TED：認(rèn)知半徑?jīng)Q定了一個人的能力

I'm a meteorologist by degree, I have a bachelor's, master's and PhD in physical meteorology, so I'm a meteorologist, card carrying.

我是一名氣象學(xué)家，我有物理氣象學(xué)的學(xué)士、碩士和博士學(xué)位，所以我是個氣象學(xué)家，有證的。

And so with that comes four questions, always. This is one prediction I will always get right.

然而總有4個問題等著我，在這件事兒上我的預(yù)測總是對的。

And those questions are, "Marshall, what channel are you on?"

這些問題是，“馬修，你在哪個頻道呢?”

"Dr. Shepherd, what's the weather going to be tomorrow?"

“謝博德博士，明天天氣怎么樣?”

And oh, I love this one: "My daughter is getting married next September, it's an outdoor wedding. Is it going to rain?"

啊，我喜歡這個：“我女兒明年九月結(jié)婚， 是個戶外婚禮。到時會下雨嗎?”

Not kidding, I get those, and I don't know the answer to that, the science isn't there. But the one I get a lot these days is, "Dr. Shepherd, do you believe in climate change?" "Do you believe in global warming?"

沒開玩笑，我總被問這些問題，然而我并不知道答案，科學(xué)在這兒不管用。但我這些天經(jīng)常被問的是，“謝博德博士，你相信氣候變化嗎?”“你相信全球變暖嗎?”

Now, I have to gather myself every time I get that question. Because it's an ill-posed question -- science isn't a belief system.

如今每次被問到這些問題時，我都得打起精神。因為這是個不恰當(dāng)?shù)膯栴}——科學(xué)可不是一個信仰體系。

My son, he's 10，he believes in the tooth fairy. And he needs to get over that, because I'm losing dollars, fast.

我10歲的兒子相信牙仙的存在。他得克服這一點，因為太費錢了。(傳說牙仙會用金幣把小孩子掉的牙換走)

But he believes in the tooth fairy. But consider this. Bank of America building, there, in Atlanta. You never hear anyone say, "Do you believe, if you go to the top of that building and throw a ball off, it's going to fall?"

他的確相信牙仙。但想一想這個。這是亞特蘭大的美國銀行大樓。你從沒聽到人說，“你相信嗎，如果你到那個樓頂，拋個球，它就會掉下去?”

You never hear that, because gravity is a thing. So why don't we hear the question, "Do you believe in gravity?" But of course, we hear the question, "Do you believe in global warming?"

你從沒聽過，因為重力是實際存在的。所以為什么我們不會聽到這個問題，“你相信重力嗎?”但我們肯定聽過這個問題，“你相信全球變暖嗎?”

Well, consider these facts. The American Association for the Advancement of Science, AAAS, one of the leading organizations in science, queried scientists and the public on different science topics.

考慮到這些事實：美國科學(xué)促進(jìn)會，簡稱AAAS，這是一個在科學(xué)領(lǐng)域的主要組織，曾就不同的科學(xué)課題向科學(xué)家和公眾提問。

Here are some of them: genetically modified food, animal research, human evolution. And look at what the scientists say about those, the people that actually study those topics, in red, versus the gray, what the public thinks.

這是其中一些課題：轉(zhuǎn)基因產(chǎn)品，動物研究，人類進(jìn)化?？纯纯茖W(xué)家對這些怎么說，紅色代表那些在研究這些課題的人， 灰色，則代表公眾的態(tài)度。

How did we get there? How did we get there? That scientists and the public are so far apart on these science issues.

這是怎么造成的?為什么會有這么大的差異?科學(xué)家和公眾在這些科學(xué)問題上意見如此相左。

Well, I'll come a little bit closer to home for me, climate change. Eighty-seven percent of scientists believe that humans are contributing to climate change. But only 50 percent of the public?

好了，我要說個我比較擅長的，氣候變化。87%的科學(xué)家認(rèn)為是人類的行為導(dǎo)致了氣候變化，但只有50%的公眾這樣認(rèn)為。

How did we get there? So it begs the question, what shapes perceptions about science? It's an interesting question and one that I've been thinking about quite a bit. I think that one thing that shapes perceptions in the public, about science, is belief systems and biases.

為什么會這樣?這就引出了問題，是什么塑造了我們對科學(xué)的認(rèn)知?這是個有趣的問題，我也一直在思考這個問題。我想有一件事影響了公眾對科學(xué)的看法，就是信仰體系和偏見。

Belief systems and biases. Go with me for a moment. Because I want to talk about three elements of that: confirmation bias, Dunning-Kruger effect and cognitive dissonance.

信仰體系和偏見。我來解釋一下。我想要談一談這個問題的三個元素：確認(rèn)偏誤，達(dá)克效應(yīng)和認(rèn)知失調(diào)。

Now, these sound like big, fancy, academic terms, and they are. But when I describe them, you're going to be like, "Oh! I recognize that; I even know somebody that does that."

這些聽起來都有點像不切實際的學(xué)術(shù)術(shù)語，它們也確實是這樣的。但當(dāng)我進(jìn)一步做出解釋時，你們就會恍然大悟，“哦!我聽說過這個;我甚至知道有人就是這樣的?！?/p>

Confirmation bias. Finding evidence that supports what we already believe. Now, we're probably all a little bit guilty of that at times. Take a look at this. I'm on Twitter.

確認(rèn)偏誤。尋找證據(jù)來支持我們已經(jīng)相信的事。我們對此可能多少都難辭其咎。看看這個。我有自己的Twitter賬戶。

And often, when it snows, I'll get this tweet back to me.

通常，遇到下雪的時候，我會收到這樣的轉(zhuǎn)發(fā)。

"Hey, Dr. Shepherd, I have 20 inches of global warming in my yard, what are you guys talking about, climate change?" I get that tweet a lot, actually. It's a cute tweet, it makes me chuckle as well.

“嘿，謝博德博士，我院子里有20英寸的全球變暖(指雪)，你們這些家伙在說啥，氣候變化?”我其實收到了很多那樣的推特。

But it's oh, so fundamentally scientifically flawed. Because it illustrates that the person tweeting doesn't understand the difference between weather and climate. I often say, weather is your mood and climate is your personality.

這條推特挺逗的，也讓我忍俊不禁。但它在科學(xué)上是站不住腳的。因為它說明了發(fā)推特的人并不理解天氣和氣候的差異。我常說，天氣是你的情緒，而氣候是你的個性。

Think about that. Weather is your mood, climate is your personality. Your mood today doesn't necessarily tell me anything about your personality, nor does a cold day tell me anything about climate change, or a hot day, for that matter.

想想看，天氣是你的情緒，氣候是你的個性。你今天的情緒不一定能代表你的個性，所以即使有一天特別冷，也不能說明氣候變化了，有一天特別熱，也一樣不能代表什么。

Dunning-Kruger. Two scholars from Cornell came up with the Dunning-Kruger effect. If you go look up the peer-reviewed paper for this, you will see all kinds of fancy terminology:

達(dá)克效應(yīng)。(高估自己的能力)康奈爾大學(xué)的兩位學(xué)者提出了達(dá)克效應(yīng)。如果你去查閱同行評議的論文，你會看到各種很炫的術(shù)語：

it's an illusory superiority complex, thinking we know things. In other words, people think they know more than they do. Or they underestimate what they don't know.

這是一種虛幻的優(yōu)越感，以為我們什么都知道。換句話說，人們高估了自己所掌握的知識?；蛘哒f，他們低估了他們的無知。

And then, there's cognitive dissonance. Cognitive dissonance is interesting. We just recently had Groundhog Day, right? Now, there's no better definition of cognitive dissonance than intelligent people asking me if a rodent's forecast is accurate.

然后是認(rèn)知失調(diào)。(新信息沖擊現(xiàn)有認(rèn)知)認(rèn)知失調(diào)很有趣。我們剛剛過了土撥鼠節(jié)，是吧?對認(rèn)知失調(diào)最好的解釋就好比是，一個聰明人問我嚙齒動物的預(yù)測是否準(zhǔn)確。

But I get that, all of the time. But I also hear about the Farmer's Almanac. We grew up on the Farmer's Almanac, people are familiar with it.

但我一直都能理解。我也聽說過黃歷。我們靠著黃歷長大，人們很熟悉它。

The problem is, it's only about 37 percent accurate, according to studies at Penn State University. But we're in an era of science where we actually can forecast the weather. And believe it or not,

但問題在于，根據(jù)賓夕法尼亞州立大學(xué)的研究，它的準(zhǔn)確性只有37%。但我們身在科學(xué)的時代，我們確實可以預(yù)測天氣。不管信不信，

and I know some of you are like, "Yeah, right," we're about 90 percent accurate, or more, with weather forecast. You just tend to remember the occasional miss, you do.

我知道你們有些人會說：“好吧好吧，你說的都對”，我們對天氣預(yù)測的準(zhǔn)確率有90%或者更高。但你們只會記得偶爾幾次的失誤，可別不承認(rèn)。

So confirmation bias, Dunning-Kruger and cognitive dissonance. I think those shape biases and perceptions that people have about science.

所以確認(rèn)偏誤，達(dá)克效應(yīng)和認(rèn)知失調(diào)。我認(rèn)為是這些形成了人們對科學(xué)的偏見和看法。

But then, there's literacy and misinformation that keep us boxed in, as well. During the hurricane season of 2017, media outlets had to actually assign reporters to dismiss fake information about the weather forecast. That's the era that we're in.

但是，文化素養(yǎng)和錯誤信息也會讓我們陷入困境。在2017年的颶風(fēng)季，媒體機(jī)構(gòu)不得不指派記者，駁斥有關(guān)天氣預(yù)報的虛假信息。這就是我們所在的時代。

I deal with this all the time in social media. Someone will tweet a forecast -- that's a forecast for Hurricane Irma, but here's the problem: it didn't come from the Hurricane Center.

我一直在社交媒體上應(yīng)對這些問題。有人會在推特上發(fā)布預(yù)報——這是颶風(fēng)厄瑪?shù)念A(yù)報，但問題是：它不是官方颶風(fēng)中心發(fā)布的。

But people were tweeting and sharing this; it went viral. It didn't come from the National Hurricane Center at all.

但人們在推特上分享這個，消息就擴(kuò)散開了。它根本就不是國家颶風(fēng)中心發(fā)布的。

So I spent 12 years of my career at NASA before coming to the University of Georgia, and I chair their Earth Science Advisory Committee, I was just up there last week in DC. And I saw some really interesting things.

在來到喬治亞大學(xué)之前，我在NASA工作了12年，我是地球科學(xué)咨詢委員會的主席，我上周剛剛?cè)ミ^華盛頓。我看到了一些很有趣的事情。

Here's a NASA model and science data from satellite showing the 2017 hurricane season. You see Hurricane Harvey there? Look at all the dust coming off of Africa. Look at the wildfires up in northwest US and in western Canada.

這是NASA的模型和 來自衛(wèi)星的科學(xué)數(shù)據(jù)，顯示了2017年颶風(fēng)季的情況。你們看到那邊的哈維颶風(fēng)沒?看看這些從非洲飄來的塵土?？纯疵绹鞅辈亢图幽么笪鞑康囊盎稹?/p>

There comes Hurricane Irma. This is fascinating to me. But admittedly, I'm a weather geek. But more importantly, it illustrates that we have the technology to not only observe the weather and climate system, but predict it.

颶風(fēng)厄瑪來了。這對我很有吸引力。無可否認(rèn)，我是個氣象迷。但更重要的是，它展示了我們擁有的科技不僅可以觀察天氣和氣候系統(tǒng)，而且可以預(yù)測它。

There's scientific understanding, so there's no need for some of those perceptions and biases that we've been talking about. We have knowledge.

這就是科學(xué)理念，所以我們剛才說的那些觀念和偏見是真的毫無用處。我們擁有知識。

But think about this ... This is Houston, Texas, after Hurricane Harvey. Now, I write a contribution for "Forbes" magazine periodically, and I wrote an article a week before Hurricane Harvey made landfall, saying,

但是想想這個…這是颶風(fēng)哈維過后的德克薩斯州休斯頓?，F(xiàn)在，我定期為《福布斯》雜志撰稿，在颶風(fēng)哈維登陸前一周，我寫了一篇文章說，

"There's probably going to be 40 to 50 inches of rainfall." I wrote that a week before it happened. But yet, when you talk to people in Houston, people are saying, "We had no idea it was going to be this bad." I'm just...

“可能會有40到50英寸的降雨量。”我在它發(fā)生的前一周寫了這個文章。但是，當(dāng)你和休斯敦的人交談時，人們會說，“我沒想到會這么糟糕?！蔽抑荒堋?/p>

A week before. But -- I know, it's amusing, but the reality is, we all struggle with perceiving something outside of our experience level. People in Houston get rain all of the time, they flood all of the time.

整整提前了一周。但是——我知道這有點可笑，但現(xiàn)實是，讓我們理解經(jīng)驗水平之外的東西真的很困難。休斯頓的人總在經(jīng)歷下雨，雨水泛濫很平常。

But they've never experienced that. Houston gets about 34 inches of rainfall for the entire year. They got 50 inches in three days. That's an anomaly event, that's outside of the normal.

但他們從沒有遭受過那樣的情況。休斯頓全年降雨量約為34英寸。而那段時間，他們在3天內(nèi)遭受了50英寸。這是異常事件，超出了正常范圍。

So belief systems and biases, literacy and misinformation. How do we step out of the boxes that are cornering our perceptions? Well we don't even have to go to Houston, we can come very close to home.

所以信仰體系和偏見，文化素養(yǎng)和錯誤信息。我們?nèi)绾巫叱鲎笥椅覀冋J(rèn)知的框框?我們甚至不需要去休斯頓，在家附近就可以觀察到。

Remember "Snowpocalypse?" Snowmageddon? Snowzilla? Whatever you want to call it. All two inches of it. Two inches of snow shut the city of Atlanta down.

還記得“末日暴雪”嗎? 雪魔?雪巨人?不管你怎么稱呼她，都只有兩英寸的雪。兩英寸厚的雪就使亞特蘭大市癱瘓了。

But the reality is, we were in a winter storm watch, we went to a winter weather advisory, and a lot of people perceived that as being a downgrade, "Oh, it's not going to be as bad."

但事實是，我們在嚴(yán)防冬季風(fēng)暴，我們?nèi)チ硕咎鞖庾稍儥C(jī)構(gòu)，很多人都認(rèn)為雪災(zāi)會降級，“哦，不會那么糟的?！?/p>

When in fact, the perception was that it was not going to be as bad, but it was actually an upgrade. Things were getting worse as the models were coming in. So that's an example of how we get boxed in by our perceptions.

事實上，人們的感覺是，不會這么糟糕，但其實雪災(zāi)升級了。隨著模型的出現(xiàn)，情況在變得更糟。這就是我們被自己的認(rèn)知束縛的一個例子。

So, the question becomes, how do we expand our radius? The area of a circle is "pi r squared". We increase the radius, we increase the area. How do we expand our radius of understanding about science?

所以問題就變成了，我們?nèi)绾螖U(kuò)大我們的認(rèn)知半徑?圓的面積是πR的平方。我們增加半徑，就能增加面積。我們?nèi)绾螖U(kuò)大我們理解科學(xué)的半徑?

Here are my thoughts. You take inventory of your own biases. And I'm challenging you all to do that. Take an inventory of your own biases. Where do they come from? Your upbringing, your political perspective, your faith -- what shapes your own biases?

這是我的思考。你們列出自己的偏見。我想讓你們所有人都這么做。列出你們的偏見。它們來自哪里?你的教養(yǎng)，你的政治觀點，你的信仰——你自己的偏見是如何形成的?

Then, evaluate your sources where do you get your information on science? What do you read, what do you listen to, to consume your information on science? And then, it's important to speak out.

然后，評估你的信息來源——你在哪里獲取科學(xué)信息?你讀什么，你聽什么，什么是你獲得科學(xué)信息的來源?然后，重要的是說出來。

Talk about how you evaluated your biases and evaluated your sources. I want you to listen to this little 40-second clip from one of the top TV meteorologists in the US, Greg Fishel, in the Raleigh, Durham area.

談?wù)勀闳绾卧u估你的偏見和信息來源。我想讓你們聽聽這個40秒的小片段，來自美國頂尖的電視氣象學(xué)家之一，格雷格·費舍爾，他住在Durham的Raleigh地區(qū)。

He's revered in that region. But he was a climate skeptic. But listen to what he says about speaking out.

他在那個地區(qū)很受尊敬。但他是個氣候懷疑論者。但是聽聽他關(guān)于發(fā)聲是怎么說的。

Greg Fishel: The mistake I was making and didn't realize until very recently, was that I was only looking for information to support what I already thought, and was not interested in listening to anything contrary.

格雷格·費舍爾：“我犯過的錯誤，并且直到最近我才意識到的是，我只看那些能支撐我想法的信息，從來不對任何相反的信息感興趣。

And so I woke up one morning, and there was this question in my mind, "Greg, are you engaging in confirmation bias? Are you only looking for information to support what you already think?"

所以有一天早晨我醒來，腦海中有個問題，‘格雷格，你是不是陷入了確認(rèn)偏誤?你是不是只看那些支持你想法的信息?！?/p>

And if I was honest with myself, and I tried to be, I admitted that was going on.

如果我對自己誠實，也試圖對自己誠實，我得承認(rèn)是這樣的。

And so the more I talked to scientists and read peer-reviewed literature and tried to conduct myself the way I'd been taught to conduct myself at Penn State when I was a student, it became very difficult for me to make the argument that we weren't at least having some effect.

所以我和科學(xué)家交談的次數(shù)越多，閱讀同行評議的文獻(xiàn)越多，我也努力像我在賓夕法尼亞州立大學(xué)上學(xué)時被教導(dǎo)的那樣去要求自己，對我來說，就越難證明我們一點也沒有被影響。

Maybe there was still a doubt as to how much, but to say "nothing" was not a responsible thing for me to do as a scientist or a person.

也許，到底被影響了多少還是個疑問，但作為一個科學(xué)家或一個人，說‘一點也沒被影響’是一件不負(fù)責(zé)任的事情?！?/p>

JMS: Greg Fishel just talked about expanding his radius of understanding of science. And when we expand our radius, it's not about making a better future, but it's about preserving life as we know it.

JMS：格雷格·費舍爾剛剛在說，擴(kuò)大他認(rèn)知科學(xué)的半徑。當(dāng)我們擴(kuò)大我們的半徑時，不是為了創(chuàng)造一個更好的未來，而是為了保留我們所知的生活。

So as we think about expanding our own radius in understanding science, it's critical for Athens, Georgia, for Atlanta, Georgia, for the state of Georgia, and for the world. So expand your radius.

所以當(dāng)我們想要擴(kuò)大我們對科學(xué)的理解范圍時，這對喬治亞州的雅典和亞特蘭大，對喬治亞州和整個世界都很重要。所以，擴(kuò)大你的半徑吧!