, and whether we try to go to a hotel,
00:36:19.480
we actually look at how many people liked that hotel and what kind of comments they gave.
00:36:23.880
And therefore, we are relying on the crowd to shortcut our decision process.
00:36:29.540
What this study shows is that the crowd decision is really not selecting quality
00:36:36.160
because there's a huge degree of volatility in the randomness of what the crowd sees first
00:36:44.880
So, but what is also interesting about it is that if you are actually following the crowd's election process,
00:36:52.680
my colleagues were able to come up with formulas that can infer the true value of each of those objects,
00:37:03.980
So that, and tell us which one is truly the best.
00:37:08.200
And why is that important is because you can rank things when you have a store, for example, based on the popularity,
00:37:15.920
or you can rank things based on the true inner value of that that you infer from these formulas.
00:37:22.960
And what the data shows is that your customers are much more likely to make a purchase if the ranking is based on the quality than it is based on popularity.
00:37:34.460
Because partly they look at it, the ranking, and if they don't like what they see at the front, they walk away from it.
00:37:41.620
If you rank on the true quality that you can infer from this data, they very likely are going to like what they see on the top and they will make a purchase.
00:37:49.720
Okay, so the third law is success is visibility times fitness.
00:37:54.060
So fitness is performance, basically, whether you're good, you know, you can replicate it.
00:37:59.820
So you said earlier, like, so how do you get, in order to be successful, you got to be successful.
00:38:04.920
Let's say there's a young entrepreneur or a young writer who's just starting out.
00:38:11.340
Well, I mean, first of all, is number one is understanding that that mechanism takes place.
00:38:15.940
And then if you're completely novice, then the big question is how you get started, right?
00:38:21.560
And I discuss in the book several studies that show how important is that initial acknowledgement of what you do.
00:38:28.620
My favorite one, actually, is the Kickstarter study, where a colleague, a sociologist from Holland,
00:38:34.840
has gone and randomly picked 200 Kickstarter projects that no one has yet supported because they were brand new.
00:38:48.000
And for one of the groups, he actually gave them a little money proportionate to how much they were asking for.
00:38:59.400
How did the projects in the two groups have done?
00:39:01.360
And what he found a month later is that the groups that he actually gave that initial tiny investment in have had a much, much higher chance of actually succeeding in collecting the money that they had compared to the random one.
00:39:16.880
Which was really odd because these were the groups were randomly decided.
00:39:21.100
So it's not that the group that he chose was any better than the group they didn't choose.
00:39:25.500
And this experiment and many other experiments he has shown, he has done, have shown how important is that initial endorsement.
00:39:34.820
Like, if you get the prize, you're much more likely to get further prize.
00:39:38.200
If you get the support from someone, you're much more likely to get further support.
00:39:42.760
In particular, people who are in the investment business, let's say startups, they know that very well.
00:39:46.680
Once a big name company or even not big name company comes and tries to invest into your company, lots of other investors will come by and say, I also want to do that.
00:39:57.000
The hardest is to get that very first investment, that very first endorsement.
00:40:04.540
The first prize is because we decision makers are very, very risk averse.
00:40:10.120
So if I have a choice between one that has been already endorsed and supported by people whom I trust and one that has never been, I'm much more likely going to choose the one that has been endorsed in others because I feel like I'm reducing my chance of failure by doing so.
00:40:26.300
So the challenge for a young interpreter and for a young person in anything, whether it's science or business or art, is that how you get that initial endorsement, how you get that initial attention, how you get that first award that will make you awardable in the future.
00:40:44.560
And I imagine it goes back to the first rule, building up your network in a smart way can help increase that chances that someone gives you that first endorsement.
00:40:52.900
And you asked earlier, if performance is bounded, then what is it that I can do?
00:41:01.140
And the answer is to pay attention to these random things like this initial endorsement.
00:41:06.900
We have a tendency to say, well, you know, this endorsement will come naturally if I do good work.
00:41:13.260
And what the research is showing is that that's not necessarily so in how they distinguish the success from failure.
00:41:19.680
Okay, so let's move on to the fourth law because people hear, okay,
00:41:24.700
So one thing like a young academic or young writer will think was, if I just collaborate with someone who's bigger than me, that can help me.
00:41:35.600
Well, I mean, often collaboration and teamwork is not a choice.
00:41:40.020
We live in a society that most big tasks cannot be achieved alone.
00:41:46.900
And when you work with others, there are two questions come up.
00:41:53.580
And the second one is, who gets the credit for the team's work?
00:41:58.080
And in the formula, I spent quite one chapter discussing really the research that came out in the last few years to understand what makes the good team.
00:42:09.620
But personally, I think the most important part of this law is not as much how you make a good team.
00:42:16.260
But once the team is there and they have achieved the work, and let's say that they successfully did so, who will walk away with the credit for it?
00:42:28.860
That is, will the person who came up with the idea, will the one who did most of the work, or perhaps the person who really came up with the Heureka moment and solved the problem?
00:42:41.500
Or maybe the promotion will go to the individual who made sure that the coffee is warm on the table at all times.
00:42:49.620
And the reason why this is an important question is because within the team, we understand who did what.
00:42:59.620
But the community outside of the team, the one who rewards you for the team's performance, often and typically has no clue about what was the role of each of the player.
00:43:11.140
And success is based on the perception, not on performance.
00:43:15.160
So, the success eventually and the reward will be determined of what the community perceives about who was responsible for the success of the team.
00:43:25.200
And this is not just kind of a theoretical discussion.
00:43:28.260
We have actually written an algorithm that looks at any research paper published in the scientific literature
00:43:35.660
and decides how much credit each of the author gets, independent of the order of the authors, or how many authors are in the paper.
00:43:46.180
And of course, we have that formula to tell you how much credit you get for the team's work.
00:43:53.360
And to tell us that we are right, we went to areas where the community has already decided who gets the reward for that work,
00:44:02.240
and namely prizes, in a particular Nobel Prizes.
00:44:06.000
So, we've taken all the Nobel Prize winning papers.
00:44:13.100
And we use our algorithm to decide who should get the Nobel Prize.
00:44:17.900
And in about 95% of the cases, we got it exactly right.
00:44:22.000
That the authors we picked are those who actually the Nobel Prize Committee awarded the prize for.
00:44:30.380
And every time we were wrong, there was a juicy story behind that,
00:44:36.480
helping understand, really, the Paris of allocation of credit when it comes to team's work.
00:44:44.700
Just to give you an idea of how it works, you are hosting this podcast, I'm simply the guest.
00:44:52.460
If this podcast will be the most successful podcast that you've ever done,
00:44:56.880
and I'm sure it will be, then it's your credit, not mine.
00:45:02.300
Because you put together the podcast, you chose me as a person to interview,
00:45:08.180
you are asking the question and guiding this conversation.
00:45:11.600
Hence, rightly so, it will be your success if this particular show is well regarded by the community.
00:45:17.820
However, if you and I write a paper about network science,
00:45:23.140
and let's assume that you come up with the idea,
00:45:26.200
and let's assume that you decide to spend the next year in my lab
00:45:29.740
and work out that idea because you are so passionate about it.
00:45:34.300
The truth is that when the paper is published, it's going to be my paper.
00:45:38.580
And it's going to be my paper, not because I did anything in that,
00:45:42.640
but because you and I, being co-authors, you have no track record in network science.
00:45:47.600
And everybody who will read the paper will say,
00:45:52.200
and here is Brad, who's actually helped him to make that a reality.
00:45:56.720
So the credit for a work doesn't really depend on who did what.
00:46:03.000
whose previous and subsequent work most likely aligns with the team's success.
00:46:10.200
That is that, you know, like if you are a scientist,
00:46:13.840
and if you publish many, many papers in network science like I did,
00:46:19.060
you and I publishing a paper means that the credit mostly goes to me
00:46:22.540
because the community sees it as part of my intellectual journey.
00:46:26.760
So which means that really one can actually look at a potential collaboration
00:46:35.820
whether if that work is successful, will I get credit for it or not?
00:46:41.540
And this is very interesting because we're not doing work only to get credit for it.
00:46:46.980
I engage in lots of team activities where I'm not there for credit.
00:46:52.280
I'm there just to make sure that that project is successful
00:47:01.280
you do so because you would like to beg credit and acknowledgements
00:47:09.080
making sure that indeed the project's outcome lines up
00:47:15.760
So what does this mean for like a young, say, an academic
00:47:21.680
They're like, I want to be a network scientist.
00:47:26.700
How can they still benefit that from that collaboration with you
00:47:43.060
Now your first or second or third publication out,
00:47:46.040
but you need to understand that this is not your paper, but my paper.
00:47:54.200
First, go out and speak at every possible venue,
00:48:14.840
And with that, the credit will slowly shift to you.
00:48:26.820
And together, we started working for the first time
00:48:34.720
and what are the fundamental laws of human mobility.
00:48:43.960
And she ended up writing quite a number of fabulous papers
00:48:52.180
So now, when the community would like an expert
00:48:59.420
She rightly so gets much of the credit for the joint work.
00:49:07.480
At some point, you have to differentiate yourself
00:49:11.620
And it's really, the key is not necessarily to say,
00:49:18.460
and I will get the credit for the joint business.
00:49:21.340
No, you have to continue working in the same area
00:49:34.340
because in the Atlantic, we'll talk about what it is,
00:49:56.020
Well, I'm glad we get to talk about the fifth law
00:50:19.140
There is a whole research in the genius literature
00:50:23.440
that we admire today have done their best work,
00:51:04.340
whether there was a Nobel Prize winning discovery
00:51:16.980
personal best work relatively early in their career.
00:51:27.780
most of their big discovery early in their career,
00:51:46.060
And as they age, they do less and less of that.
00:51:49.200
And when we put productivity and success together,
00:51:51.540
we realized that really there is no age dependence
00:52:12.380
that each of them has the same probability of winning.
00:52:33.080
or only young people can be actually successful.
00:52:45.940
because it means that if I continue doing research,
00:52:50.700
that would overshadow everything that I did until now.
00:52:56.200
I discuss in the book the example of John Flann,
00:53:25.860
So at the end, what we learned from this research
00:53:47.600
that will take away from their initial expertise
00:53:58.280
they could come up with their break to discovery
00:54:09.880
he was still in the lab working on the next paper.
00:54:19.620
First law is really about performance drives success,
00:55:07.860
saying that while team success requires diversity
00:55:28.220
for the work you have done in the team setting.
00:55:44.420
is there's sometimes not specific prescriptions