3 kinds of bias that shape your worldview | J. Marshall Shepherd

譯者: Lilian Chiu 審譯者: congmei Han

I'm a meteorologist by degree,

從學位上來看，我是氣象學家，

I have a bachelor's, master's and PhD in physical meteorology,

我有物理氣象學的 學士、碩士、博士學位，

so I'm a meteorologist, card carrying.

所以我是氣象學家，有認證的。

And so with that comes four questions, always.

這總是會伴隨著四個問題。

This is one prediction I will always get right.

我的這項預測是最準的。

(Laughter)

（笑聲）

And those questions are,

那幾個問題是：

"Marshall, what channel are you on?"

「馬歇爾，你在哪一台播氣象？」

(Laughter)

（笑聲）

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

「薛佛博士，明天的天氣如何？」

(Laughter)

（笑聲）

And oh, I love this one:

喔，我很愛這一個：

"My daughter is getting married next September, it's an outdoor wedding.

「我女兒明年九月要結婚，

是戶外婚禮。到時會下雨嗎？」

Is it going to rain?"

(Laughter)

（笑聲）

Not kidding, I get those, and I don't know the answer to that,

不是開玩笑的，我會被問 這些問題，但我沒有答案，

the science isn't there.

那沒有科學。

But the one I get a lot these days is,

但最近我最常被問的是這個：

"Dr. Shepherd, do you believe in climate change?"

「薛佛博士，你相信 氣候變遷嗎？」

"Do you believe in global warming?"

「你相信全球暖化嗎？」

Now, I have to gather myself every time I get that question.

每當我被問那個問題時， 我就得打起精神。

Because it's an ill-posed question --

因為這是個不適定的問題—— 科學並不是個信念系統。

science isn't a belief system.

My son, he's 10 -- he believes in the tooth fairy.

我的兒子十歲——他相信 有牙仙（會把牙齒換成金幣）。

And he needs to get over that, because I'm losing dollars, fast.

他最好快點度過這段時期， 因為我虧錢虧得很快。

(Laughter)

（笑聲）

But he believes in the tooth fairy.

但他相信有牙仙。

But consider this.

但，想想這一點。

Bank of America building, there, in Atlanta.

美國銀行大樓，位在亞特蘭大。

You never hear anyone say,

你從來不會聽到有人說：

"Do you believe, if you go to the top of that building

「你信不信，如果到 那棟大樓的樓頂，

and throw a ball off, it's going to fall?"

把一顆球丟出去， 它就會向下落？」

You never hear that, because gravity is a thing.

你從來沒有聽過， 因為重力是客觀存在。

So why don't we hear the question,

所以我們不會聽到這個問題：

"Do you believe in gravity?"

「你相信重力嗎？」

But of course, we hear the question,

但，當然，我們會聽到這個問題：

"Do you believe in global warming?"

「你相信全球暖化嗎？」

Well, consider these facts.

想想看這些事實。

The American Association for the Advancement of Science, AAAS,

美國科學促進會， 縮寫 AAAS，

one of the leading organizations in science,

是科學界最重要的組織之一，

queried scientists and the public on different science topics.

詢問科學家和大眾 各種不同的科學主題。

Here are some of them:

以下是其中一些：基因改造食物、

genetically modified food, animal research, human evolution.

動物研究、人類演化。

And look at what the scientists say about those,

看看科學家們對這些主題的說法，

the people that actually study those topics, in red,

真正在研究那些 主題的人是紅色的，

versus the gray, what the public thinks.

相對的，灰色是大眾的想法。

How did we get there?

我們是怎麼走到這一步的？

How did we get there?

我們是怎麼走到這一步的？

That scientists and the public are so far apart on these science issues.

在這些科學議題上， 科學家和大眾的認知差好多。

Well, I'll come a little bit closer to home for me,

我挑個我比較熟悉的主題來談：

climate change.

氣候變遷。

Eighty-seven percent of scientists

87% 的科學家

believe that humans are contributing to climate change.

相信人類造成了氣候變遷。

But only 50 percent of the public?

但只有 50% 的民眾這麼想？

我們是怎麼走到這一步的？

How did we get there?

So it begs the question,

這就帶出了一個問題：

what shapes perceptions about science?

關於科學的認知，是怎麼來的？

It's an interesting question

這是個很有意思的問題， 我花了不少時間在思考它。

and one that I've been thinking about quite a bit.

I think that one thing that shapes perceptions in the public, about science,

我認為，大眾對於科學的認知，

是由信念系統和偏見所形塑的。

is belief systems and biases.

Belief systems and biases.

信念系統和偏見。

Go with me for a moment.

耐心聽我說一下。

Because I want to talk about three elements of that:

因為我想要談它的三項元素：

confirmation bias, Dunning-Kruger effect

確認偏誤、

達克效應，

and cognitive dissonance.

以及認知失調。

Now, these sound like big, fancy, academic terms, and they are.

這些聽起來是很博大、 很炫的學術名詞，的確是的。

But when I describe them, you're going to be like, "Oh!

但當我描述它們時， 你們會說類似：「喔！

I recognize that; I even know somebody that does that."

我知道，我甚至認識 有這種狀況的人。」

Confirmation bias.

確認偏誤。

Finding evidence that supports what we already believe.

找證據來支持我們已經相信的事。

Now, we're probably all a little bit guilty of that at times.

我們可能難免有時 都會有一點確認偏誤。

Take a look at this.

看看這個。

I'm on Twitter.

我在推特上。

And often, when it snows,

通常，下雪時，

I'll get this tweet back to me.

我會收到這樣的回覆。

(Laughter)

（笑聲）

"Hey, Dr. Shepherd, I have 20 inches of global warming in my yard,

「嘿，薛佛博士，我的院子裡 有二十英吋的全球暖化，

what are you guys talking about, climate change?"

你們在說的是什麼？氣候變遷？」

I get that tweet a lot, actually.

其實，我常收到這種推特訊息。

It's a cute tweet, it makes me chuckle as well.

這種訊息很可愛，會讓我咯咯笑。

But it's oh, so fundamentally scientifically flawed.

但，它在根本上， 有很大的科學瑕疵。

Because it illustrates

因為它說明了寫這則 推特訊息的人並不了解

that the person tweeting doesn't understand

the difference between weather and climate.

天氣和氣候之間的差別。

I often say, weather is your mood

我常說，天氣是你的心情，

and climate is your personality.

氣候是你的個性。

Think about that.

想想看。天氣是你的心情， 氣候是你的個性。

Weather is your mood, climate is your personality.

Your mood today doesn't necessarily tell me anything about your personality,

你今天的心情不見得 能代表你的個性，

nor does a cold day tell me anything about climate change,

有一天很冷，並不表示 就有氣候變遷，

or a hot day, for that matter.

有一天很熱也是一樣的。

Dunning-Kruger.

達克效應。

Two scholars from Cornell came up with the Dunning-Kruger effect.

康乃爾大學的兩位學者 提出了達克效應。

If you go look up the peer-reviewed paper for this,

若你去找相關的同儕審查論文，

你會看到各式各樣 很炫的專有名詞：

you will see all kinds of fancy terminology:

it's an illusory superiority complex, thinking we know things.

它是一種虛幻的優越情節， 認為我們什麼都知道。

In other words, people think they know more than they do.

換言之，「認為自己知道的」 比「真正知道的」多。

Or they underestimate what they don't know.

或是說低估了自己不知道的。

And then, there's cognitive dissonance.

接著，還有認知失調。

Cognitive dissonance is interesting.

認知失調很有趣。

We just recently had Groundhog Day, right?

我們才剛過了土撥鼠節，對吧？

Now, there's no better definition of cognitive dissonance

認知失調最好的定義就是

than intelligent people asking me if a rodent's forecast is accurate.

有智慧的人問我齧齒目 動物的預測是否正確。

(Laughter)

（笑聲）

But I get that, all of the time.

但常常會有人問我這個問題。

(Laughter)

（笑聲）

But I also hear about the Farmer's Almanac.

但我也聽過農民曆。

We grew up on the Farmer's Almanac, people are familiar with it.

我們是看農民曆長大的， 大家很熟悉它。

The problem is, it's only about 37 percent accurate,

問題是， 它只有 37% 的正確率，

according to studies at Penn State University.

這是賓夕法尼亞州立大學 研究出來的數據。

But we're in an era of science

但我們所處的科學時代，

where we actually can forecast the weather.

是可以正確預測天氣的。

And believe it or not, and I know some of you are like, "Yeah, right,"

信不信由你，我知道 有人在想「最好是啦」。

在天氣預測上，我們可以 達到 90% 以上的正確率。

we're about 90 percent accurate, or more, with weather forecast.

你們只是傾向會記得偶爾 才發生的錯誤預測，真的。

You just tend to remember the occasional miss, you do.

(Laughter)

（笑聲）

So confirmation bias, Dunning-Kruger and cognitive dissonance.

所以，確認偏誤、 達克效應，和認知失調。

I think those shape biases and perceptions that people have about science.

我認為這些元素造成了 對於科學的偏見和認知。

But then, there's literacy and misinformation

但，還有識字能力和錯誤訊息，

that keep us boxed in, as well.

會讓我們的所知受限。

During the hurricane season of 2017,

在 2017 年的颶風季，

media outlets had to actually assign reporters

媒體管道真的有指派記者

to dismiss fake information about the weather forecast.

去排除關於天氣預測的假資訊。

That's the era that we're in.

那就是我們所處的時代。

I deal with this all the time in social media.

我總是要在社交媒體上 處理這種事。

Someone will tweet a forecast --

有人會在推特上發佈預測—— 那是颶風艾瑪的預測，

that's a forecast for Hurricane Irma, but here's the problem:

但有個問題：這個預測 並非來自國家颶風中心。

it didn't come from the Hurricane Center.

But people were tweeting and sharing this; it went viral.

但大家就不斷轉推和分享 這個預測；它被瘋傳。

It didn't come from the National Hurricane Center at all.

它完全不是來自國家颶風中心的。

So I spent 12 years of my career at NASA

我的職涯中，有十二年 是在太空總署，

before coming to the University of Georgia,

後來才到喬治亞大學，

and I chair their Earth Science Advisory Committee,

我在他們的地球科學 諮詢委員會當主席，

I was just up there last week in DC.

我上週才到華盛頓特區。

And I saw some really interesting things.

我看到了一些很有趣的事。

Here's a NASA model and science data from satellite

這是太空總署的模型， 以及來自衛星的資料，

showing the 2017 hurricane season.

呈現出來的 是 2017 年的颶風季。

You see Hurricane Harvey there?

有看到那裡的颶風哈維嗎？

Look at all the dust coming off of Africa.

看看所有從非洲來的塵土。

Look at the wildfires up in northwest US and in western Canada.

看看美國西北部 和加拿大西部的野火。

There comes Hurricane Irma.

颶風艾瑪來了。

This is fascinating to me.

這對我來說很迷人。

But admittedly, I'm a weather geek.

但，必須要承認， 我是個天氣怪咖。

But more importantly, it illustrates that we have the technology

但，更重要的是， 它說明了我們不僅有

to not only observe the weather and climate system,

可以觀察天氣和氣候系統的 科技，也能做預測。

but predict it.

There's scientific understanding,

這裡有科學上的了解，

so there's no need for some of those perceptions and biases

所以就不需要我們先前談的

that we've been talking about.

那些認知和偏見。

We have knowledge.

我們有知識。但，想想看……

But think about this ...

This is Houston, Texas, after Hurricane Harvey.

這是颶風哈維過後的德州休士頓。

Now, I write a contribution for "Forbes" magazine periodically,

我定期會為《富比士》雜誌寫稿，

and I wrote an article a week before Hurricane Harvey made landfall, saying,

在颶風哈維登陸前一週， 我寫了一篇文章，說：

"There's probably going to be 40 to 50 inches of rainfall."

「可能會有四十 到五十英吋的降雨。」

I wrote that a week before it happened.

這是在發生前一週寫的。

But yet, when you talk to people in Houston,

但，當你和休士頓的人談話時，

people are saying, "We had no idea it was going to be this bad."

他們會說：「我們完全 不知道這次會這麼糟。」

I'm just...

我只是……

(Sigh)

（嘆氣）

(Laughter)

（笑聲）

A week before.

一週前。

But --

但——

I know, it's amusing, but the reality is,

我知道，這很有趣，但現實是，

we all struggle with perceiving something outside of our experience level.

我們都很難認知

在我們經驗層級以外的東西。

People in Houston get rain all of the time,

休士頓的人常常遇到下雨，

they flood all of the time.

常常有水災。

But they've never experienced that.

但他們從來沒有經驗過那種災難。

Houston gets about 34 inches of rainfall for the entire year.

休士頓整年的降雨量 大約是三十四英吋。

They got 50 inches in three days.

三天內，降雨共五十英吋。

That's an anomaly event, that's outside of the normal.

那是件異常事件，並非正常的。

So belief systems and biases, literacy and misinformation.

所以，信念系統和偏見， 識字能力和錯誤資訊。

How do we step out of the boxes that are cornering our perceptions?

要如何爬出限制我們認知的井底？

Well we don't even have to go to Houston, we can come very close to home.

我們甚至不用到休士頓， 我們可以到離家很近的地方。

(Laughter)

（笑聲）

Remember "Snowpocalypse?"

記得「雪界末日 （改自世界末日）嗎」？

(Laughter)

（笑聲）

Snowmageddon?

末日暴雪（改自末日大戰）？

Snowzilla?

雪吉拉（改自哥吉拉）？ 不論你怎麼稱呼它。

Whatever you want to call it.

All two inches of it.

積雪總共有兩英吋。

(Laughter)

（笑聲）

Two inches of snow shut the city of Atlanta down.

兩英吋的積雪， 讓亞特蘭大市關閉。

(Laughter)

（笑聲）

But the reality is, we were in a winter storm watch,

但現實是當時我們 正處於冬季風暴中。

we went to a winter weather advisory,

我們發布寒冬天氣預報。

and a lot of people perceived that as being a downgrade,

很多人認為說得太嚴重了：

"Oh, it's not going to be as bad."

「喔，不會那麼糟的。」

當他們的認知是「不會那麼糟」，

When in fact, the perception was that it was not going to be as bad,

but it was actually an upgrade.

事實卻是狀況更新成「更為嚴峻」。

Things were getting worse as the models were coming in.

隨著新到的模型，一切都在惡化。

So that's an example of how we get boxed in by our perceptions.

那就是我們被認知 困在井底的一個例子。

So, the question becomes,

所以，問題變成了：

how do we expand our radius?

我們要如何擴展我們的半徑？

The area of a circle is "pi r squared".

圓的面積是「π r 平方」。

We increase the radius, we increase the area.

我們若能增加半徑， 就能增加面積。

How do we expand our radius of understanding about science?

我們要如何擴展我們 在了解科學方面的半徑？

Here are my thoughts.

以下是我的想法。

You take inventory of your own biases.

你把你自己的偏見盤點一下。

And I'm challenging you all to do that.

我挑戰各位去做這件事。

Take an inventory of your own biases.

把你自己的偏見盤點一下。

Where do they come from?

它們是從哪裡來的？

Your upbringing, your political perspective, your faith --

你的養育過程、 你的政治觀點、你的信仰——

what shapes your own biases?

你自己的偏見是由什麼形成的？

Then, evaluate your sources --

接著，評估你的資訊來源——

where do you get your information on science?

你從哪裡取得 那些關於科學的資訊？

What do you read, what do you listen to,

你會讀什麼、你會聽什麼，

to consume your information on science?

來取得關於科學的資訊？

And then, it's important to speak out.

接著，很重要的是要說出來。

Talk about how you evaluated your biases and evaluated your sources.

談談你如何評估 你的偏見以及你的來源。

I want you to listen to this little 40-second clip

我想請各位聽聽 這一小段影片，只有四十秒，

from one of the top TV meteorologists in the US, Greg Fishel,

美國最頂尖的電視氣象學家之一，

北卡羅萊納州，羅利達拉姆 三角區的格雷格費雪爾。

in the Raleigh, Durham area.

He's revered in that region.

他在那個地區倍受推崇。 但他是個氣候懷疑論者。

But he was a climate skeptic.

But listen to what he says about speaking out.

但聽聽他對於「說出來」怎麼說。

Greg Fishel: The mistake I was making

格雷格費雪爾：我所犯下的錯誤，

and didn't realize until very recently,

且一直到最近才發現，

was that I was only looking for information

就是我只有針對我既有的想法

to support what I already thought,

來尋找支持的資訊，

and was not interested in listening to anything contrary.

且我沒興趣傾聽任何相反資訊。

And so I woke up one morning,

所以，有天早上我醒來時，

and there was this question in my mind,

腦中有一個問題：

"Greg, are you engaging in confirmation bias?

「格雷格，你是否有確認偏誤？

Are you only looking for information to support what you already think?"

你是否只在尋找 支持你想法的資訊？」

And if I was honest with myself, and I tried to be,

若我對自己很誠實， 且我有試著這麼做，

I admitted that was going on.

我會承認我的確有這個狀況。

And so the more I talked to scientists

所以，我和越多科學家談話，

and read peer-reviewed literature

閱讀越多同儕審查論文，

and tried to conduct myself the way I'd been taught to conduct myself

並照我學生時期 在賓夕法尼亞州立大學

at Penn State when I was a student,

被教導的方式來做人做事，

it became very difficult for me to make the argument

我就越難主張說

that we weren't at least having some effect.

我們沒有受到絲毫影響。

Maybe there was still a doubt as to how much,

也許還無法確定影響有多少，

but to say "nothing" was not a responsible thing for me to do

但對我來說，說「沒有」 是很不負責的，

as a scientist or a person.

不論以一個科學家 或一個人的身分都一樣。

JMS: Greg Fishel just talked about expanding his radius

講者：格雷格費雪爾在說的，

就是擴展他了解科學的半徑。

of understanding of science.

And when we expand our radius,

當我們能擴展我們的半徑時，

it's not about making a better future,

重點並不在於讓未來更好，

but it's about preserving life as we know it.

而在於保存生命現有的狀態。

So as we think about expanding our own radius in understanding science,

所以，當我們想著要擴展 我們了解科學的半徑時，

it's critical for Athens, Georgia, for Atlanta, Georgia,

這對於喬治亞州的雅典、 喬治亞州的亞特蘭大、

for the state of Georgia, and for the world.

整個喬治亞州，及全世界，都很重要。

So expand your radius.

所以，擴展你們的半徑吧。

Thank you.

謝謝。

(Applause)

（掌聲）