Every day we face issues like climate change or the safety of vaccines where we have to answer questions whose answers rely heavily on scientific information. Scientists tell us that the world is warming. Scientists tell us that vaccines are safe. But how do we know if they are right? Why should be believe the science? The fact is, many of us actually don't believe the science. Public opinion polls consistently show that significant proportions of the American people don't believe the climate is warming due to human activities, don't think that there is evolution by natural selection, and aren't persuaded by the safety of vaccines.
每天,我们都面对着像气候变化或 疫苗安全这样的问题, 而对这些问题的解答 都仰仗于科学知识。 科学家告诉我们世界正在变暖。 科学家告诉我们疫苗是安全的。 但我们怎么知道他们说的是对的呢? 为什么我们要相信科学呢? 事实是,我们中的大多数人 实际上不相信科学。 民意调查一贯表明, 有相当一部分美国人 不相信气候正在变暖是由于人类活动, 不相信有自然选择的生物演化, 并且不相信疫苗的安全性。
So why should we believe the science? Well, scientists don't like talking about science as a matter of belief. In fact, they would contrast science with faith, and they would say belief is the domain of faith. And faith is a separate thing apart and distinct from science. Indeed they would say religion is based on faith or maybe the calculus of Pascal's wager. Blaise Pascal was a 17th-century mathematician who tried to bring scientific reasoning to the question of whether or not he should believe in God, and his wager went like this: Well, if God doesn't exist but I decide to believe in him nothing much is really lost. Maybe a few hours on Sunday. (Laughter) But if he does exist and I don't believe in him, then I'm in deep trouble. And so Pascal said, we'd better believe in God. Or as one of my college professors said, "He clutched for the handrail of faith." He made that leap of faith leaving science and rationalism behind.
那么我们为什么要相信科学呢? 好吧,科学家并不喜欢把 科学当作一种信念来讨论。 事实上,他们会把科学与信仰相对, 并且会说信念是信仰的一部分。 而信仰是一个处在科学之外, 与其截然不同的东西。 事实上他们会说宗教是基于信仰的, 或是基于帕斯卡赌注的演算。 布莱兹·帕斯卡是一位17世纪的数学家, 他试着将科学的论证引入 他是否该信仰上帝这一问题, 他的赌注如下: 好吧,如果上帝不存在, 但我决定信仰他, 没有什么大的损失。 也许只是周日要花掉几个小时。 (众人笑。) (译注:基督教周日礼拜。) 但如果他存在但我不信仰他, 那我就有很大的麻烦了。 所以帕斯卡说我们最好相信上帝, 或者像我的一个大学教授所说, “他紧紧抓住了信仰的扶手。” 帕斯卡最终选择了信仰, 放下了科学与理性。
Now the fact is though, for most of us, most scientific claims are a leap of faith. We can't really judge scientific claims for ourselves in most cases. And indeed this is actually true for most scientists as well outside of their own specialties. So if you think about it, a geologist can't tell you whether a vaccine is safe. Most chemists are not experts in evolutionary theory. A physicist cannot tell you, despite the claims of some of them, whether or not tobacco causes cancer. So, if even scientists themselves have to make a leap of faith outside their own fields, then why do they accept the claims of other scientists? Why do they believe each other's claims? And should we believe those claims?
事实是对我们中的大多数人, 大多数科学结论都是一种信仰。 在大多数时候, 我们并不能亲自验证科学结论。 事实上在专业领域之外, 对于大多是科学家来说也是这样。 所以就算你想知道一个疫苗是否安全, 一个地质学家也不能告诉你答案。 大多数化学家也不是进化论的专家。 一个物理学家不能告诉你, 烟草是否致癌, 尽管他们中的一些说自己能。 所以,如果连科学家 在自己的专业领域之外 都得相信无法感知的结论, 那么这些科学家为什么要相信 其他科学家的断言呢? 为什么他们要相信彼此的结论? 我们也应该相信这些结论吗?
So what I'd like to argue is yes, we should, but not for the reason that most of us think. Most of us were taught in school that the reason we should believe in science is because of the scientific method. We were taught that scientists follow a method and that this method guarantees the truth of their claims. The method that most of us were taught in school, we can call it the textbook method, is the hypothetical deductive method. According to the standard model, the textbook model, scientists develop hypotheses, they deduce the consequences of those hypotheses, and then they go out into the world and they say, "Okay, well are those consequences true?" Can we observe them taking place in the natural world? And if they are true, then the scientists say, "Great, we know the hypothesis is correct."
所以我的答案是”没错,我们应该相信。“ 但不是由于我们大多数人所想的原因。 学校教诲我们大多数人 要相信科学是因为那些科学方法。 我们了解到科学家是遵从某种方法, 并且这个方法保证了 他们结论的正确性。 大多数人在学校学到的这种方法, 我们称之为教科书方法, 是一种假设性的演绎推理。 根据标准的模型,即教科书模型, 科学家发展了假说, 并推理出这些假说的结果, 然后他们对全世界宣称, “好的,这些结果正确吗?” 我们能够观测到 它们在自然界中发生吗? 如果它们是正确的,那么科学家说, “好的,我们知道假设是正确的。”
So there are many famous examples in the history of science of scientists doing exactly this. One of the most famous examples comes from the work of Albert Einstein. When Einstein developed the theory of general relativity, one of the consequences of his theory was that space-time wasn't just an empty void but that it actually had a fabric. And that that fabric was bent in the presence of massive objects like the sun. So if this theory were true then it meant that light as it passed the sun should actually be bent around it. That was a pretty startling prediction and it took a few years before scientists were able to test it but they did test it in 1919, and lo and behold it turned out to be true. Starlight actually does bend as it travels around the sun. This was a huge confirmation of the theory. It was considered proof of the truth of this radical new idea, and it was written up in many newspapers around the globe.
所以历史上有许多著名的 关于科学家们做了这件事的例子。 最著名的例子之一 来自爱因斯坦的工作。 当爱因斯坦发展他的广义相对论时, 理论的一个结果是 时空并不是空空如也, 而是有一个网状结构。 那种结构会在大质量物体 比如太阳的附近弯曲。 所以如果这个理论是正确的, 那么当光线经过太阳时, 会在太阳附近被弯曲。 这是一个很惊人的预测, 科学家花了很多年 才验证了它, 但是他们确实在1919年进行了验证, 并且结果是正确的。 星光确实在经过太阳附近时被弯曲。 这是对广义相对论的一个重要肯定, 这被认为是对广义相对论 这一新颖理论的证明, 这次验证也被全球许多报纸 报道了。
Now, sometimes this theory or this model is referred to as the deductive-nomological model, mainly because academics like to make things complicated. But also because in the ideal case, it's about laws. So nomological means having to do with laws. And in the ideal case, the hypothesis isn't just an idea: ideally, it is a law of nature. Why does it matter that it is a law of nature? Because if it is a law, it can't be broken. If it's a law then it will always be true in all times and all places no matter what the circumstances are. And all of you know of at least one example of a famous law: Einstein's famous equation, E=MC2, which tells us what the relationship is between energy and mass. And that relationship is true no matter what.
有时候这些理论和模型 被称为演绎-规律模型(D-N模型), 主要是因为学者喜欢让事情复杂化。 但也是因为在理想状态下,这是法则。 Nomological表示要遵循法则。 在理想情况下,假说不仅仅是一个想法: 它是自然法则。 为什么自然法则很重要呢? 因为法则不能被打破。 法则一定是正确的, 不管何时何地, 不管在什么情况下。 你们大概都知道一个著名的法则: 爱因斯坦的著名等式,E=mc^2, 它告诉了我们能量和 质量的关系。 这个关系在任何情况下都成立。
Now, it turns out, though, that there are several problems with this model. The main problem is that it's wrong. It's just not true. (Laughter) And I'm going to talk about three reasons why it's wrong. So the first reason is a logical reason. It's the problem of the fallacy of affirming the consequent. So that's another fancy, academic way of saying that false theories can make true predictions. So just because the prediction comes true doesn't actually logically prove that the theory is correct. And I have a good example of that too, again from the history of science. This is a picture of the Ptolemaic universe with the Earth at the center of the universe and the sun and the planets going around it. The Ptolemaic model was believed by many very smart people for many centuries. Well, why? Well the answer is because it made lots of predictions that came true. The Ptolemaic system enabled astronomers to make accurate predictions of the motions of the planet, in fact more accurate predictions at first than the Copernican theory which we now would say is true. So that's one problem with the textbook model. A second problem is a practical problem, and it's the problem of auxiliary hypotheses. Auxiliary hypotheses are assumptions that scientists are making that they may or may not even be aware that they're making. So an important example of this comes from the Copernican model, which ultimately replaced the Ptolemaic system. So when Nicolaus Copernicus said, actually the Earth is not the center of the universe, the sun is the center of the solar system, the Earth moves around the sun. Scientists said, well okay, Nicolaus, if that's true we ought to be able to detect the motion of the Earth around the sun. And so this slide here illustrates a concept known as stellar parallax. And astronomers said, if the Earth is moving and we look at a prominent star, let's say, Sirius -- well I know I'm in Manhattan so you guys can't see the stars, but imagine you're out in the country, imagine you chose that rural life — and we look at a star in December, we see that star against the backdrop of distant stars. If we now make the same observation six months later when the Earth has moved to this position in June, we look at that same star and we see it against a different backdrop. That difference, that angular difference, is the stellar parallax. So this is a prediction that the Copernican model makes. Astronomers looked for the stellar parallax and they found nothing, nothing at all. And many people argued that this proved that the Copernican model was false.
但结果表明, 似乎这个模型有一些问题。 最主要的问题就是这个模型是错的。 它不是正确的。 (笑声) 我想讲讲三个原因, 为什么说它是错的。 第一个是逻辑问题。 问题在于荒谬地断定结果。 这是另一种幻想,用学术术语称为 错误的理论得出正确的预测。 所以仅仅因为预测是正确的, 并不能从逻辑上证明理论是正确的。 科学史上有一个很好的例子。 这是一幅展现托勒密宇宙的图, 地球在宇宙中心, 太阳和行星都围绕它运行。 托勒密模型在多个世纪中 都被很多聪明人所接纳。 为什么? 因为由它进行的许多预测 都被证明是正确的。 托勒密系统使得天文学家 对行星运行做出准确的预测, 事实上甚至比哥白尼理论 在最初时更准确, 虽然后者现在被公认是正确的。 所以这是教科书模型的一个问题。 第二个问题是一个实际问题, 问题在于辅助假说。 辅助假说是科学家 做出的假设, 他们可能甚至没意识到 自己做出了这个假设。 一个很重要的例子是 哥白尼模型, 它最终取代了托勒密模型。 所以当尼古拉斯哥白尼宣称 事实上地球不是宇宙的中心, 太阳是太阳系的中心, 地球围绕着太阳旋转, 科学家说,好吧,尼古拉斯, 如果这是对的, 我们应该能检测出地球围绕 太阳的运动。 这一页幻灯片展示了这个概念, 叫做星球视差。 天文家说,如果地球是运动的 我们关注一个显眼的星星, 比如天狼星—— 在曼哈顿可能看不到这颗星星, 但想象你在乡村,你过着田园生活, 在十二月我们看着那个星星, 以其他遥远的星星做背景。 而六个月之后, 如果我们做同样的观察, 在六月,地球已经移动到了这个位置, 我们观察同样的星星 应该看到不同的背景。 这个视角的差异被称为星球视差。 这就是哥白尼模型做出的预测。 天文学家想找到星球视差, 但他们找不到任何差异。 许多人称这证明了哥白尼模型 是错误的。
So what happened? Well, in hindsight we can say that astronomers were making two auxiliary hypotheses, both of which we would now say were incorrect. The first was an assumption about the size of the Earth's orbit. Astronomers were assuming that the Earth's orbit was large relative to the distance to the stars. Today we would draw the picture more like this, this comes from NASA, and you see the Earth's orbit is actually quite small. In fact, it's actually much smaller even than shown here. The stellar parallax therefore, is very small and actually very hard to detect.
怎么回事呢? 事后我们能说天文学家 做出了两个假设, 现在被公认都是错误的。 第一个是关于地球轨道大小的假设。 天文学家假定地球的轨道很大, 相较于星星的距离。 今天我们会画出一个这样的图案 来自NASA。 可以看到地球的轨道非常小。 事实上可能比这显示的更小。 因此,星球视差 会非常小,而且难以探测。
And that leads to the second reason why the prediction didn't work, because scientists were also assuming that the telescopes they had were sensitive enough to detect the parallax. And that turned out not to be true. It wasn't until the 19th century that scientists were able to detect the stellar parallax.
这也引出了另一个原因, 为什么这个预测不准确。 因为科学家也假定 他们的天文望远镜足够灵敏, 足以检测到这个视差。 后来证明是错的。 直到19世纪 科学家才能检测出 星球视差。
So, there's a third problem as well. The third problem is simply a factual problem, that a lot of science doesn't fit the textbook model. A lot of science isn't deductive at all, it's actually inductive. And by that we mean that scientists don't necessarily start with theories and hypotheses, often they just start with observations of stuff going on in the world. And the most famous example of that is one of the most famous scientists who ever lived, Charles Darwin. When Darwin went out as a young man on the voyage of the Beagle, he didn't have a hypothesis, he didn't have a theory. He just knew that he wanted to have a career as a scientist and he started to collect data. Mainly he knew that he hated medicine because the sight of blood made him sick so he had to have an alternative career path. So he started collecting data. And he collected many things, including his famous finches. When he collected these finches, he threw them in a bag and he had no idea what they meant. Many years later back in London, Darwin looked at his data again and began to develop an explanation, and that explanation was the theory of natural selection.
还有第三个问题。 这是现实问题, 许多的科学不适用教科书模型。 许多的科学根本不是演绎, 而是归纳出来的。 这样的话,科学家并不需要 由理论和假设出发, 通常他们只是从观察出发, 观察世界上的的一切。 最著名的例子是 最著名的科学家,查尔斯·达尔文。 当达尔文还年轻,在比格号上航行时, 他没有假设,他没有理论。 他只知道他希望成为科学家, 他开始收集数据。 主要是因为他讨厌医学, 因为他晕血, 所以他必须选择另一条职业道路。 所以他开始收集数据。 他收集许多的东西, 包括他著名的雀鸟。 他收集这些雀鸟的时候, 会把它们扔到袋子里, 他并不知道它们意味着什么。 许多年以后回到伦敦时, 达尔文再次翻看他的数据, 开始做出了一些解释, 这个解释就是自然选择论。
Besides inductive science, scientists also often participate in modeling. One of the things scientists want to do in life is to explain the causes of things. And how do we do that? Well, one way you can do it is to build a model that tests an idea.
除了归纳科学, 科学家也常常进行建模。 他们一生中最想做的事情 就是解释事情的起因。 我们怎么做呢? 建立模型是一个方法, 可以用来测试一个想法。
So this is a picture of Henry Cadell, who was a Scottish geologist in the 19th century. You can tell he's Scottish because he's wearing a deerstalker cap and Wellington boots. (Laughter) And Cadell wanted to answer the question, how are mountains formed? And one of the things he had observed is that if you look at mountains like the Appalachians, you often find that the rocks in them are folded, and they're folded in a particular way, which suggested to him that they were actually being compressed from the side. And this idea would later play a major role in discussions of continental drift. So he built this model, this crazy contraption with levers and wood, and here's his wheelbarrow, buckets, a big sledgehammer. I don't know why he's got the Wellington boots. Maybe it's going to rain. And he created this physical model in order to demonstrate that you could, in fact, create patterns in rocks, or at least, in this case, in mud, that looked a lot like mountains if you compressed them from the side. So it was an argument about the cause of mountains.
这是亨利卡德尔的照片, 他是19世纪的苏格兰地理学家。 从服饰可以看出他是一个苏格兰人, 猎鹿帽和威林顿靴。 (笑声) 卡德尔希望能回答这个问题, 山是怎么形成的? 他观察到的一个事情是, 如果你注视山,比如阿帕拉契山脉时, 你会发现山中的石头 是叠层的, 它们由特定的方式堆叠而成, 这显示 它们是由两侧挤压而成的。 这个想法在之后的 大陆漂移假说中扮演了重要角色。 所以他建造了这个模型, 这个疯狂的装置, 有杠杆、木头、独轮车、 木桶、大锤子, 我不知道他什么要穿威林顿靴。 也许要下雨了吧。 他建造了这个实物模型, 用来证明你事实上能在石头上, 或至少像这样在泥土上制造 山石那样的纹路, 只需要从侧面挤压它们。 这是关于山体形成的论证。
Nowadays, most scientists prefer to work inside, so they don't build physical models so much as to make computer simulations. But a computer simulation is a kind of a model. It's a model that's made with mathematics, and like the physical models of the 19th century, it's very important for thinking about causes. So one of the big questions to do with climate change, we have tremendous amounts of evidence that the Earth is warming up. This slide here, the black line shows the measurements that scientists have taken for the last 150 years showing that the Earth's temperature has steadily increased, and you can see in particular that in the last 50 years there's been this dramatic increase of nearly one degree centigrade, or almost two degrees Fahrenheit.
如今,科学家们更希望 进行深入的研究, 他们并不常常建立实物模型, 而是用计算机模拟。 但计算机模拟仅仅是一个模型。 一个数学模型, 正如19世纪的实物模型一样, 思考起因是非常重要的。 所以应对气候变化最重要的问题就是, 我们有大量的证据表明 地球正在升温。 这页幻灯片中,黑色的线条显示 科学家们测量的 过去150年的统计结果, 显示了地球的温度 正在稳步升高, 尤其是最近的50年 上升是显著的, 几乎是1摄氏度, 或2华氏度。
So what, though, is driving that change? How can we know what's causing the observed warming? Well, scientists can model it using a computer simulation. So this diagram illustrates a computer simulation that has looked at all the different factors that we know can influence the Earth's climate, so sulfate particles from air pollution, volcanic dust from volcanic eruptions, changes in solar radiation, and, of course, greenhouse gases. And they asked the question, what set of variables put into a model will reproduce what we actually see in real life? So here is the real life in black. Here's the model in this light gray, and the answer is a model that includes, it's the answer E on that SAT, all of the above. The only way you can reproduce the observed temperature measurements is with all of these things put together, including greenhouse gases, and in particular you can see that the increase in greenhouse gases tracks this very dramatic increase in temperature over the last 50 years. And so this is why climate scientists say it's not just that we know that climate change is happening, we know that greenhouse gases are a major part of the reason why.
那么是什么驱动了这个改变呢? 我们怎么知道是什么导致了 这么明显的升温呢? 科学家可以建模, 用计算机进行模拟。 这张图展示了计算机模拟, 考虑了各种 可能影响地球气候的因素, 从空气污染中的硫酸盐颗粒, 到火山喷发中的火山灰, 到太阳辐射的改变, 当然,还有温室气体。 他们问了这样一个问题, 在模型中加入什么样的变量 能再现我们在真实生活中 看到的情况呢? 黑线表示真实观察的数据, 浅灰色表示模拟的数据, 答案是 在上述的模拟中加入SAT考试中的E, 也就是以上皆有。 (译注:SAT考试中最常见答案) 能再现所观察到的 温度测量数据的唯一的方法, 就是把所有的东西放到一起, 包括温室气体, 特别是我们可以观察到 在对温室气体数据 追踪时显示温度的上升, 在过去的50年非常明显。 所以这就是为什么气候学家称 我们不仅仅知道气候变化正在发生, 我们还知道温室气体是主要的 影响因素。
So now because there all these different things that scientists do, the philosopher Paul Feyerabend famously said, "The only principle in science that doesn't inhibit progress is: anything goes." Now this quotation has often been taken out of context, because Feyerabend was not actually saying that in science anything goes. What he was saying was, actually the full quotation is, "If you press me to say what is the method of science, I would have to say: anything goes." What he was trying to say is that scientists do a lot of different things. Scientists are creative.
由于科学家做的这些 各种各样的事情, 哲学家保罗·费耶阿本德 说过一句名言, “在不影响进步的情况下, 科学界唯一个法则就是: 任何方法都可以。” 这句名言经常被断章取义, 因为费耶阿本德并不是说 在科学上怎么都行。 他想说的 完整版的话应该是, “如果你强制我说出 科学研究方法是什么, 我会说:任何方法都可以。" 他想要说的应该是 科学家做了许多不同的事情。 科学家很有创造力。
But then this pushes the question back: If scientists don't use a single method, then how do they decide what's right and what's wrong? And who judges? And the answer is, scientists judge, and they judge by judging evidence. Scientists collect evidence in many different ways, but however they collect it, they have to subject it to scrutiny. And this led the sociologist Robert Merton to focus on this question of how scientists scrutinize data and evidence, and he said they do it in a way he called "organized skepticism." And by that he meant it's organized because they do it collectively, they do it as a group, and skepticism, because they do it from a position of distrust. That is to say, the burden of proof is on the person with a novel claim. And in this sense, science is intrinsically conservative. It's quite hard to persuade the scientific community to say, "Yes, we know something, this is true." So despite the popularity of the concept of paradigm shifts, what we find is that actually, really major changes in scientific thinking are relatively rare in the history of science.
但这个问题又回来了, 如果科学家不用一种统一的方法, 他们怎么决定 什么是正确的或者错误的? 由谁来决定呢? 答案是,由科学家决定, 他们依照证据决定。 科学家通过不同的方法收集证据, 但不论他们如何收集, 他们要审慎看待这些证据。 这就导致了社会学家罗伯特 · 默顿 关注这样一个问题,即科学家该如何 审慎看待他们的证据和数据, 他将这种方法称之为 “组织性怀疑”。 “组织性”说明 科学家合作收集数据, 他们作为团队一起工作, “怀疑”说明他们对证据 持怀疑态度。 这就是说,关于证据的主要工作 落在了宣称自己 发现了新东西的人身上。 在这种情况下,科学的本质是保守。 想要说服科学界 称“我们发现了些东西,这是真的”很难。 尽管方式转变的观念 被广泛地接受, 我们却发现 科学思维上的重要改变 在科学史上十分罕见。
So finally that brings us to one more idea: If scientists judge evidence collectively, this has led historians to focus on the question of consensus, and to say that at the end of the day, what science is, what scientific knowledge is, is the consensus of the scientific experts who through this process of organized scrutiny, collective scrutiny, have judged the evidence and come to a conclusion about it, either yea or nay.
最后我们提出了另一个想法: 如果科学家集体评判证据, 这就导致历史学家聚焦在了 “共识”这一问题上, 在最后, 什么是科学, 科学知识是什么, 这是科学专家达成的共识, 他们通过这种组织性的怀疑, 合作的怀疑, 来评判证据 得出结论, 判断正误,
So we can think of scientific knowledge as a consensus of experts. We can also think of science as being a kind of a jury, except it's a very special kind of jury. It's not a jury of your peers, it's a jury of geeks. It's a jury of men and women with Ph.D.s, and unlike a conventional jury, which has only two choices, guilty or not guilty, the scientific jury actually has a number of choices. Scientists can say yes, something's true. Scientists can say no, it's false. Or, they can say, well it might be true but we need to work more and collect more evidence. Or, they can say it might be true, but we don't know how to answer the question and we're going to put it aside and maybe we'll come back to it later. That's what scientists call "intractable."
所以我们可以认为科学知识 就是专家的共识。 我们也能认为科学是 一种陪审团下的产物, 当然这个陪审团非常特别。 他们不是你的同辈组成的, 他们是怪才组成的陪审团。 他们是由男博士女博士组成的, 不同于传统的陪审团 只有两种选择, 有罪或无罪, 科学陪审团有很多的选择。 科学家们能说,这是对的。 他们能说,这是错的。 他们也能说,这可能是对的, 但我们需要更多的证据。 他们也能说,这可能是对的, 但我们不知道如何回答这个问题, 可以先放在一边, 之后再讨论。 他们称这种情况“很棘手”。
But this leads us to one final problem: If science is what scientists say it is, then isn't that just an appeal to authority? And weren't we all taught in school that the appeal to authority is a logical fallacy? Well, here's the paradox of modern science, the paradox of the conclusion I think historians and philosophers and sociologists have come to, that actually science is the appeal to authority, but it's not the authority of the individual, no matter how smart that individual is, like Plato or Socrates or Einstein. It's the authority of the collective community. You can think of it is a kind of wisdom of the crowd, but a very special kind of crowd. Science does appeal to authority, but it's not based on any individual, no matter how smart that individual may be. It's based on the collective wisdom, the collective knowledge, the collective work, of all of the scientists who have worked on a particular problem. Scientists have a kind of culture of collective distrust, this "show me" culture, illustrated by this nice woman here showing her colleagues her evidence. Of course, these people don't really look like scientists, because they're much too happy. (Laughter)
但这把我们引向另一个问题: 如果科学是科学家定义的, 那这是不是只诉诸权威呢? 我们不是在学校学过, 诉诸权威是逻辑上的谬误吗? 其实这是现代科学的悖论, 我相信历史学家、 哲学家和社会学家都会得出来的悖论, 事实上科学就是诉诸权威, 但这不是对个人的权威, 不论这个人有多聪明, 像柏拉图,苏格拉底或爱因斯坦。 这是对精英群体的权威。 你可以把它理解成一种集体智慧, 但这个集体非常特别。 科学确实诉诸权威, 但不是诉诸个人, 不论这个人多聪明。 它建立于集体智慧之上, 建立于集体知识,集体工作之上, 建立于为这个问题努力过的 所有科学家之上。 科学家有一种集体怀疑的文化, 这就是“给我看”文化, 这个女士向我们展示了这一点, 她在向她的同事展示她的证据。 当然,这些人并不像科学家, 他们看起来太开心了。 (笑声)
Okay, so that brings me to my final point. Most of us get up in the morning. Most of us trust our cars. Well, see, now I'm thinking, I'm in Manhattan, this is a bad analogy, but most Americans who don't live in Manhattan get up in the morning and get in their cars and turn on that ignition, and their cars work, and they work incredibly well. The modern automobile hardly ever breaks down.
最后,我想说: 我们大多数人要早起奔波。 大多数人依赖我们的汽车。 瞧,我现在在曼哈顿, 这不是一个很好的类比, 但大多数美国人没住在曼哈顿, 早上起来,钻进汽车, 点火,汽车就运转了, 运转得相当不错。 现代的汽车基本不怎么抛锚。
So why is that? Why do cars work so well? It's not because of the genius of Henry Ford or Karl Benz or even Elon Musk. It's because the modern automobile is the product of more than 100 years of work by hundreds and thousands and tens of thousands of people. The modern automobile is the product of the collected work and wisdom and experience of every man and woman who has ever worked on a car, and the reliability of the technology is the result of that accumulated effort. We benefit not just from the genius of Benz and Ford and Musk but from the collective intelligence and hard work of all of the people who have worked on the modern car. And the same is true of science, only science is even older. Our basis for trust in science is actually the same as our basis in trust in technology, and the same as our basis for trust in anything, namely, experience.
为什么呢? 为什么车能运行得这么好? 这不是因为亨利·福特的天才, 也不是卡尔·奔驰或伊隆·马斯克。 这是因为现代的汽车 是100多年努力的结晶, 是成百上千, 甚至上万人的努力。 现代汽车是 集体工作和智慧及经验的产物, 是所有为汽车工作过的 男人和女人的产物, 这项技术的可靠性就是 这些付出加起来的结果。 我们不仅仅从奔驰,福特和马斯克的 天才中获益, 而是从所有为现代汽车 奋斗的人们的 集体智慧和工作中获益。 科学界也一样, 只是科学更加古老。 我们信任科学的基石, 与我们信任技术的基石是一样的, 与我们信任其他事物的基石 都是一样的, 也就是,经验。
But it shouldn't be blind trust any more than we would have blind trust in anything. Our trust in science, like science itself, should be based on evidence, and that means that scientists have to become better communicators. They have to explain to us not just what they know but how they know it, and it means that we have to become better listeners.
但这不应该是盲目的信任, 不能盲目信任任何事情。 我们对科学的信任如同科学本身, 应该建立于证据, 这意味着科学家 应该善于沟通。 他们不仅必须向我们 解释他们知道的东西, 还要解释他们知道的过程, 这意味着我们需要变为更好的聆听者。
Thank you very much.
十分感谢。
(Applause)
(鼓掌)