The Q&A: Samuel Arbesman
Nov 28th 2012, 13:56 by R.D.A. | LOS ANGELES
IN PRIMARY school
Babbage learned that there were nine planets in the solar system. None
were known to exist outside it. Since then, astronomers have spotted
over 800 planets around other stars (and thousands more "candidates")
and demoted Pluto to a mere "dwarf planet". Even a cursory glance at
other fields reveals similar patterns.
Samuel Arbesman, a
mathematician at Harvard, calls this "The Half-life of Facts", the title
of his new book. In it he explains that this churn of knowledge is like
radioactive decay: you cannot predict which individual fact is going to
succumb to it, but you can know how long it takes for half the facts in
a discipline to become obsolete. Such quantitative analysis of science
has become known as scientometrics. We talked to Dr Arbesman about how
knowledge changes over time, and what this means for the way people
consume information.
What is scientometrics?
Put simply,
scientometrics is the science of science. It grew out of bibliometrics,
the science of books and research papers. In bibliometrics the unit of
measurement is a research paper, which are easy to study because you can
quantify different aspects of it: who the authors are, who has
co-authored papers with those authors, how often a paper is cited, by
whom, and so on.
Librarians were some
of the first people to do this. In the 1970s people started looking
around and noticing that scientific knowledge was growing very rapidly,
but papers had not been digitised yet, and libraries were finite in size
and had finite resources. And so librarians had to grapple with the
question what to carry on their shelves. They had to calculate which
fields get overturned really rapidly, in other words, which papers and
books people were unlikely to care about in the future.
But bibliometrics is
only one subfield of scientometrics. There are all kinds of ways that
you can quantify science: you can measure the number of discoveries that
are occurring within a particular field, the number of elements in the
periodic table, etc. Broadly, scientometrics is about quantifying and
understanding how science occurs.
That includes both
the social aspects of science and the relationship between science and
technology. There is a tight interplay between the capacities of our
tools and what we can actually discover. Technology is crucial to the
story of science. Science of science is about all these different
things. And my book is about how the facts of the world—the stuff we
know—grow in number, and how they change.
What does it mean to say that a fact has a half-life?
When I say that a
fact has a half-life, I am trying to illustrate how knowledge changes by
making an analogy to radioactivity. With radioactivity, if you give me a
single atom of uranium, I can tell you it will eventually decay. When
it does, it will break down into specific bits and release a certain
amount of energy. But I have no way of telling when it is going to
decay. It could be in the next half-second or not for millions and
millions of years.
But things change
when you go from a single atom to lots of atoms. When you have a big
chunk of uranium, you can graph out the decay; you can say it takes 4.47
billion years for half of the atoms in a chunk of uranium to break
down. You aren't going to know which half, but you know the overall rate
of the decay. And the same thing is true for science, and for knowledge
in general. Even though I cannot predict what discovery is going to be
made or what fact is going to be overturned, there are regularities in
how knowledge grows and changes over time.
For example, in the
area of medical science dealing with hepatitis and cirrhosis, two liver
diseases, researchers actually measured how long it takes for half of
the knowledge in these fields to be overturned. They gave a whole bunch
of research papers from fifty years ago to a panel of experts and asked
them which were still regarded as true and which had been refuted or no
longer considered interesting. They plotted this on a graph. What they
found is that there is a nice, smooth rate of decay; you can predict
that every 45 years, half of this particular sort of knowledge gets
outdated.
You can use these
same methods with citations in newer papers. There, you look to see how
long papers are cited in a field and then derive a half-life based on
how long it takes for papers to receive half the citations they used to
receive. Of course, some papers are no longer cited precisely because
they are so influential. No one is citing Newton's Principia even though
we still use a lot of his ideas. But by and large, the citation rate of
papers is a good proxy for the half-life of knowledge.
What scientific fields decay the slowest—or the fastest—and what drives that difference?
Well it depends,
because these rates tend to change over time. For example, when medicine
transitioned from an art to a science, its half-life was much more
rapid than it is now. That said, medicine still has a very short
half-life; in fact it is one of the areas where knowledge changes the
fastest. One of the slowest is mathematics, because when you prove
something in mathematics it is pretty much a settled matter unless
someone finds an error in one of your proofs.
One thing we have
seen is that the social sciences have a much faster rate of decay than
the physical sciences, because in the social sciences there is a lot
more "noise" at the experimental level. For instance, in physics, if you
want to understand the arc of a parabola, you shoot a cannon 100 times
and see where the cannonballs land. And when you do that, you are likely
to find a really nice cluster around a single location. But if you are
making measurements that have to do with people, things are a lot
messier, because people respond to a lot of different things, and that
means the effect sizes are going to be smaller.
What is a "fact phase transition" and how does it make events like the first Moon landing predictable?
First, here is what I
mean by a phase transition. An example in the natural world is when
water goes from liquid to ice when it freezes. For most people that is
pretty unremarkable. But it is actually really interesting when you look
at it from a physics perspective. A continuous change—in this case, a
change in temperature—is accompanied by a step-change is other
properties: water going from being a liquid to a crystal. This is a good
way to think of rapid changes in knowledge.
Some of these happen
rapidly, but underneath there are these gradual changes. For example,
with the moon landing was a pretty big change in human knowledge and
human accomplishment. For all of human history, no one had ever set foot
on the moon, and then one day in 1969 people had. But if you look
carefully you will see that the moon landing was completely predictable.
Look at the fastest speeds enabled by technology, for instance, and it
turns out that they follow a regular curve. In the 1950s the American
air force graphed this out and determined that if transportation speeds
continued rising at the rate they were going, humans should be able to
get into orbit, and then eventually land on the Moon, within a set
number of years. And, sure enough, right on schedule, Sputnik happened,
and a decade later humans landed on the moon. That was a fact phase
transition, an abrupt change with slow incremental processes hiding
beneath the surface.
One theoretical fact phase transition that you describe is "actuarial escape velocity", a concept borrowed from medical science.
Actuarial escape
velocity is the idea that at some point average human lifetime will grow
by more than a year each year. Right now the rate is only a fraction of
a year (thanks to changes in medical science and hygiene) a year. If it
exceeds one year per year, people will effectively live for ever,
without having to solve the immortality problem. The reason I bring it
up in my book is to illustrate that small changes in science can
actually bring about big changes in other areas of knowledge, or
elsewhere in the world.
For example, if an
astronomer finds another planet outside solar system, unless it has
certain properties, it will just be another piece of data. It is not
going to alter the structure of people's ideas about planets. But if he
discovers a planet that can harbour life, that is a game-changer. And
actuarial escape velocity is similar, in the sense that these
incremental changes in medical science and hygiene can eventually create
a huge change in how we live our lives.
In your book, you
make a convincing case that scientific breakthroughs are becoming more
difficult to achieve with time. One gets the sense that the low-hanging
fruit of empiricism have been picked. But you also argue that science as
a human activity is growing, and getting better. How is that?
In some fields
science is getting harder, but I would not say that science as a whole
is becoming more difficult. We are still adding new scientists every
year, but the rate of growth has slowed and science is increasingly
being done by large teams. But there are many areas where we thought
there is nothing left to explore, only for someone to come along and say
that there is something there, after all.
In mathematics there
was an extreme case of this in the 1990s, when two high-school students
figured out a new way to prove one of Euclid's theorems, something that
had not been done in a thousand years. So even though basic geometric
proofs are not the frontier of mathematics, there are still things you
can do. And even where things slow down in science, often that slowing
forces scientists to be cleverer, both in finding ways to create new
knowledge but also in finding new ways to combine disciplines. Plus
nowadays new technology is a real driving force; the new computational
tools have created the potential for a scientific revolution.
Reading your book it
is difficult not to think about consilience, the term that Edward Wilson
uses to describe an idealised unity of all scientific knowledge. Do you
think scientometrics can get us to something like consilience faster
than if science were merely left to its own devices?
There is a great deal
of power in the idea of consilience, and in synthesising ideas. When it
comes to understanding the march of knowledge, scientometrics can be
very helpful. I don't think it is necessarily going to help us realise
the complete synthesis of all knowledge, but if we have a better sense
of how we know what we know, and how what we know changes, that will
force a reckoning in how we think about how knowledge as a whole is
organised. If you create a networked view of different scientific
fields, you quickly realize how connected they are. There are
surprisingly few steps from thinking about abstract mathematics to
thinking about models of how population size changes in an ecosystem. As
science grows and becomes more and more complicated, having people that
can exist in these liminal spaces is going to be increasingly
important.
It seems that one of
your purposes in writing this book is to call attention to the human
habit of becoming accustomed to whatever state of affairs is true when a
situation is initially examined. By showing how knowledge about the
world shifts systematically, you seem to be suggesting a renewed
vigilance against growing complacency about knowledge of the world.
That is certainly one
of my arguments. I want to show people how knowledge changes. But at
the same time I want to say, now that you know how knowledge changes,
you have to be on guard, so you are not shocked when your children
coming home to tell you that dinosaurs have feathers. You have to look
things up more often and recognise that most of the stuff you learned
when you were younger is not at the cutting edge. We are coming a lot
closer to a true understanding of the world; we know a lot more about
the universe than we did even just a few decades ago. It is not the case
that just because knowledge is constantly being overturned we do not
know anything. But too often, we fail to acknowledge change.
Some fields are
starting to recognise this. Medicine, for example, has got really good
at encouraging its practitioners to stay current. A lot of medical
students are taught that everything they learn is going to be obsolete
soon after they graduate. There is even a website called "up to date"
that constantly updates medical textbooks. In that sense we could all
stand to learn from medicine; we constantly have to make an effort to
explore the world anew—even if that means just looking at Wikipedia more
often. And I am not just talking about dinosaurs and outer space. You
see this same phenomenon with knowledge about nutrition or childcare—the
stuff that has to do with how we live our lives.
Samuel Arbesman, a
mathematician at Harvard, calls this "The Half-life of Facts", the title
of his new book. In it he explains that this churn of knowledge is like
radioactive decay: you cannot predict which individual fact is going to
succumb to it, but you can know how long it takes for half the facts in
a discipline to become obsolete.
Put simply,
scientometrics is the science of science. It grew out of bibliometrics,
the science of books and research papers. In bibliometrics the unit of
measurement is a research paper, which are easy to study because you can
quantify different aspects of it: who the authors are, who has
co-authored papers with those authors, how often a paper is cited, by
whom, and so on.
You can use these
same methods with citations in newer papers. There, you look to see how
long papers are cited in a field and then derive a half-life based on
how long it takes for papers to receive half the citations they used to
receive.
專(zhuān)訪(fǎng)：薩繆爾 阿布斯曼
筆者在上小學(xué)時(shí)知道了太陽(yáng)系
有九大行星。當(dāng)時(shí)人們還沒(méi)有找到太陽(yáng)系之外的任何行星?，F(xiàn)在，天文學(xué)家已經(jīng)找到超過(guò)800顆圍繞其它恒星公轉(zhuǎn)的行星（此外可能是行星的“候選”天體數(shù)量更
是上千），而同時(shí)冥王星卻已被降級(jí)為一顆“矮行星”。即使粗略檢視其他領(lǐng)域你也會(huì)發(fā)現(xiàn)類(lèi)似的情況。
哈佛大學(xué)數(shù)學(xué)家薩繆爾·阿布斯曼
（Samuel
Arbesman）給這種現(xiàn)象取名叫“知識(shí)的半衰期”，并寫(xiě)了一本以此為題的書(shū)。在書(shū)中他解釋說(shuō)知識(shí)的新舊交替如同放射性衰變，你無(wú)法預(yù)知某項(xiàng)特定知識(shí)是
否會(huì)受這種衰變效果影響而被淘汰，但你能夠找出某一領(lǐng)域的知識(shí)在多久之后會(huì)有一半被淘汰。這種對(duì)科學(xué)的定量分析被稱(chēng)之為科學(xué)計(jì)量學(xué)。我們和阿布斯曼博士就
知識(shí)如何隨時(shí)間改變、以及這對(duì)人們使用信息的方式有何影響進(jìn)行了一次專(zhuān)訪(fǎng)。
什么是科學(xué)計(jì)量學(xué)？
簡(jiǎn)單來(lái)說(shuō)，科學(xué)計(jì)量學(xué)就是研究科學(xué)的
科學(xué)。這是從研究書(shū)籍和論文的科學(xué)——文獻(xiàn)計(jì)量學(xué)中引申出來(lái)的。文獻(xiàn)計(jì)量學(xué)使用的測(cè)量單位是論文。論文是比較容易研究的課題，因?yàn)槟憧梢粤炕撐牡母鞣N特
點(diǎn)：作者是誰(shuí)、誰(shuí)曾經(jīng)和該論文的作者合作發(fā)表過(guò)論文、該論文被引用的頻率、被誰(shuí)引用等等。
圖書(shū)館員是最早進(jìn)行這類(lèi)研究的人士之
一。20世紀(jì)70年代人們發(fā)現(xiàn)身邊的科學(xué)知識(shí)增長(zhǎng)速度非?？?，但當(dāng)時(shí)論文還沒(méi)有數(shù)字化，圖書(shū)館的規(guī)模和資源都有限。因此圖書(shū)館員必須處理該讓什么書(shū)上架這
一問(wèn)題。換句話(huà)說(shuō)，他們必須計(jì)算哪些領(lǐng)域的知識(shí)更新速度非常快，哪些書(shū)籍和論文會(huì)在未來(lái)變得無(wú)關(guān)緊要。
但文獻(xiàn)計(jì)量學(xué)只是科學(xué)計(jì)量學(xué)的一個(gè)子領(lǐng)域。要量化科學(xué)有各種方式：你可以測(cè)量某個(gè)特定領(lǐng)域內(nèi)新發(fā)現(xiàn)的數(shù)量，元素周期表內(nèi)元素的數(shù)量等等。大體來(lái)說(shuō)，科學(xué)計(jì)量學(xué)研究的是如何量化并理解科學(xué)成果產(chǎn)生的方式。
這包括科學(xué)的社會(huì)面以及科學(xué)和技術(shù)之
間的關(guān)系。我們使用的工具的能力和我們能發(fā)現(xiàn)的成果之間有密切的相互依賴(lài)關(guān)系。對(duì)于科學(xué)發(fā)現(xiàn)來(lái)說(shuō)技術(shù)是至關(guān)重要的。研究科學(xué)的科學(xué)致力于研究所有這些不同
的方面。我的書(shū)討論的是世界上的知識(shí)——我們所知的事物——如何增長(zhǎng)、如何改變。
知識(shí)有半衰期是什么意思？
我說(shuō)知識(shí)有半衰期是為了用放射性現(xiàn)象
的比喻來(lái)描述知識(shí)的變化。就放射性現(xiàn)象來(lái)說(shuō)，你如果給我一個(gè)鈾原子，我可以告訴你它最終會(huì)衰變。一旦衰變發(fā)生，它會(huì)分解為特定的產(chǎn)物，并釋放一定的能量。
但是我不可能告訴你它什么時(shí)候會(huì)衰變?？赡茉龠^(guò)半秒它就衰變了，也可能要等幾萬(wàn)億年。
但是如果你給我的不是一個(gè)原子，而是
大量原子，情況就不同了。當(dāng)你有一大塊鈾時(shí)，你可以繪出衰變曲線(xiàn)。你可以確定這些鈾中有一半原子會(huì)在44.7億年的時(shí)間里衰變。你不知道具體哪一半會(huì)衰
變，但你可以確定總體的衰變率。對(duì)于科學(xué)、以及更廣義的知識(shí)來(lái)說(shuō)也是一樣。雖然我無(wú)法預(yù)測(cè)會(huì)出現(xiàn)什么樣的新發(fā)現(xiàn)，或是有哪些知識(shí)會(huì)被推翻，知識(shí)隨時(shí)間增長(zhǎng)
并變化的規(guī)律是有跡可循的。
例如，在研究肝炎和肝硬化的醫(yī)學(xué)方
面，研究者對(duì)過(guò)多久這些領(lǐng)域內(nèi)的一半知識(shí)會(huì)被推翻進(jìn)行了研究。他們將50年前的一批論文交給一個(gè)專(zhuān)家評(píng)審團(tuán)，判斷其中哪些的結(jié)果如今已被推翻，或是已經(jīng)不
再重要。他們將結(jié)果繪成一副圖表，并在圖中看到非常清楚平滑的衰變曲線(xiàn)。你可以用該圖預(yù)測(cè)每過(guò)45年，該領(lǐng)域的知識(shí)都會(huì)有一半變得過(guò)時(shí)。
把同一方法用在最近論文的引用上，你
可以研究某一領(lǐng)域內(nèi)的論文在多長(zhǎng)時(shí)間內(nèi)會(huì)被別的論文引用，然后你可以根據(jù)論文發(fā)表后多少時(shí)間內(nèi)其引用次數(shù)會(huì)減少到其最初引用次數(shù)的一半來(lái)推算出半衰期。當(dāng)
然，有的論文不再被引用恰恰是因?yàn)樗鼈冇绊懥O大?，F(xiàn)在沒(méi)人會(huì)在論文內(nèi)注出對(duì)牛頓《原理》的引用，但我們還在繼續(xù)使用很多他的思想。但除了這些特例，基本
上論文引用率還是很適合代表知識(shí)半衰期的。
哪些科學(xué)領(lǐng)域的“衰變”最慢？哪些最快？它們之間為什么會(huì)有這樣的差異？
這是沒(méi)有標(biāo)準(zhǔn)答案的，因?yàn)樗プ兟时旧?
會(huì)隨時(shí)間推移而改變。例如，當(dāng)醫(yī)學(xué)最早從一門(mén)藝術(shù)變成一門(mén)科學(xué)時(shí)，其半衰期比現(xiàn)在要迅速多了。盡管如此，醫(yī)學(xué)仍然是半衰期非常短的一門(mén)科學(xué)，實(shí)際上它是知
識(shí)變化最快的領(lǐng)域之一。知識(shí)變化最慢的領(lǐng)域之一是數(shù)學(xué)，因?yàn)樵跀?shù)學(xué)里一旦證明某一定理很多時(shí)候結(jié)果就等于是敲定了，除非有人發(fā)現(xiàn)你證明過(guò)程中的錯(cuò)誤其一般
不會(huì)被推翻。
我們觀(guān)察到的一個(gè)現(xiàn)象就是社會(huì)科學(xué)的
衰變率比自然科學(xué)要快得多，因?yàn)樯鐣?huì)科學(xué)在實(shí)驗(yàn)水平上存在的“噪音”要大得多。例如，在物理學(xué)里，如果你想要研究拋物線(xiàn)軌跡，你可以發(fā)射大炮100次，看
看炮彈會(huì)落在哪里。你會(huì)發(fā)現(xiàn)炮彈大多非常漂亮地集中在某個(gè)點(diǎn)周?chē)５侨绻銣y(cè)量的對(duì)象是人，那問(wèn)題就多了，因?yàn)槿藢?duì)大量不同的外界事物會(huì)有反應(yīng)，換句話(huà)
說(shuō)，效應(yīng)大小會(huì)小得多。
什么是“知識(shí)相變”？它如何讓第一次登月這樣的事件變得可預(yù)知？
首先，我解釋一下相變這個(gè)概念。自然
界的一個(gè)例子就是水凝固時(shí)從液態(tài)變?yōu)楣虘B(tài)。對(duì)大多數(shù)人來(lái)說(shuō)這一現(xiàn)象平淡無(wú)奇，但是從物理學(xué)的角度來(lái)看你會(huì)發(fā)現(xiàn)這其實(shí)是一個(gè)很有趣的現(xiàn)象。某一屬性的連續(xù)轉(zhuǎn)
變（在這個(gè)例子里是溫度改變）會(huì)伴隨著其它屬性質(zhì)的飛躍：水從液態(tài)變?yōu)榫w固態(tài)了。這可以用來(lái)幫助我們思考知識(shí)的迅速改變。
有些改變是迅速發(fā)生的，但在突變之下
隱藏有上文所說(shuō)的漸變。例如，登月對(duì)于人類(lèi)知識(shí)和成就來(lái)說(shuō)是一個(gè)巨大的飛躍。在人類(lèi)歷史上，從未有人踏足過(guò)月亮表面，1969年的某一天某人突然就做到
了。但如果仔細(xì)分析，你會(huì)發(fā)現(xiàn)登月完全是可預(yù)見(jiàn)的。例如，你如果看看依靠科技所能達(dá)到的最高移動(dòng)時(shí)速，它基本上沿著一條正常曲線(xiàn)一路升高。20世紀(jì)50年
代時(shí)，美國(guó)空軍把該數(shù)據(jù)繪成曲線(xiàn)圖，發(fā)現(xiàn)如果移動(dòng)速度照這個(gè)趨勢(shì)繼續(xù)升高，那么人類(lèi)會(huì)在若干年內(nèi)進(jìn)入繞地軌道，并最終登陸月球。事實(shí)和他們預(yù)測(cè)的一樣，斯
普特尼克準(zhǔn)時(shí)登空。之后又過(guò)了十年，人類(lèi)就登上了月球。這就是一個(gè)知識(shí)相變，受到隱藏在表面之下漸變的過(guò)程推動(dòng)而發(fā)生的突變。
你提到的一個(gè)理論上的知識(shí)相變：“死亡逃逸速度”，這是來(lái)自醫(yī)學(xué)的一個(gè)概念。
死亡逃逸速度是指最終人類(lèi)壽命每年會(huì)
增加超過(guò)一年這一觀(guān)點(diǎn)?，F(xiàn)在人類(lèi)壽命每年增長(zhǎng)的程度（多虧了醫(yī)學(xué)和衛(wèi)生）還只是一年的一小部分。如果這個(gè)增加速度達(dá)到每年增長(zhǎng)超過(guò)一年，那人們實(shí)際上可以
不需要解決不死問(wèn)題，就能實(shí)現(xiàn)永生。我在書(shū)中提到這一點(diǎn)是為了描述科學(xué)中的細(xì)微變化可以在其它知識(shí)領(lǐng)域、或世界其它地方產(chǎn)生巨大的實(shí)際改變。
例如，如果天文學(xué)家發(fā)現(xiàn)太陽(yáng)系外的一
顆行星，除非它有某些特殊性質(zhì)，不然這僅僅是多一份數(shù)據(jù)。發(fā)現(xiàn)新的行星不會(huì)改變?nèi)祟?lèi)對(duì)行星的理解。但如果天文學(xué)家發(fā)現(xiàn)的行星上有生命，那就完全不同了。死
亡逃逸速度也是如此，醫(yī)學(xué)和衛(wèi)生領(lǐng)域內(nèi)的漸進(jìn)改變最終可以導(dǎo)致我們生活方式的巨大改變。
在你的書(shū)中，你令人信服地論證了隨著時(shí)間推移會(huì)越來(lái)越難做出科學(xué)上的突破。讀者會(huì)了解到易摘的經(jīng)驗(yàn)主義果實(shí)都已被采光了。但你也論證了科學(xué)作為一項(xiàng)人類(lèi)活動(dòng)正在增長(zhǎng)，而且蒸蒸日上。這怎么說(shuō)？
在某些領(lǐng)域科學(xué)研究確實(shí)越來(lái)越艱難，
但我不認(rèn)為科學(xué)研究整體正在變得更為艱難。我們每年都有新的科學(xué)家加入研究隊(duì)伍，但科學(xué)研究的增長(zhǎng)率確實(shí)在放緩，而越來(lái)越多的研究要依賴(lài)大型團(tuán)隊(duì)。但是有
很多領(lǐng)域我們?cè)詾闆](méi)什么可以探索了，不料卻有人在該領(lǐng)域仍然發(fā)現(xiàn)新的事物。
在數(shù)學(xué)里就有一個(gè)很極端的例子發(fā)生在
20世紀(jì)90年代，當(dāng)時(shí)兩位高中生發(fā)現(xiàn)了一種新的方式來(lái)證明歐幾里德的一條定理，從一千年前起就已沒(méi)人找出過(guò)歐幾里德定理的新證明方法了。因此雖然基礎(chǔ)幾
何學(xué)如今已不是數(shù)學(xué)的前沿，還是有新發(fā)現(xiàn)等待人們?nèi)グl(fā)掘。即使科學(xué)發(fā)現(xiàn)的速度放緩了，這種放緩現(xiàn)象常常會(huì)讓科學(xué)家變得更聰明，因?yàn)樗仁箍茖W(xué)家設(shè)法創(chuàng)造新
知識(shí)并尋找新方式來(lái)綜合各個(gè)領(lǐng)域。此外如今新技術(shù)是真正的推動(dòng)力，新的計(jì)算工具醞釀著新的科學(xué)革命。
你的書(shū)很容易讓人聯(lián)想到“知識(shí)融通”，愛(ài)德華·威爾遜（Edward Wilson）用這個(gè)詞來(lái)形容所有科學(xué)知識(shí)的理想化統(tǒng)一。你認(rèn)為和任科學(xué)自己發(fā)展相比，科學(xué)計(jì)量學(xué)的出現(xiàn)可以讓我們比更快達(dá)到這一境界嗎？
“知識(shí)融通”以及綜合思想是非常強(qiáng)大
的概念。在理解知識(shí)的累積增進(jìn)時(shí)，科學(xué)計(jì)量學(xué)可以幫助很大。我不認(rèn)為科學(xué)計(jì)量學(xué)一定會(huì)對(duì)實(shí)現(xiàn)知識(shí)的完全綜合有所貢獻(xiàn)，但是如果我們能更好理解我們是如何掌
握現(xiàn)有知識(shí)的，以及我們擁有的知識(shí)是如何隨時(shí)間推移改變的，這將會(huì)迫使我們思考知識(shí)整體是如何組織的。如果你將不同的科學(xué)領(lǐng)域之間的聯(lián)系看成一個(gè)網(wǎng)絡(luò)，你
會(huì)立刻意識(shí)到不同領(lǐng)域的相互聯(lián)接非常發(fā)達(dá)。從思考抽象的數(shù)學(xué)問(wèn)題到思考生態(tài)系統(tǒng)內(nèi)的種群數(shù)量變化模型之間其實(shí)只需要沒(méi)幾個(gè)過(guò)渡步驟，這是很讓人驚奇的。隨
著科學(xué)逐漸進(jìn)步，變得越來(lái)越復(fù)雜，能夠身處這樣的模糊臨界領(lǐng)域思考問(wèn)題的人將來(lái)會(huì)變得越來(lái)越重要。
你寫(xiě)這本書(shū)的其中一個(gè)目的好像是為了讓人們注意到自己的一個(gè)習(xí)慣：即人類(lèi)會(huì)逐漸把最早研究某一事物時(shí)得到的觀(guān)察視為理所當(dāng)然。你的書(shū)告訴人們知識(shí)是如何系統(tǒng)化地改變，其中似乎蘊(yùn)涵讓人們重新自省，防止在對(duì)知識(shí)的認(rèn)識(shí)上過(guò)于傲慢的意思？
當(dāng)然，這是我的論點(diǎn)之一。我希望讓人
們看見(jiàn)知識(shí)是如何改變的。但是同時(shí)我也想指出一旦你知道知識(shí)是會(huì)改變的，你必須要常常警醒，這樣才不會(huì)在你的孩子放學(xué)回家告訴你恐龍有羽毛時(shí)大吃一驚。你
必須經(jīng)常查閱資料，意識(shí)到自己年輕時(shí)學(xué)到的很多知識(shí)并不是最先進(jìn)的。我們已經(jīng)大幅接近對(duì)世界的真實(shí)理解了，我們對(duì)宇宙的認(rèn)識(shí)比僅僅幾十年前已經(jīng)多得多了。
知識(shí)在不停得更新并不意味著我們無(wú)知。但是很多時(shí)候，我們確實(shí)沒(méi)有意識(shí)到這種改變。
某些領(lǐng)域開(kāi)始意識(shí)到這一點(diǎn)。例如，醫(yī)學(xué)在鼓勵(lì)從業(yè)者時(shí)時(shí)保持學(xué)習(xí)最新知識(shí)這一點(diǎn)上做得很好。很多醫(yī)學(xué)院學(xué)生會(huì)被告知他們所學(xué)的一切在畢業(yè)后不久就會(huì)過(guò)時(shí)。甚至有一個(gè)名叫“最新信息”的網(wǎng)站在
不停地更新醫(yī)學(xué)教科書(shū)。在一定程度上各個(gè)領(lǐng)域都可以學(xué)習(xí)醫(yī)學(xué)，我們必須不停地努力來(lái)重新探索世界，就算這僅僅體現(xiàn)在更頻繁地查維基百科也好。而且我所講的不僅只是關(guān)于恐龍和外太空。你在營(yíng)養(yǎng)和育兒這些和我們生活息息相關(guān)的領(lǐng)域也會(huì)看到同樣的知識(shí)更新現(xiàn)象。
IN PRIMARY school
Babbage learned that there were nine planets in the solar system. None
were known to exist outside it.筆者在上小學(xué)時(shí)知道了太陽(yáng)系有九大行星。當(dāng)時(shí)人們還沒(méi)有找到它們之外的任何行星。
評(píng)論：for reader’s benefit: exist outside it = exist outside the solar system.
哈佛大學(xué)數(shù)學(xué)家薩繆爾?阿布斯曼
（Samuel
Arbesman）給這種現(xiàn)象取名叫“知識(shí)的半衰期”，并寫(xiě)了一本以此為題的書(shū)。在書(shū)中他解釋說(shuō)知識(shí)的新舊交替如同放射性衰變，你無(wú)法預(yù)知某項(xiàng)特定知識(shí)是
否會(huì)被淘汰，但你能夠找出某一領(lǐng)域的知識(shí)在多久之后會(huì)有一半被淘汰
評(píng)論：for reader’s benefit: succumb to it = succumb to this churn of knowledge.
簡(jiǎn)單來(lái)說(shuō)，科學(xué)計(jì)量學(xué)就是研究科學(xué)的
科學(xué)。這是從研究書(shū)籍和論文的科學(xué)——文獻(xiàn)計(jì)量學(xué)中引申出來(lái)的。文獻(xiàn)計(jì)量學(xué)使用的測(cè)量單位是論文。論文是比較容易研究的課題，因?yàn)槟憧梢粤炕撐牡母鞣N特
點(diǎn)：作者是誰(shuí)、誰(shuí)曾經(jīng)和該論文的作者合作發(fā)表過(guò)論文、該論文被引用的頻率、被誰(shuí)引用等等。
評(píng)論：which are easy to study 顯然不對(duì)， 或不好， 應(yīng)當(dāng)是 which (=a research paper) is easy to study
把同一方法用在最近論文的引用上，你可以研究某一領(lǐng)域內(nèi)的論文在多長(zhǎng)時(shí)間內(nèi)會(huì)被別的論文引用，然后你可以根據(jù)論文發(fā)表后多少時(shí)間內(nèi)其引用次數(shù)達(dá)到一般引用次數(shù)的一半來(lái)推算出半衰期。
評(píng)論：把同一方法用在最近論文所引用的論文上，你可以研究某一領(lǐng)域內(nèi)的論文在多長(zhǎng)時(shí)間內(nèi)會(huì)被別的論文引用，然后你可以根據(jù)論文發(fā)表后多少時(shí)間內(nèi)其引用次數(shù)減少到當(dāng)初引用（used to）次數(shù)的一半來(lái)推算出半衰期。
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