TEDxMidAtlantic
Albert-László Barabási: The real relationship between your age and your chance of success
艾伯特拉斯洛 · 巴拉巴西: 年齡和成功機會之間的真正關係
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透過數學分析，網路理論家艾伯特拉斯洛 · 巴拉巴西帶大家探究驅使成功背後隱藏的機制，不論你的領域是什麼。他還揭露出年齡和成功機會之間的有趣關聯。
Albert-László Barabási - Network scientist
A pioneer in network science, Albert-László Barabási uncovers the hidden order behind complex systems. Full bio
A pioneer in network science, Albert-László Barabási uncovers the hidden order behind complex systems. Full bio
Double-click the English transcript below to play the video.
00:12
Today, actually, is
a very special day for me,
a very special day for me,
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今天對我來說是個很特別的一天，
00:14
because it is my birthday.
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因為今天是我的生日。
00:16
(Applause)
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（掌聲）
00:20
And so, thanks to all of you
for joining the party.
for joining the party.
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謝謝大家來參加這場派對。
00:24
(Laughter)
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（笑聲）
00:25
But every time you throw a party,
there's someone there to spoil it. Right?
there's someone there to spoil it. Right?
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但，每次你辦派對時，
總會掃興的人，對吧？
總會掃興的人，對吧？
00:30
(Laughter)
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（笑聲）
00:31
And I'm a physicist,
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我是物理學家，
00:32
and this time I brought
another physicist along to do so.
another physicist along to do so.
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這次，我帶來了
另一位來掃興的物理學家。
另一位來掃興的物理學家。
00:36
His name is Albert Einstein --
also Albert -- and he's the one who said
also Albert -- and he's the one who said
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他叫做阿爾伯特愛因斯坦——
也叫阿爾伯特——他說過
也叫阿爾伯特——他說過
00:41
that the person who has not made
his great contributions to science
his great contributions to science
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如果一個人到了三十歲
都還沒有對科學
都還沒有對科學
做出偉大的貢獻，
就永遠不會有貢獻了。
就永遠不會有貢獻了。
00:46
by the age of 30
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00:47
will never do so.
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00:49
(Laughter)
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（笑聲）
00:50
Now, you don't need to check Wikipedia
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各位不需要去維基百科查證，
00:52
that I'm beyond 30.
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我已經超過三十歲了。
00:54
(Laughter)
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（笑聲）
00:55
So, effectively, what
he is telling me, and us,
he is telling me, and us,
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所以，實際上，他要
告訴我以及我們的是，
告訴我以及我們的是，
00:59
is that when it comes to my science,
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在我的科學領域中，
01:01
I'm deadwood.
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我已經是枯枝。
01:04
Well, luckily, I had my share
of luck within my career.
of luck within my career.
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嗯，幸運的是，我在
我的職涯中有好運氣。
我的職涯中有好運氣。
01:10
Around age 28, I became
very interested in networks,
very interested in networks,
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大約二十八歲時，
我對於網路非常感興趣，
我對於網路非常感興趣，
01:13
and a few years later, we managed
to publish a few key papers
to publish a few key papers
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幾年後，我們出版了
幾篇重要論文，
幾篇重要論文，
01:18
that reported the discovery
of scale-free networks
of scale-free networks
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闡述我們發現了無尺度網路，
01:22
and really gave birth to a new discipline
that we call network science today.
that we call network science today.
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創造出了一門新的學科，
就是現今所稱的網路科學。
就是現今所稱的網路科學。
01:26
And if you really care about it,
you can get a PhD now in network science
you can get a PhD now in network science
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如果各位想知道，現在可以
取得網路科學博士學位的地方
取得網路科學博士學位的地方
01:30
in Budapest, in Boston,
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包括布達佩斯、波士頓，
01:32
and you can study it all over the world.
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且在全世界各地都可以研讀它。
01:35
A few years later,
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幾年後，
01:37
when I moved to Harvard
first as a sabbatical,
first as a sabbatical,
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我搬到哈佛，一開始是學術休假，
01:40
I became interested
in another type of network:
in another type of network:
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我開始對另一種網路產生了興趣：
01:43
that time, the networks within ourselves,
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我們體內的網路，
01:46
how the genes and the proteins
and the metabolites link to each other
and the metabolites link to each other
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基因、蛋白質、代謝物
彼此之間如何連結，
彼此之間如何連結，
01:50
and how they connect to disease.
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以及它們和疾病的關係。
01:53
And that interest led
to a major explosion within medicine,
to a major explosion within medicine,
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那項興趣導致了醫學上的大爆炸，
01:57
including the Network Medicine
Division at Harvard,
Division at Harvard,
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包括哈佛的網路醫學部門，
02:01
that has more than 300 researchers
who are using this perspective
who are using this perspective
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有超過三百名研究者使用這種觀點
02:05
to treat patients and develop new cures.
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來治療病人和開發新解藥。
02:09
And a few years ago,
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幾年前，
02:11
I thought that I would take
this idea of networks
this idea of networks
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我認為我可以把網路的這個點子
02:13
and the expertise we had in networks
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以及我們對網路的專長
帶到不同的領域去，
帶到不同的領域去，
02:15
in a different area,
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02:17
that is, to understand success.
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也就是，用來了解成功。
02:19
And why did we do that?
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為什麼要那樣做？
02:20
Well, we thought that, to some degree,
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嗯，我們認為，在某種程度上，
02:23
our success is determined
by the networks we're part of --
by the networks we're part of --
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我們的成功是由我們所屬的網路決定，
02:26
that our networks can push us forward,
they can pull us back.
they can pull us back.
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我們的網路將我們向前推進，
也可以讓我們遲滯不前。
也可以讓我們遲滯不前。
02:30
And I was curious if we could use
the knowledge and big data and expertise
the knowledge and big data and expertise
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我很好奇，我們是否
能用這知識和大數據
能用這知識和大數據
及我們開發網路的專門技術
來將成功的發生給量化。
來將成功的發生給量化。
02:35
where we develop the networks
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02:36
to really quantify
how these things happen.
how these things happen.
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這就是研究的結果。
02:40
This is a result from that.
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02:41
What you see here is a network
of galleries in museums
of galleries in museums
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各位現在看到的是
博物館的畫廊的網路，
博物館的畫廊的網路，
02:44
that connect to each other.
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它們彼此連結。
02:46
And through this map
that we mapped out last year,
that we mapped out last year,
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透過我們去年畫的這張地圖，
02:50
we are able to predict very accurately
the success of an artist
the success of an artist
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我們就可以很精確地預測
一位藝術家是否會成功，
一位藝術家是否會成功，
02:55
if you give me the first five exhibits
that he or she had in their career.
that he or she had in their career.
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只要給我這位藝術家
在職涯中的最早五件展示品。
在職涯中的最早五件展示品。
03:01
Well, as we thought about success,
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當我們在思考成功時，
03:04
we realized that success
is not only about networks;
is not only about networks;
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我們發現，成功不只和網路有關；
03:07
there are so many
other dimensions to that.
other dimensions to that.
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還有好多其他的維度。
03:10
And one of the things
we need for success, obviously,
we need for success, obviously,
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很顯然，我們想要成功
就一定需要的一樣東西
就一定需要的一樣東西
03:13
is performance.
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就是表現。
03:14
So let's define what's the difference
between performance and success.
between performance and success.
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所以，咱們來定義一下
表現和成功之間的差別。
表現和成功之間的差別。
03:18
Well, performance is what you do:
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表現是你所做的事：
03:20
how fast you run,
what kind of paintings you paint,
what kind of paintings you paint,
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你能跑多快、你畫出什麼樣的畫、
03:23
what kind of papers you publish.
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你出版什麼樣的論文。
03:25
However, in our working definition,
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然而，根據我們的工作定義，
03:28
success is about what the community
notices from what you did,
notices from what you did,
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成功的重點在於大家
能注意到你做了什麼、
能注意到你做了什麼、
03:32
from your performance:
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你的表現如何：
03:34
How does it acknowledge it,
and how does it reward you for it?
and how does it reward you for it?
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怎麼認可你的表現，
你的表現帶給你什麼報償？
你的表現帶給你什麼報償？
03:38
In other terms,
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換言之，
03:39
your performance is about you,
but your success is about all of us.
but your success is about all of us.
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你的表現是你的事，
但你的成功是我們所有人的事。
但你的成功是我們所有人的事。
03:45
And this was a very
important shift for us,
important shift for us,
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這對我們來說是很重要的轉變，
03:48
because the moment we defined success
as being a collective measure
as being a collective measure
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因為當我們把成功定義為
團體提供我們的一個集體測量值，
它就變成可測量的，
它就變成可測量的，
03:52
that the community provides to us,
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03:54
it became measurable,
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03:56
because if it's in the community,
there are multiple data points about that.
there are multiple data points about that.
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因為如果它是在團體中，
就有相關的許多資料點。
就有相關的許多資料點。
04:00
So we go to school,
we exercise, we practice,
we exercise, we practice,
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所以我們去學校，
我們做作業，我們練習，
我們做作業，我們練習，
04:06
because we believe
that performance leads to success.
that performance leads to success.
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因為我們相信表現會導致成功。
但我們這樣開始探究之後，
04:09
But the way we actually
started to explore,
started to explore,
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04:11
we realized that performance and success
are very, very different animals
are very, very different animals
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便了解到在數學問題上，
表現和成功非常不同。
04:15
when it comes to
the mathematics of the problem.
the mathematics of the problem.
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04:18
And let me illustrate that.
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讓我說明一下。
04:20
So what you see here is
the fastest man on earth, Usain Bolt.
the fastest man on earth, Usain Bolt.
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各位在這裡看到的是世界上
最快的人，尤塞恩博爾特。
最快的人，尤塞恩博爾特。
04:25
And of course, he wins most of
the competitions that he enters.
the competitions that he enters.
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當然，他參加的比賽，
他大部分都有贏。
他大部分都有贏。
04:30
And we know he's the fastest on earth
because we have a chronometer
because we have a chronometer
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我們知道他跑得最快，因為我們
有精密計時器來測量速度。
有精密計時器來測量速度。
04:33
to measure his speed.
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04:34
Well, what is interesting about him
is that when he wins,
is that when he wins,
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關於他，有一點很有趣，
那就是當他贏的時候，
那就是當他贏的時候，
04:38
he doesn't do so by really significantly
outrunning his competition.
outrunning his competition.
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他並不是明顯超越他的對手許多。
04:44
He's running at most a percent faster
than the one who loses the race.
than the one who loses the race.
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他最多是比輸家快 1% 而已。
04:49
And not only does he run only
one percent faster than the second one,
one percent faster than the second one,
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他不僅只比第二名快 1%，
04:53
but he doesn't run
10 times faster than I do --
10 times faster than I do --
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他也沒有跑得比我快十倍——
04:56
and I'm not a good runner,
trust me on that.
trust me on that.
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我不是個好跑者，相信我。
04:58
(Laughter)
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（笑聲）
04:59
And every time we are able
to measure performance,
to measure performance,
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每當我們能夠測量表現時，
05:03
we notice something very interesting;
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我們就會注意到一件很有趣的事；
05:05
that is, performance is bounded.
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那就是，表現是受限的。
05:07
What it means is that there are
no huge variations in human performance.
no huge variations in human performance.
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意思就是說，人類的表現
並沒有太大的變動。
並沒有太大的變動。
05:11
It varies only in a narrow range,
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人類表現只在一個小範圍中變動，
05:14
and we do need the chronometer
to measure the differences.
to measure the differences.
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我們的確需要很精密的
計時器才能測出差別。
計時器才能測出差別。
05:18
This is not to say that we cannot
see the good from the best ones,
see the good from the best ones,
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這並不是說我們分不出
好和最好的差別，
好和最好的差別，
05:21
but the best ones
are very hard to distinguish.
are very hard to distinguish.
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而是很難分辨出最好的人。
05:24
And the problem with that
is that most of us work in areas
is that most of us work in areas
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那所造成的問題就是，
我們大部分人工作的領域中
我們大部分人工作的領域中
05:27
where we do not have a chronometer
to gauge our performance.
to gauge our performance.
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並沒有精密的計時器
來測量我們的表現。
來測量我們的表現。
05:31
Alright, performance is bounded,
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好，表現是受限的，
05:32
there are no huge differences between us
when it comes to our performance.
when it comes to our performance.
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我們之間在表現上
沒有很大的差異。
沒有很大的差異。
05:36
How about success?
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那成功呢？
05:37
Well, let's switch to
a different topic, like books.
a different topic, like books.
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咱們切換到一個不同的
主題，以書籍為例。
主題，以書籍為例。
05:40
One measure of success for writers is
how many people read your work.
how many people read your work.
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對作家來說，成功的測量值之一
就是有多少人讀你的作品。
就是有多少人讀你的作品。
05:46
And so when my previous book
came out in 2009,
came out in 2009,
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我的上一本書在 2009 年推出時，
05:51
I was in Europe talking with my editor,
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我在歐洲跟我的編輯談，
05:53
and I was interested:
Who is the competition?
Who is the competition?
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我很感興趣：競爭對手是誰？
05:56
And I had some fabulous ones.
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我有一些很棒的對手。
05:59
That week --
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那週——
06:00
(Laughter)
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（笑聲）
06:01
Dan Brown came out with "The Lost Symbol,"
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丹布朗推出《失落的符號》，
《最後一首歌》也推出了，
06:04
and "The Last Song" also came out,
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尼可拉斯史派克的作品。
06:07
Nicholas Sparks.
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06:09
And when you just look at the list,
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當你只是看列表，
06:12
you realize, you know, performance-wise,
there's hardly any difference
there's hardly any difference
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你會知道，就表現來說，
這些書和我的書之間
幾乎沒有什麼差別。
幾乎沒有什麼差別。
06:15
between these books or mine.
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06:17
Right?
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對吧？
06:18
So maybe if Nicholas Sparks's team
works a little harder,
works a little harder,
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所以，也許尼可拉斯史派克的
團隊更努力一點，
團隊更努力一點，
06:23
he could easily be number one,
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他很容易成為第一名，
06:25
because it's almost by accident
who ended up at the top.
who ended up at the top.
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因為誰會在頂端幾乎都是意外。
06:28
So I said, let's look at the numbers --
I'm a data person, right?
I'm a data person, right?
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所以，我說，咱們來看看數字，
我是研究資料的人，對吧？
我是研究資料的人，對吧？
06:31
So let's see what were
the sales for Nicholas Sparks.
the sales for Nicholas Sparks.
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咱們來看看尼可拉斯
史派克的銷售額如何。
史派克的銷售額如何。
06:36
And it turns out that
that opening weekend,
that opening weekend,
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結果發現，在第一個週末，
06:38
Nicholas Sparks sold more than
a hundred thousand copies,
a hundred thousand copies,
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尼可拉斯史派克
賣出了超過十萬本書，
賣出了超過十萬本書，
06:41
which is an amazing number.
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這個數字很驚人。
06:42
You can actually get to the top
of the "New York Times" best-seller list
of the "New York Times" best-seller list
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只要一週銷售一萬本，
就可以登上《紐約時報》
暢銷書排行榜了，
暢銷書排行榜了，
06:46
by selling 10,000 copies a week,
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06:48
so he tenfold overcame
what he needed to be number one.
what he needed to be number one.
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所以他超越了成為第一名
需要的數字足足十倍。
需要的數字足足十倍。
06:52
Yet he wasn't number one.
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但，他並非第一名。為什麼？
06:53
Why?
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06:54
Because there was Dan Brown,
who sold 1.2 million copies that weekend.
who sold 1.2 million copies that weekend.
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因為還有丹布朗，那個週末，
他的書賣了一百二十萬本。
他的書賣了一百二十萬本。
06:59
(Laughter)
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（笑聲）
07:01
And the reason I like this number
is because it shows that, really,
is because it shows that, really,
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我喜歡這些數字是因為，
它呈現出成功是沒有限制的，
07:05
when it comes to success, it's unbounded,
137
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07:08
that the best doesn't only get
slightly more than the second best
slightly more than the second best
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第一名並不會只比第二名多一點，
07:14
but gets orders of magnitude more,
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而是用指數倍數來算，
07:17
because success is a collective measure.
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因為成功是集體的測量值。
07:20
We give it to them, rather than
we earn it through our performance.
we earn it through our performance.
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我們把成功給他們，而不是
透過自己的表現贏來成功的。
透過自己的表現贏來成功的。
07:24
So one of things we realized is that
performance, what we do, is bounded,
performance, what we do, is bounded,
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我們了解到，表現，
也就是我們所做的，會受限，
也就是我們所做的，會受限，
07:30
but success, which is
collective, is unbounded,
collective, is unbounded,
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但成功，是集體的，沒有限制，
07:32
which makes you wonder:
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這就會讓人納悶：
07:34
How do you get these
huge differences in success
huge differences in success
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如果在表現上只能有小小的差別，
07:37
when you have such tiny
differences in performance?
differences in performance?
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在成功上如何造成
這麼巨大的差別？
這麼巨大的差別？
07:40
And recently, I published a book
that I devoted to that very question.
that I devoted to that very question.
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最近，我出版了一本書，
就是針對這個問題而寫的。
就是針對這個問題而寫的。
07:44
And they didn't give me enough time
to go over all of that,
to go over all of that,
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他們沒有給我足夠的時間
去談所有這些，
去談所有這些，
07:47
so I'm going to go back
to the question of,
to the question of,
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所以我要回到這個問題，
07:49
alright, you have success;
when should that appear?
when should that appear?
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好，你有成功；它會何時出現？
07:52
So let's go back to the party spoiler
and ask ourselves:
and ask ourselves:
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咱們回到讓派對掃興的
那個人，問問我們自己：
那個人，問問我們自己：
07:57
Why did Einstein make
this ridiculous statement,
this ridiculous statement,
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為什麼愛因斯坦
會說出那句荒謬的話，
會說出那句荒謬的話，
08:00
that only before 30
you could actually be creative?
you could actually be creative?
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說只有在三十歲之前
你才可能真的有創意？
你才可能真的有創意？
08:03
Well, because he looked around himself
and he saw all these fabulous physicists
and he saw all these fabulous physicists
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因為他看看自己身邊，
這些很出色的物理學家，
這些很出色的物理學家，
08:08
that created quantum mechanics
and modern physics,
and modern physics,
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發明了量子力學和近代物理的人，
08:11
and they were all in their 20s
and early 30s when they did so.
and early 30s when they did so.
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他們提出發明時都是
二十多歲或三十初頭。
二十多歲或三十初頭。
08:15
And it's not only him.
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不只是他而已。
08:16
It's not only observational bias,
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這並不是觀察偏見，
08:18
because there's actually
a whole field of genius research
a whole field of genius research
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因為有一整個領域的天才研究
08:22
that has documented the fact that,
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記錄這個事實，
08:24
if we look at the people
we admire from the past
we admire from the past
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如果我們去看我們
所欣賞的過去人物，
所欣賞的過去人物，
08:28
and then look at what age
they made their biggest contribution,
they made their biggest contribution,
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看看他們做出最大貢獻的年齡，
08:31
whether that's music,
whether that's science,
whether that's science,
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2096
不論是音樂、不論是科學、
08:33
whether that's engineering,
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1619
不論是工程，
08:35
most of them tend to do so
in their 20s, 30s, early 40s at most.
in their 20s, 30s, early 40s at most.
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6123
大部分都是在二、三十歲時達成，
最多四十初頭。
08:41
But there's a problem
with this genius research.
with this genius research.
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2791
但這種天才研究有一個問題。
08:45
Well, first of all, it created
the impression to us
the impression to us
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首先，它讓我們有一種印象，
08:48
that creativity equals youth,
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認為創意等同年輕，
08:52
which is painful, right?
169
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1610
這很痛，對吧？
08:53
(Laughter)
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1951
（笑聲）
08:55
And it also has an observational bias,
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它也有存在觀察偏見，
08:59
because it only looks at geniuses
and doesn't look at ordinary scientists
and doesn't look at ordinary scientists
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因為它只研究天才，
沒有研究一般科學家，
沒有研究一般科學家，
09:04
and doesn't look at all of us and ask,
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1965
且沒有研究我們所有人並問：
09:06
is it really true that creativity
vanishes as we age?
vanishes as we age?
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3185
真的在我們年長之後
創意就消失嗎？
創意就消失嗎？
09:10
So that's exactly what we tried to do,
175
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1877
那就是我們試圖要做的，
09:12
and this is important for that
to actually have references.
to actually have references.
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3803
能真正有參考是很重要的。
09:16
So let's look at an ordinary
scientist like myself,
scientist like myself,
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咱們來看看一般的
科學家，像我自己，
科學家，像我自己，
09:18
and let's look at my career.
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1522
來看看我的職涯。
09:20
So what you see here is all the papers
that I've published
that I've published
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3202
這裡是我出版過的所有論文，
09:23
from my very first paper, in 1989;
I was still in Romania when I did so,
I was still in Romania when I did so,
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5115
我的第一篇論文在 1989 年出版，
當時我還在羅馬尼亞，
09:28
till kind of this year.
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556688
1593
直到今年。
09:30
And vertically, you see
the impact of the paper,
the impact of the paper,
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2518
垂直來看，可以看到論文的影響，
09:33
that is, how many citations,
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1403
也就是引用數，
09:34
how many other papers
have been written that cited that work.
have been written that cited that work.
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3988
有多少篇其他論文
曾經引用過那篇文章。
曾經引用過那篇文章。
如果去看那些，就會發現
我的職涯大致可以分為三個階段。
我的職涯大致可以分為三個階段。
09:39
And when you look at that,
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567397
1300
09:40
you see that my career
has roughly three different stages.
has roughly three different stages.
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2813
前十年，我很努力工作，
沒有很高的成就。
沒有很高的成就。
09:43
I had the first 10 years
where I had to work a lot
where I had to work a lot
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2435
09:46
and I don't achieve much.
188
574017
1276
似乎沒有人在乎我做什麼，對吧？
09:47
No one seems to care
about what I do, right?
about what I do, right?
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2118
09:49
There's hardly any impact.
190
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1681
幾乎沒有任何影響力。
09:51
(Laughter)
191
579164
1404
（笑聲）
09:52
That time, I was doing material science,
192
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2887
那段時間，我在做材料科學，
09:55
and then I kind of discovered
for myself networks
for myself networks
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3691
接著，我發現了網路，
09:59
and then started publishing in networks.
194
587218
1947
接著開始出版網路的文章。
10:01
And that led from one high-impact
paper to the other one.
paper to the other one.
195
589189
3073
導致了一篇又一篇的
高影響力論文出現。
高影響力論文出現。
10:04
And it really felt good.
That was that stage of my career.
That was that stage of my career.
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3104
感覺真的很好，我職涯的那個階段。
10:07
(Laughter)
197
595414
1282
（笑聲）
10:08
So the question is,
what happens right now?
what happens right now?
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3208
問題是，現在會發生什麼事？
10:12
And we don't know, because there
hasn't been enough time passed yet
hasn't been enough time passed yet
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600587
3239
我們不知道，因為
還沒有經過那麼多時間，
還沒有經過那麼多時間，
無法得知那些論文的影響會有
多大；那需要時間才能知道。
多大；那需要時間才能知道。
10:15
to actually figure out how much impact
those papers will get;
those papers will get;
200
603850
2987
10:18
it takes time to acquire.
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606861
1227
如果去看資料，似乎，愛因斯坦，
那些天才研究，是對的，
那些天才研究，是對的，
10:20
Well, when you look at the data,
202
608112
1569
10:21
it seems to be that Einstein,
the genius research, is right,
the genius research, is right,
203
609705
2854
10:24
and I'm at that stage of my career.
204
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1811
我正在職涯的那個階段。
10:26
(Laughter)
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2308
（笑聲）
10:28
So we said, OK, let's figure out
how does this really happen,
how does this really happen,
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5974
所以，我們說，好，
咱們來研究看看這是如何發生的，
10:34
first in science.
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622748
1778
先看科學。
10:36
And in order not to have
the selection bias,
the selection bias,
208
624550
3632
為了避免選樣偏誤，
10:40
to look only at geniuses,
209
628206
1337
只去研究天才，
10:41
we ended up reconstructing the career
of every single scientist
of every single scientist
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3716
我們最後為每一位
科學家都重建了職涯，
科學家都重建了職涯，
10:45
from 1900 till today
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2502
從 1900 年至今的所有科學家，
10:47
and finding for all scientists
what was their personal best,
what was their personal best,
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3712
並針對所有科學家，
找出他們個人的顛峰時期，
找出他們個人的顛峰時期，
10:51
whether they got the Nobel Prize
or they never did,
or they never did,
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639569
2812
不論他們是否有得到諾貝爾獎，
10:54
or no one knows what they did,
even their personal best.
even their personal best.
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642405
3407
或者即使他們在顛峰時
也沒有人知道他們做了什麼。
也沒有人知道他們做了什麼。
10:57
And that's what you see in this slide.
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1915
那就是這張投影片呈現的。
10:59
Each line is a career,
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1573
每一條線就是一段職涯，
11:01
and when you have a light blue dot
on the top of that career,
on the top of that career,
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3003
淡藍色的點就是那職涯的顛峰，
11:04
it says that was their personal best.
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2040
那是他們個人的最佳狀態。
問題是，他們何時
有最重大的發現？
有最重大的發現？
11:06
And the question is,
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1155
11:07
when did they actually make
their biggest discovery?
their biggest discovery?
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655642
3568
11:11
To quantify that,
221
659234
1165
為了量化它，我們去研究
做出最重大發現的機率，
做出最重大發現的機率，
11:12
we look at what's the probability
that you make your biggest discovery,
that you make your biggest discovery,
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3376
比如，你的職涯開始之後的
一、二、三，或十年？
一、二、三，或十年？
11:15
let's say, one, two, three
or 10 years into your career?
or 10 years into your career?
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663823
2672
11:18
We're not looking at real age.
224
666519
1480
我們研究的不是真實年齡，
而是所謂的「學術年齡」。
而是所謂的「學術年齡」。
11:20
We're looking at
what we call "academic age."
what we call "academic age."
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668023
2134
11:22
Your academic age starts
when you publish your first papers.
when you publish your first papers.
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3250
你的學術年齡開始於
你的第一篇論文被刊出時。
你的第一篇論文被刊出時。
11:25
I know some of you are still babies.
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1779
我知道在座還有一些嬰兒。
11:27
(Laughter)
228
675258
1397
（笑聲）
11:28
So let's look at the probability
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676679
2706
咱們來看看你出版
最有影響力的論文的機率。
11:31
that you publish
your highest-impact paper.
your highest-impact paper.
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679409
2066
11:33
And what you see is, indeed,
the genius research is right.
the genius research is right.
231
681499
3071
各位可以看見，的確，
天才研究是對的。
天才研究是對的。
11:36
Most scientists tend to publish
their highest-impact paper
their highest-impact paper
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684594
3024
大部分的科學家傾向會在
職涯的前十、十五年
職涯的前十、十五年
11:39
in the first 10, 15 years in their career,
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687642
2899
出版他們最有影響力的論文，
11:42
and it tanks after that.
234
690565
3133
之後就開始下滑。
11:45
It tanks so fast that I'm about --
I'm exactly 30 years into my career,
I'm exactly 30 years into my career,
235
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5107
下滑的速度很快，我大約——
我現在正在我職涯的三十年，
11:50
and the chance that I will publish a paper
that would have a higher impact
that would have a higher impact
236
698853
3540
我有可能出版一篇
比我以前所有論文
比我以前所有論文
都更有影響力的論文的機率，
11:54
than anything that I did before
237
702417
1940
11:56
is less than one percent.
238
704381
1353
低於 1%。
11:57
I am in that stage of my career,
according to this data.
according to this data.
239
705758
3049
根據這些資料，我現在
就處在職涯的那個階段。
就處在職涯的那個階段。
12:01
But there's a problem with that.
240
709648
1843
但有個問題。
12:03
We're not doing controls properly.
241
711515
3675
我們沒有把控制做好。
12:07
So the control would be,
242
715214
1417
控制指的是，
12:08
what would a scientist look like
who makes random contribution to science?
who makes random contribution to science?
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716655
4607
對科學做出隨機貢獻的科學家
看起來會是什麼樣子的？
看起來會是什麼樣子的？
12:13
Or what is the productivity
of the scientist?
of the scientist?
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2995
或，那位科學家的產能會是什麼？
12:16
When do they write papers?
245
724305
2006
他們何時撰寫論文？
12:18
So we measured the productivity,
246
726335
2444
所以我們測量了產能，
12:20
and amazingly, the productivity,
247
728803
2052
很驚人的是，產能，
12:22
your likelihood of writing a paper
in year one, 10 or 20 in your career,
in year one, 10 or 20 in your career,
248
730879
4131
你在職涯第一、十、二十年
寫一篇論文的可能性，
寫一篇論文的可能性，
12:27
is indistinguishable from the likelihood
of having the impact
of having the impact
249
735034
3606
很接近在你職涯的那個部分
有所影響的可能性。
12:30
in that part of your career.
250
738664
1775
12:33
And to make a long story short,
251
741026
1783
長話短說，
12:34
after lots of statistical tests,
there's only one explanation for that,
there's only one explanation for that,
252
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4228
經過許多統計檢定，
只找出了一個解釋，
只找出了一個解釋，
12:39
that really, the way we scientists work
253
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2894
那就是，我們科學家工作的方式，
12:42
is that every single paper we write,
every project we do,
every project we do,
254
750003
3633
我們所寫的每一篇論文，
我們所做的每一個研究計畫，
我們所做的每一個研究計畫，
12:45
has exactly the same chance
of being our personal best.
of being our personal best.
255
753660
4160
都有同等的機會成為
我們個人的最佳作。
我們個人的最佳作。
12:49
That is, discovery is like
a lottery ticket.
a lottery ticket.
256
757844
4953
也就是說，探究
就像是買彩券一樣。
就像是買彩券一樣。
12:54
And the more lottery tickets we buy,
257
762821
2351
我們買越多彩券，
12:57
the higher our chances.
258
765196
1507
機會就越高。
12:58
And it happens to be so
259
766727
1559
只是剛好
13:00
that most scientists buy
most of their lottery tickets
most of their lottery tickets
260
768310
2719
大部分的科學家是在
職涯的前十、十五年
職涯的前十、十五年
13:03
in the first 10, 15 years of their career,
261
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2460
買了他們大部分的彩券而已，
13:05
and after that,
their productivity decreases.
their productivity decreases.
262
773537
3413
那之後，他們的產能就下降了。
13:09
They're not buying
any more lottery tickets.
any more lottery tickets.
263
777411
2084
他們不再買更多的彩券。
13:11
So it looks as if
they would not be creative.
they would not be creative.
264
779519
3444
所以看起來就好像是
他們沒有創意了。
他們沒有創意了。
13:14
In reality, they stopped trying.
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現實上，他們只是沒再嘗試。
13:17
So when we actually put the data together,
the conclusion is very simple:
the conclusion is very simple:
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所以當我們把資料拼在一起，
結論就非常簡單：
結論就非常簡單：
13:21
success can come at any time.
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成功隨時都可能到來。
13:23
It could be your very first
or very last paper of your career.
or very last paper of your career.
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可能是你職涯中的第一篇
或最後一篇論文。
或最後一篇論文。
13:27
It's totally random
in the space of the projects.
in the space of the projects.
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在研究計畫的空間中，
這完全是隨機的。
這完全是隨機的。
13:31
It is the productivity that changes.
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改變的是產能。
13:33
Let me illustrate that.
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讓我說明一下。
13:35
Here is Frank Wilczek,
who got the Nobel Prize in Physics
who got the Nobel Prize in Physics
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這是弗朗克韋爾切克，
得過諾貝爾物理獎，
得過諾貝爾物理獎，
13:38
for the very first paper he ever wrote
in his career as a graduate student.
in his career as a graduate student.
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得獎的是他研究生
職涯中的第一篇論文。
職涯中的第一篇論文。
13:42
(Laughter)
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（笑聲）
13:43
More interesting is John Fenn,
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更有趣的是約翰芬恩，
13:46
who, at age 70, was forcefully retired
by Yale University.
by Yale University.
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他在七十歲時被迫
從耶魯大學退休。
從耶魯大學退休。
13:51
They shut his lab down,
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他們關掉了他的實驗室，
13:53
and at that moment, he moved
to Virginia Commonwealth University,
to Virginia Commonwealth University,
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那時，他搬到維吉尼亞聯邦大學，
13:57
opened another lab,
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開了另一間實驗室，
13:58
and it is there, at age 72,
that he published a paper
that he published a paper
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在那裡，七十二歲時，
他刊出了一篇論文，
他刊出了一篇論文，
14:02
for which, 15 years later, he got
the Nobel Prize for Chemistry.
the Nobel Prize for Chemistry.
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十五年後，那篇論文
讓他得了諾貝爾化學獎。
讓他得了諾貝爾化學獎。
14:06
And you think, OK,
well, science is special,
well, science is special,
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你們會想，好，科學是比較特別，
14:10
but what about other areas
where we need to be creative?
where we need to be creative?
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但其他需要有創意的領域呢？
14:13
So let me take another
typical example: entrepreneurship.
typical example: entrepreneurship.
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讓我舉另一個很典型的例子：
企業家精神。
14:18
Silicon Valley,
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矽谷，
14:20
the land of the youth, right?
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年輕人之地，對吧？
14:22
And indeed, when you look at it,
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的確，當你去看它時，
14:24
you realize that the biggest awards,
the TechCrunch Awards and other awards,
the TechCrunch Awards and other awards,
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你會發現，最大的獎項
TechCrunch 獎及其他獎項
TechCrunch 獎及其他獎項
14:28
are all going to people
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得獎人平均都是
14:31
whose average age
is late 20s, very early 30s.
is late 20s, very early 30s.
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快要三十歲或三十歲初頭的人。
14:36
You look at who the VCs give the money to,
some of the biggest VC firms --
some of the biggest VC firms --
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可以去看創投公司把錢給誰，
有些最大的創投公司——
有些最大的創投公司——
14:42
all people in their early 30s.
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都是三十初頭的人。
當然，我們知道；
14:44
Which, of course, we know;
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14:46
there is this ethos in Silicon Valley
that youth equals success.
that youth equals success.
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在矽谷有一種風氣，
就是年輕等同成功。
就是年輕等同成功。
14:51
Not when you look at the data,
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資料可不是這麼說的。
14:53
because it's not only
about forming a company --
about forming a company --
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因為重點並不只是成立公司——
成立公司就像是產能，
嘗試、嘗試、嘗試——
嘗試、嘗試、嘗試——
14:56
forming a company is like productivity,
trying, trying, trying --
trying, trying, trying --
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14:59
when you look at which
of these individuals actually put out
of these individuals actually put out
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如果你只是去看
這些人當中有誰設立了
這些人當中有誰設立了
15:02
a successful company, a successful exit.
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成功的公司、成功的退場。
15:05
And recently, some of our colleagues
looked at exactly that question.
looked at exactly that question.
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最近，我們的一些同事
就在探究這個問題。
就在探究這個問題。
15:09
And it turns out that yes,
those in the 20s and 30s
those in the 20s and 30s
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結果發現，是的，二、三十歲的人
15:12
put out a huge number of companies,
form lots of companies,
form lots of companies,
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成立了很多公司，
創辦了很多公司，
創辦了很多公司，
15:15
but most of them go bust.
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但大部分都破產收場。
15:18
And when you look at the successful exits,
what you see in this particular plot,
what you see in this particular plot,
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如果去看成功退場的公司，
各位在這張圖上可以看到，
各位在這張圖上可以看到，
15:22
the older you are, the more likely that
you will actually hit the stock market
you will actually hit the stock market
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你的年紀越大，
你就越有可能上市，
你就越有可能上市，
15:26
or the sell the company successfully.
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或者成功把公司賣掉。
15:28
This is so strong, actually,
that if you are in the 50s,
that if you are in the 50s,
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這個機率強到，
如果你是五十幾歲，
如果你是五十幾歲，
15:31
you are twice as likely
to actually have a successful exit
to actually have a successful exit
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你有可能成功退場的機會，
是你三十幾歲時的兩倍。
15:35
than if you are in your 30s.
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15:38
(Applause)
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（掌聲）
15:43
So in the end, what is it
that we see, actually?
that we see, actually?
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所以，最後，這些到底是什麼意思？
15:46
What we see is that creativity has no age.
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我們看到的是，創意不分年齡。
15:50
Productivity does, right?
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產能倒是會有差，對吧？
15:53
Which is telling me that
at the end of the day,
at the end of the day,
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這就是告訴我，到頭來，
15:57
if you keep trying --
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如果你繼續嘗試——
15:59
(Laughter)
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（笑聲）
16:02
you could still succeed
and succeed over and over.
and succeed over and over.
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你仍然有可能成功，且一再成功。
16:05
So my conclusion is very simple:
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所以我的結論非常簡單：
16:08
I am off the stage, back in my lab.
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我要下台，回到我的實驗室了。
16:10
Thank you.
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謝謝。
16:11
(Applause)
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（掌聲）
ABOUT THE SPEAKER
Albert-László Barabási - Network scientistA pioneer in network science, Albert-László Barabási uncovers the hidden order behind complex systems.
Why you should listen
Albert-László Barabási is fascinated by a wide range of topics, from the structure of the brain and treating diseases with network medicine to the emergence of success in art and how science really works. His work uses the quantitative tools of network science, a research field that he pioneered, and led to the discovery of scale-free networks, helping explain the emergence of many natural, technological and social networks.
Barabási is the Robert Gray Dodge Professor of Network Science at Northeastern University and holds an appointment in the Department of Medicine at Harvard Medical School. He splits his time with Budapest, where he runs a European Research Council project at Central European University. A Hungarian born native of Transylvania, Romania, he received his masters in theoretical physics at the Eötvös University in Budapest, Hungary and his PhD three years later at Boston University.
Barabási’s latest book is The Formula: The Universal Laws of Success. He is also the author of Network Science, Linked and Bursts. He co-edited Network Medicine and The Structure and Dynamics of Networks. His books have been translated into over twenty languages.
Albert-László Barabási | Speaker | TED.com