TED2012
Jean-Baptiste Michel: The mathematics of history
让－巴蒂斯特·米歇尔：历史中的数学
Filmed:
Readability: 4.3
1,279,350 views
数学能解释历史么？让－巴蒂斯特·米歇尔在这篇演讲中告诉我们：不仅能，事实上数学可以用来解释很多历史规律。从语言的变迁到战争的伤亡，他向我们展示了数字化存储的历史记录正在逐渐帮助我们深化对历史规律的理解。
Jean-Baptiste Michel - Data researcher
Jean-Baptiste Michel looks at how we can use large volumes of data to better understand our world. Full bio
Jean-Baptiste Michel looks at how we can use large volumes of data to better understand our world. Full bio
Double-click the English transcript below to play the video.
00:15
So it turns out that mathematics is a very powerful language.
0
0
3671
数学是一门很有用的语言。
00:19
It has generated considerable insight in physics,
1
3671
2312
在解释物理、生物以及经济学方面，
00:21
in biology and economics,
2
5983
2100
它拥有与生俱来的洞察力，
00:23
but not that much in the humanities and in history.
3
8083
2817
但对于历史等人文学科解释力稍弱。
00:26
I think there's a belief that it's just impossible,
4
10900
2283
我想有人认为用数学解释人文学科是不可能的，
00:29
that you cannot quantify the doings of mankind,
5
13183
2646
因为你不能量化人类的行为，
00:31
that you cannot measure history.
6
15829
2519
你也不能用数学来度量历史。
00:34
But I don't think that's right.
7
18348
1527
但我并不这样认为，
00:35
I want to show you a couple of examples why.
8
19875
2042
我想向大家展示几个反例。
00:37
So my collaborator Erez and I were considering the following fact:
9
21917
2958
我和我的合作伙伴Erez考虑了以下事实：
00:40
that two kings separated by centuries
10
24875
2729
这个事实就是身处不同时代的君王
00:43
will speak a very different language.
11
27604
1767
会用非常不同的语言交谈。
00:45
That's a powerful historical force.
12
29371
2304
这是一股非常强大的历史作用力。
00:47
So the king of England, Alfred the Great,
13
31675
1773
因此英国国王阿尔弗雷德大帝，
00:49
will use a vocabulary and grammar
14
33448
1640
将会使用一种语法和词汇，
00:50
that is quite different from the king of hip hop, Jay-Z.
15
35088
3700
而这种语法和词汇是完全不同于hip hop之王Jay-Z所使用的。
00:54
(Laughter)
16
38788
1666
（笑声）
00:56
Now it's just the way it is.
17
40454
2171
这就是我想说的事实。
00:58
Language changes over time, and it's a powerful force.
18
42625
2292
语言与时俱进，并且拥有强大的力量。
01:00
So Erez and I wanted to know more about that.
19
44917
2287
因此我和Erez想了解更多有关语言的历史。
01:03
So we paid attention to a particular grammatical rule, past-tense conjugation.
20
47204
3657
我们研究了一个特定的语法规则：过去式。
01:06
So you just add "ed" to a verb at the end to signify the past.
21
50861
3264
人们通过在动词后面加“ed”来表示过去。
01:10
"Today I walk. Yesterday I walked."
22
54125
1927
例如：“Today I walk.”今天我走路，
“Yesterday I walked.”昨天我走路。
“Yesterday I walked.”昨天我走路。
01:11
But some verbs are irregular.
23
56052
1344
但有一些动词的变形是不规则的。
01:13
"Yesterday I thought."
24
57396
1396
例如：“Yesterday I thought.”昨天我思考了。
01:14
Now what's interesting about that
25
58792
1666
这里有趣的现象是：
01:16
is irregular verbs between Alfred and Jay-Z have become more regular.
26
60458
3830
从阿尔弗雷德大帝到Jay-Z时代，不规则动词变的越来越规则了。
01:20
Like the verb "to wed" that you see here has become regular.
27
64288
2735
例如你们这里看到的动词“to wed”就变成了规则动词。
01:22
So Erez and I followed the fate of over 100 irregular verbs
28
67023
4022
因此我和Erez跟踪了100个不规则动词
01:26
through 12 centuries of English language,
29
71045
1919
在12个世纪的英语演化中的命运，
01:28
and we saw that there's actually a very simple mathematical pattern
30
72964
2911
我们发现了一个非常简单的数学规律
01:31
that captures this complex historical change,
31
75875
2542
来捕捉复杂的历史变化，
01:34
namely, if a verb is 100 times more frequent than another,
32
78417
3660
这个规律就是，如果一个动词比另一个动词的使用频率高一百倍，
01:37
it regularizes 10 times slower.
33
82077
2665
这个动词规则化的速度就会慢十倍。
01:40
That's a piece of history, but it comes in a mathematical wrapping.
34
84742
3935
这是历史的一部分，但却以数学的方式得以展现。
01:44
Now in some cases math can even help explain,
35
88677
3654
在某些情况下，数学甚至可以帮助解释，
01:48
or propose explanations for, historical forces.
36
92331
2879
或者对历史的推动力加以解释。
01:51
So here Steve Pinker and I
37
95210
1832
我和Steve Pinker
01:52
were considering the magnitude of wars during the last two centuries.
38
97042
3852
考虑了过去两个世纪中战争的激烈程度。
01:56
There's actually a well-known regularity to them
39
100894
2495
事实上有一条广为人知的规律：
01:59
where the number of wars that are 100 times deadlier
40
103389
3422
造成一百倍伤亡人数的战争数目
02:02
is 10 times smaller.
41
106811
1952
只占更少伤亡人数的战争数目的十分之一。
02:04
So there are 30 wars that are about as deadly as the Six Days War,
42
108763
3344
有30场战争的伤亡人数与“六日战争”（第三次阿以战争）的几乎持平，
02:08
but there's only four wars that are 100 times deadlier --
43
112107
2820
但只有4场战争比“六日战争”的伤亡人数高出一百倍，
02:10
like World War I.
44
114927
1977
例如第一次世界大战。
02:12
So what kind of historical mechanism can produce that?
45
116904
2923
那么这背后的历史原因是什么呢？
02:15
What's the origin of this?
46
119827
2000
什么是这条规律的原因呢？
02:17
So Steve and I, through mathematical analysis,
47
121827
2265
我和Steve通过数学分析，
02:19
propose that there's actually a very simple phenomenon at the root of this,
48
124092
4241
认为这是由一个非常简单的现象导致的，
02:24
which lies in our brains.
49
128333
1690
而这个现象植根于我们的意识之中。
02:25
This is a very well-known feature
50
130023
2019
这是一个众所周知的特征，
02:27
in which we perceive quantities in relative ways --
51
132042
2975
人们对数量的感觉是相对的，
02:30
quantities like the intensity of light or the loudness of a sound.
52
135017
3716
诸如光源的强弱或者声音的大小。
02:34
For instance, committing 10,000 soldiers to the next battle sounds like a lot.
53
138733
5309
下面有个例子，派遣10000名士兵去参加下一场战斗乍一听是很多人。
02:39
It's relatively enormous if you've already committed 1,000 soldiers previously.
54
144042
3444
特别是如果你之前只派遣了1000名士兵去参战的话，这个数字就更显得庞大了。
02:43
But it doesn't sound so much,
55
147486
1827
但同样的数字也会显得不那么多，
02:45
it's not relatively enough, it won't make a difference
56
149313
3020
当它还不足以造成巨大的差别的时候，
02:48
if you've already committed 100,000 soldiers previously.
57
152333
2952
假如你之前已经派遣了100000名士兵参加战斗。
02:51
So you see that because of the way we perceive quantities,
58
155285
3613
因此你们会看到由于我们对数字的感觉是相对的，
02:54
as the war drags on,
59
158898
1767
随着战争的延续，
02:56
the number of soldiers committed to it and the casualties
60
160665
3085
参战士兵的数量及伤亡人数
02:59
will increase not linearly --
61
163750
1683
将不会线性地增加，
03:01
like 10,000, 11,000, 12,000 --
62
165433
1888
例如从10000到11000再到12000，
03:03
but exponentially -- 10,000, later 20,000, later 40,000.
63
167321
4275
而是呈指数增加的——起先是10000，之后是20000，再后来就成了40000。
03:07
And so that explains this pattern that we've seen before.
64
171596
3085
而这正好解释了我们之前看到的规律。
03:10
So here mathematics is able to link a well-known feature of the individual mind
65
174681
5498
这里数学能够将有关个体认知的常识
03:16
with a long-term historical pattern
66
180179
2989
和长期的历史规律联系起来。
03:19
that unfolds over centuries and across continents.
67
183168
2857
这种历史规律可以延续数个世纪并在各个大洲有效。
03:21
So these types of examples, today there are just a few of them,
68
186025
4017
今天我们只有有限的几个诸如此类的例子，
03:25
but I think in the next decade they will become commonplace.
69
190042
2689
但我认为在未来的十年间它们（用数学解释历史规律）会越来越多。
03:28
The reason for that is that the historical record
70
192731
2392
而其原因则是历史记录
03:31
is becoming digitized at a very fast pace.
71
195123
2460
正以非常快的速度被数字化存储。
03:33
So there's about 130 million books
72
197583
2610
自人类的文明史发端至今
03:36
that have been written since the dawn of time.
73
200193
2311
我们大约拥有了1.3亿本不同的书。
03:38
Companies like Google have digitized many of them --
74
202504
2454
而像谷歌这样的公司正致力于将其中很多书数字化存储
03:40
above 20 million actually.
75
204958
1584
事实上被数字化的数量已经超过了2千万本。
03:42
And when the stuff of history is available in digital form,
76
206542
3578
而当历史记录能以数字格式被存取的时候，
03:46
it makes it possible for a mathematical analysis
77
210120
2380
回顾人类历史文化规律
03:48
to very quickly and very conveniently
78
212500
2375
的相关数学分析
03:50
review trends in our history and our culture.
79
214875
2725
会变得快捷而且方便。
03:53
So I think in the next decade,
80
217600
2721
因此我认为在接下来的十年，
03:56
the sciences and the humanities will come closer together
81
220321
2750
自然科学和人文科学会更加紧密
03:58
to be able to answer deep questions about mankind.
82
223071
3329
并能够解释有关人类的深层次问题。
04:02
And I think that mathematics will be a very powerful language to do that.
83
226400
4121
我认为数学将会成为进行这项工作的有力工具。
04:06
It will be able to reveal new trends in our history,
84
230521
3146
数学会帮助我们发现人类历史中的新规律，
04:09
sometimes to explain them,
85
233667
1750
有时也许可以解释某些规律，
04:11
and maybe even in the future to predict what's going to happen.
86
235417
3306
甚至在未来我们可以利用数学来预测将要发生的事情。
04:14
Thank you very much.
87
238723
1491
非常感谢大家。
04:16
(Applause)
88
240214
3678
（掌声）
ABOUT THE SPEAKER
Jean-Baptiste Michel - Data researcherJean-Baptiste Michel looks at how we can use large volumes of data to better understand our world.
Why you should listen
Jean-Baptiste Michel holds joint academic appointments at Harvard (FQEB Fellow) and Google (Visiting Faculty). His research focusses on using large volumes of data as tools that help better understand the world around us -- from the way diseases progress in patients over years, to the way cultures change in human societies over centuries. With his colleague Erez Lieberman Aiden, Jean-Baptiste is a Founding Director of Harvard's Cultural Observatory, where their research team pioneers the use of quantitative methods for the study of human culture, language and history. His research was featured on the covers of Science and Nature, on the front pages of the New York Times and the Boston Globe, in The Economist, Wired and many other venues. The online tool he helped create -- ngrams.googlelabs.com -- was used millions of times to browse cultural trends. Jean-Baptiste is an Engineer from Ecole Polytechnique (Paris), and holds an MS in Applied Mathematics and a PhD in Systems Biology from Harvard.
Jean-Baptiste Michel | Speaker | TED.com