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  • Today, actually, is a very special day for me,

    翻訳: Midori T 校正: Misaki Sato

  • because it is my birthday.

  • (Applause)

  • And so, thanks to all of you for joining the party.

  • (Laughter)

  • But every time you throw a party, there's someone there to spoil it. Right?

  • (Laughter)

  • And I'm a physicist,

  • and this time I brought another physicist along to do so.

  • His name is Albert Einstein -- also Albert -- and he's the one who said

  • that the person who has not made his great contributions to science

  • by the age of 30

  • will never do so.

  • (Laughter)

  • Now, you don't need to check Wikipedia

  • that I'm beyond 30.

  • (Laughter)

  • So, effectively, what he is telling me, and us,

  • is that when it comes to my science,

  • I'm deadwood.

  • Well, luckily, I had my share of luck within my career.

  • Around age 28, I became very interested in networks,

  • and a few years later, we managed to publish a few key papers

  • that reported the discovery of scale-free networks

  • and really gave birth to a new discipline that we call network science today.

  • And if you really care about it, you can get a PhD now in network science

  • in Budapest, in Boston,

  • and you can study it all over the world.

  • A few years later,

  • when I moved to Harvard first as a sabbatical,

  • I became interested in another type of network:

  • that time, the networks within ourselves,

  • how the genes and the proteins and the metabolites link to each other

  • and how they connect to disease.

  • And that interest led to a major explosion within medicine,

  • including the Network Medicine Division at Harvard,

  • that has more than 300 researchers who are using this perspective

  • to treat patients and develop new cures.

  • And a few years ago,

  • I thought that I would take this idea of networks

  • and the expertise we had in networks

  • in a different area,

  • that is, to understand success.

  • And why did we do that?

  • Well, we thought that, to some degree,

  • our success is determined by the networks we're part of --

  • that our networks can push us forward, they can pull us back.

  • And I was curious if we could use the knowledge and big data and expertise

  • where we develop the networks

  • to really quantify how these things happen.

  • This is a result from that.

  • What you see here is a network of galleries in museums

  • that connect to each other.

  • And through this map that we mapped out last year,

  • we are able to predict very accurately the success of an artist

  • if you give me the first five exhibits that he or she had in their career.

  • Well, as we thought about success,

  • we realized that success is not only about networks;

  • there are so many other dimensions to that.

  • And one of the things we need for success, obviously,

  • is performance.

  • So let's define what's the difference between performance and success.

  • Well, performance is what you do:

  • how fast you run, what kind of paintings you paint,

  • what kind of papers you publish.

  • However, in our working definition,

  • success is about what the community notices from what you did,

  • from your performance:

  • How does it acknowledge it, and how does it reward you for it?

  • In other terms,

  • your performance is about you, but your success is about all of us.

  • And this was a very important shift for us,

  • because the moment we defined success as being a collective measure

  • that the community provides to us,

  • it became measurable,

  • because if it's in the community, there are multiple data points about that.

  • So we go to school, we exercise, we practice,

  • because we believe that performance leads to success.

  • But the way we actually started to explore,

  • we realized that performance and success are very, very different animals

  • when it comes to the mathematics of the problem.

  • And let me illustrate that.

  • So what you see here is the fastest man on earth, Usain Bolt.

  • And of course, he wins most of the competitions that he enters.

  • And we know he's the fastest on earth because we have a chronometer

  • to measure his speed.

  • Well, what is interesting about him is that when he wins,

  • he doesn't do so by really significantly outrunning his competition.

  • He's running at most a percent faster than the one who loses the race.

  • And not only does he run only one percent faster than the second one,

  • but he doesn't run 10 times faster than I do --

  • and I'm not a good runner, trust me on that.

  • (Laughter)

  • And every time we are able to measure performance,

  • we notice something very interesting;

  • that is, performance is bounded.

  • What it means is that there are no huge variations in human performance.

  • It varies only in a narrow range,

  • and we do need the chronometer to measure the differences.

  • This is not to say that we cannot see the good from the best ones,

  • but the best ones are very hard to distinguish.

  • And the problem with that is that most of us work in areas

  • where we do not have a chronometer to gauge our performance.

  • Alright, performance is bounded,

  • there are no huge differences between us when it comes to our performance.

  • How about success?

  • Well, let's switch to a different topic, like books.

  • One measure of success for writers is how many people read your work.

  • And so when my previous book came out in 2009,

  • I was in Europe talking with my editor,

  • and I was interested: Who is the competition?

  • And I had some fabulous ones.

  • That week --

  • (Laughter)

  • Dan Brown came out with "The Lost Symbol,"

  • and "The Last Song" also came out,

  • Nicholas Sparks.

  • And when you just look at the list,

  • you realize, you know, performance-wise, there's hardly any difference

  • between these books or mine.

  • Right?

  • So maybe if Nicholas Sparks's team works a little harder,

  • he could easily be number one,

  • because it's almost by accident who ended up at the top.

  • So I said, let's look at the numbers -- I'm a data person, right?

  • So let's see what were the sales for Nicholas Sparks.

  • And it turns out that that opening weekend,

  • Nicholas Sparks sold more than a hundred thousand copies,

  • which is an amazing number.

  • You can actually get to the top of the "New York Times" best-seller list

  • by selling 10,000 copies a week,

  • so he tenfold overcame what he needed to be number one.

  • Yet he wasn't number one.

  • Why?

  • Because there was Dan Brown, who sold 1.2 million copies that weekend.

  • (Laughter)

  • And the reason I like this number is because it shows that, really,

  • when it comes to success, it's unbounded,

  • that the best doesn't only get slightly more than the second best

  • but gets orders of magnitude more,

  • because success is a collective measure.

  • We give it to them, rather than we earn it through our performance.

  • So one of things we realized is that performance, what we do, is bounded,

  • but success, which is collective, is unbounded,

  • which makes you wonder:

  • How do you get these huge differences in success

  • when you have such tiny differences in performance?

  • And recently, I published a book that I devoted to that very question.

  • And they didn't give me enough time to go over all of that,

  • so I'm going to go back to the question of,

  • alright, you have success; when should that appear?

  • So let's go back to the party spoiler and ask ourselves:

  • Why did Einstein make this ridiculous statement,

  • that only before 30 you could actually be creative?

  • Well, because he looked around himself and he saw all these fabulous physicists

  • that created quantum mechanics and modern physics,

  • and they were all in their 20s and early 30s when they did so.

  • And it's not only him.

  • It's not only observational bias,

  • because there's actually a whole field of genius research

  • that has documented the fact that,

  • if we look at the people we admire from the past

  • and then look at what age they made their biggest contribution,

  • whether that's music, whether that's science,

  • whether that's engineering,

  • most of them tend to do so in their 20s, 30s, early 40s at most.

  • But there's a problem with this genius research.

  • Well, first of all, it created the impression to us

  • that creativity equals youth,

  • which is painful, right?

  • (Laughter)

  • And it also has an observational bias,

  • because it only looks at geniuses and doesn't look at ordinary scientists

  • and doesn't look at all of us and ask,

  • is it really true that creativity vanishes as we age?

  • So that's exactly what we tried to do,

  • and this is important for that to actually have references.

  • So let's look at an ordinary scientist like myself,

  • and let's look at my career.

  • So what you see here is all the papers that I've published

  • from my very first paper, in 1989; I was still in Romania when I did so,

  • till kind of this year.

  • And vertically, you see the impact of the paper,

  • that is, how many citations,

  • how many other papers have been written that cited that work.

  • And when you look at that,

  • you see that my career has roughly three different stages.

  • I had the first 10 years where I had to work a lot

  • and I don't achieve much.

  • No one seems to care about what I do, right?

  • There's hardly any impact.

  • (Laughter)

  • That time, I was doing material science,

  • and then I kind of discovered for myself networks

  • and then started publishing in networks.

  • And that led from one high-impact paper to the other one.

  • And it really felt good. That was that stage of my career.

  • (Laughter)

  • So the question is, what happens right now?

  • And we don't know, because there hasn't been enough time passed yet

  • to actually figure out how much impact those papers will get;

  • it takes time to acquire.

  • Well, when you look at the data,

  • it seems to be that Einstein, the genius research, is right,

  • and I'm at that stage of my career.

  • (Laughter)

  • So we said, OK, let's figure out how does this really happen,

  • first in science.

  • And in order not to have the selection bias,

  • to look only at geniuses,

  • we ended up reconstructing the career of every single scientist

  • from 1900 till today

  • and finding for all scientists what was their personal best,

  • whether they got the Nobel Prize or they never did,

  • or no one knows what they did, even their personal best.

  • And that's what you see in this slide.

  • Each line is a career,

  • and when you have a light blue dot on the top of that career,

  • it says that was their personal best.

  • And the question is,

  • when did they actually make their biggest discovery?

  • To quantify that,

  • we look at what's the probability that you make your biggest discovery,

  • let's say, one, two, three or 10 years into your career?

  • We're not looking at real age.

  • We're looking at what we call "academic age."

  • Your academic age starts when you publish your first papers.

  • I know some of you are still babies.

  • (Laughter)

  • So let's look at the probability

  • that you publish your highest-impact paper.

  • And what you see is, indeed, the genius research is right.

  • Most scientists tend to publish their highest-impact paper

  • in the first 10, 15 years in their career,

  • and it tanks after that.

  • It tanks so fast that I'm about -- I'm exactly 30 years into my career,

  • and the chance that I will publish a paper that would have a higher impact

  • than anything that I did before

  • is less than one percent.

  • I am in that stage of my career, according to this data.

  • But there's a problem with that.

  • We're not doing controls properly.

  • So the control would be,

  • what would a scientist look like who makes random contribution to science?

  • Or what is the productivity of the scientist?

  • When do they write papers?

  • So we measured the productivity,

  • and amazingly, the productivity,

  • your likelihood of writing a paper in year one, 10 or 20 in your career,

  • is indistinguishable from the likelihood of having the impact

  • in that part of your career.