What to trust in a "post-truth" world | Alex Edmans

譯者: Lilian Chiu 審譯者: SF Huang

Belle Gibson was a happy young Australian.

貝兒．吉布森曾經是一位 快樂的年輕澳洲人。

She lived in Perth, and she loved skateboarding.

她住在伯斯，喜歡玩滑板。

But in 2009, Belle learned that she had brain cancer and four months to live.

但 2009 年，貝兒得知她得了 腦瘤，只剩下 4 個月的生命。

Two months of chemo and radiotherapy had no effect.

兩個月的化療和放射線治療 都沒有效果。

But Belle was determined.

但貝兒意志很堅強。

She'd been a fighter her whole life.

她一輩子都是個鬥士。

From age six, she had to cook for her brother, who had autism,

6 歲時，她得幫自閉症的弟弟

and her mother, who had multiple sclerosis.

及多發性硬化症的母親煮飯。

Her father was out of the picture.

父親在她的生命中缺席。

So Belle fought, with exercise, with meditation

貝兒靠著運動和冥想，

and by ditching meat for fruit and vegetables.

並以蔬果代替肉類來抗癌。

And she made a complete recovery.

她完全復原了。

Belle's story went viral.

貝兒的故事被瘋傳。

It was tweeted, blogged about, shared and reached millions of people.

在推特和部落格中， 有數百萬人分享並流傳著。

It showed the benefits of shunning traditional medicine

它顯示出不用傳統醫學

而改用飲食和運動的益處。

for diet and exercise.

In August 2013, Belle launched a healthy eating app,

2013 年 8 月，貝兒推出了 一個健康飲食的應用程式

The Whole Pantry,

「健康廚房」，

downloaded 200,000 times in the first month.

首月就有 20 萬的下載人次。

But Belle's story was a lie.

但是，貝兒的故事是假的。

Belle never had cancer.

貝兒從來沒有得過癌症。

People shared her story without ever checking if it was true.

大家在分享她的故事時， 根本沒有先確認真假。

This is a classic example of confirmation bias.

這是個確認偏誤的典型例子。

We accept a story uncritically if it confirms what we'd like to be true.

如果一個故事符合 我們希望它為真的想法，

我們就會不加鑑別地接受它。

And we reject any story that contradicts it.

且我們會排斥任何與之對立的故事。

How often do we see this

在我們分享和忽略故事的時候，

in the stories that we share and we ignore?

有多常看到這樣的現象？

In politics, in business, in health advice.

在政治、商業、保健的建議中。

The Oxford Dictionary's word of 2016 was "post-truth."

牛津字典選出 2016 年的 年度詞彙是「後真相」。

And the recognition that we now live in a post-truth world

因為認知到我們現在的世界 是個後真相的世界，

has led to a much needed emphasis on checking the facts.

因此更需著眼在確認訊息是否屬實。

But the punch line of my talk

但，我這場演說的重點是： 僅確認是否屬實是不夠的。

is that just checking the facts is not enough.

Even if Belle's story were true,

即使貝兒的故事是真的，

it would be just as irrelevant.

那也不重要。

Why?

為什麼？

Well, let's look at one of the most fundamental techniques in statistics.

讓我們來看看統計學中 最基礎的技巧之一。

It's called Bayesian inference.

就是「貝氏推論」。

And the very simple version is this:

用非常簡單的方式來說明：

We care about "does the data support the theory?"

我們在乎「資料是否支持理論？」

Does the data increase our belief that the theory is true?

資料是否會增加 我們對於理論為真的信心？

But instead, we end up asking, "Is the data consistent with the theory?"

但，我們卻淪為在問： 「資料和理論一致嗎？」

But being consistent with the theory

但，資料和理論一致

does not mean that the data supports the theory.

並不表示資料就支持理論。

Why?

為什麼？

Because of a crucial but forgotten third term --

因為有個很關鍵卻被遺忘記的 第三個條件 ——

the data could also be consistent with rival theories.

資料也有可能和對立理論一致。

But due to confirmation bias, we never consider the rival theories,

但因為確認偏誤，我們從來 都不會去考量對立理論，

because we're so protective of our own pet theory.

因為我們是如此防護 自己特別鍾愛的理論。

Now, let's look at this for Belle's story.

我們來用貝兒的故事做說明。

Well, we care about: Does Belle's story support the theory

我們在乎：貝兒的故事能否支持

「飲食能治癒癌症」的理論？

that diet cures cancer?

But instead, we end up asking,

但，我們最後反而在問：

"Is Belle's story consistent with diet curing cancer?"

「貝兒的故事是否和 飲食能治癒癌症一致？」

And the answer is yes.

答案是「是」。

If diet did cure cancer, we'd see stories like Belle's.

如果飲食能治癒癌症， 我們就會看到像貝兒這樣的故事。

But even if diet did not cure cancer,

但即使飲食不能治癒癌症，

we'd still see stories like Belle's.

我們仍然會看到像貝兒這樣的故事。

A single story in which a patient apparently self-cured

像是一個病人顯然能夠自我治癒，

just due to being misdiagnosed in the first place.

僅因一開始她就被誤診的故事。

Just like, even if smoking was bad for your health,

就像即使抽菸會危害你的健康，

you'd still see one smoker who lived until 100.

你仍然能找到一個 活到 100 歲的老煙槍。

(Laughter)

（笑聲）

Just like, even if education was good for your income,

就像即使教育有益於你的收入，

you'd still see one multimillionaire who didn't go to university.

你仍然能找到一個沒有 大學學歷的大富豪。

(Laughter)

（笑聲）

So the biggest problem with Belle's story is not that it was false.

所以，貝兒的故事最大的問題 並不在於它是假的。

It's that it's only one story.

問題在於它只是單一個故事。

There might be thousands of other stories where diet alone failed,

可能還有幾千個光靠飲食 而失敗的故事，

but we never hear about them.

但我們從來沒有聽到這些故事。

We share the outlier cases because they are new,

我們會分享特例， 因為特例很新穎，

and therefore they are news.

因此，新穎就是新聞。

We never share the ordinary cases.

我們從來不會去分享一般的案例。

They're too ordinary, they're what normally happens.

它們太一般了。 日常生活中隨處可見。

And that's the true 99 percent that we ignore.

真實的 99 % 就這樣被我們忽略了。

Just like in society, you can't just listen to the one percent,

就像在社會中， 你不能只聽那 1% 的特例，

the outliers,

and ignore the 99 percent, the ordinary.

而忽略 99 % 的一般狀況。

Because that's the second example of confirmation bias.

因為那是確認偏誤的第二個例子。

We accept a fact as data.

我們接受事實作為資料。

The biggest problem is not that we live in a post-truth world;

最大的問題並不是我們身在 「後真相」的世界中；

it's that we live in a post-data world.

而是我們身在「後資料」的世界中。

We prefer a single story to tons of data.

相較於一大堆資料， 我們比較偏好單一個故事。

Now, stories are powerful, they're vivid, they bring it to life.

故事是很強大、很生動的， 它們是很活靈活現的。

They tell you to start every talk with a story.

人們說演講要用故事來當開場。

I did.

我就這麼做了。

But a single story is meaningless and misleading

但單一個故事沒有意義， 還會造成誤導，

unless it's backed up by large-scale data.

除非它背後有大規模的資料來支持。

But even if we had large-scale data,

但即使我們有大規模的資料，

that might still not be enough.

那可能還是不夠。

Because it could still be consistent with rival theories.

因為它仍可能和對立的理論一致。

Let me explain.

讓我解釋一下。

A classic study by psychologist Peter Wason

精神科醫生彼得．瓦森 有一項經典的研究，

gives you a set of three numbers

給你一組 3 個數字，

and asks you to think of the rule that generated them.

請你去思考產生出 這些數字的規則。

So if you're given two, four, six,

如果你拿到的數字是 2、4、6，

what's the rule?

規則是什麼？

Well, most people would think, it's successive even numbers.

大部分的人會想， 這是連續的偶數。

How would you test it?

你要如何測試它？

Well, you'd propose other sets of successive even numbers:

你會提出其他組的連續偶數：

4, 6, 8 or 12, 14, 16.

4、6、8 或 12、14、16。

And Peter would say these sets also work.

彼得會說，這幾組的確行得通。

But knowing that these sets also work,

但知道這幾組也行得通，

knowing that perhaps hundreds of sets of successive even numbers also work,

也許有數百組連續偶數的 數字也行得通，

tells you nothing.

並不能告訴你什麼。

Because this is still consistent with rival theories.

因為這仍然和對立理論一致。

Perhaps the rule is any three even numbers.

也許規則是任何 3 個偶數。

Or any three increasing numbers.

或任何 3 個越來越大的數字。

And that's the third example of confirmation bias:

那就是確認偏誤的第三例子：

accepting data as evidence,

接受資料作為證據，

even if it's consistent with rival theories.

即使資料和對立理論一致。

Data is just a collection of facts.

資料只是一大堆事實。

Evidence is data that supports one theory and rules out others.

支持單一個理論並排除其他 理論的資料，才叫做證據。

So the best way to support your theory

所以，若要支持你的理論，

is actually to try to disprove it, to play devil's advocate.

最好的方式就是證明它是錯的， 要盡可能地吹毛求疵。

So test something, like 4, 12, 26.

所以，要測試如 4、12、26 這樣的組合。

If you got a yes to that, that would disprove your theory

如果結果也是肯定的，

那麼你的連續偶數理論就不成立了。

of successive even numbers.

Yet this test is powerful,

但，這種測試是很強大的，

because if you got a no, it would rule out "any three even numbers"

因為如果你得到「否」， 就能排除「任何 3 個偶數」

和「任何 3 個越來越大的數字」。

and "any three increasing numbers."

It would rule out the rival theories, but not rule out yours.

對立的理論會被排除， 你的理論卻不會被排除。

But most people are too afraid of testing the 4, 12, 26,

但大部分人都太害怕 而不敢測試 4、12、26，

because they don't want to get a yes and prove their pet theory to be wrong.

因為他們不希望得到「是」， 來證明自己鍾愛的理論是錯的。

Confirmation bias is not only about failing to search for new data,

確認偏誤並不只是

未能尋找到新的資料，

but it's also about misinterpreting data once you receive it.

還包括你對接收到的資料 做出錯誤的判讀。

And this applies outside the lab to important, real-world problems.

這也適用在實驗室以外的 重要、真實世界的問題上。

Indeed, Thomas Edison famously said,

的確，愛迪生有句名言是說：

"I have not failed,

「我沒有失敗，

I have found 10,000 ways that won't work."

我只是找出一萬種行不通的方式。」

Finding out that you're wrong

發現你的錯誤

is the only way to find out what's right.

是找到真相的唯一方式。

Say you're a university admissions director

假設你是一間大學的招生部主任，

and your theory is that only students with good grades

你的理論是：只有來自富裕家庭的

績優學生才會有優良的表現。

from rich families do well.

So you only let in such students.

所以你只讓這種學生入學。

And they do well.

他們也的確表現優良。

But that's also consistent with the rival theory.

但這個狀況也和對立理論一致。

Perhaps all students with good grades do well,

也許所有成績好的學生 都會有優良的表現，

rich or poor.

不論富有或貧窮。

But you never test that theory because you never let in poor students

但你從來沒有測試那個理論， 因為你從來不讓貧窮的學生入學，

because you don't want to be proven wrong.

因為你不希望自己被證明是錯的。

So, what have we learned?

所以，我們學到了什麼？

A story is not fact, because it may not be true.

一個故事並不是事實， 因為它可能不是真的。

A fact is not data,

一個事實並不是資料，

it may not be representative if it's only one data point.

如果它只一個資料點， 它可能不具代表性。

And data is not evidence --

資料並不是證據 ——

it may not be supportive if it's consistent with rival theories.

如果它和對立理論一致， 它就不見得有支持的力道。

So, what do you do?

所以，你能怎麼做？

When you're at the inflection points of life,

如果你正處於人生中的轉捩點，

deciding on a strategy for your business,

要為你的事業決定一種策略，

a parenting technique for your child

要為你的孩子決定一種教養技巧，

or a regimen for your health,

或要為你的健康決定一種食物療法，

how do you ensure that you don't have a story

你要如何確保你所取得的

不是一個故事，而是證據？

but you have evidence?

Let me give you three tips.

讓我提供大家 3 個秘訣。

The first is to actively seek other viewpoints.

第一，主動尋求其他觀點。

Read and listen to people you flagrantly disagree with.

閱讀並傾聽你非常不贊同的人。

Ninety percent of what they say may be wrong, in your view.

在你看來，他們說的話 有 90% 可能都是錯的。

But what if 10 percent is right?

但如果有 10% 是對的呢？

As Aristotle said,

如亞里斯多德說的：

"The mark of an educated man

「一位有教養的人

is the ability to entertain a thought

是能夠包容一種想法，

without necessarily accepting it."

卻不見得一定要接受它。」

Surround yourself with people who challenge you,

和挑戰自己的人在一起，

and create a culture that actively encourages dissent.

創造出一種主動鼓勵別人 提出不同意見的文化。

Some banks suffered from groupthink,

有些銀行飽受團體迷思之苦，

where staff were too afraid to challenge management's lending decisions,

員工太害怕去挑戰 管理階層的借貸決策，

contributing to the financial crisis.

因而造成金融財務危機。

In a meeting, appoint someone to be devil's advocate

在會議中，指定一個人

去吹毛求疵你心愛的想法。

against your pet idea.

And don't just hear another viewpoint --

且不要只是去聽不同的觀點——

listen to it, as well.

而是要認真地聽進去。

As psychologist Stephen Covey said,

心理學家史蒂芬．柯維說：

"Listen with the intent to understand,

「抱著想要了解的意圖去傾聽，

not the intent to reply."

而非想回應的意圖。」

A dissenting viewpoint is something to learn from

可以從不同意的觀點中學習，

not to argue against.

並非盲目地反對它。

Which takes us to the other forgotten terms in Bayesian inference.

這就帶到了在貝氏推論中 其他被遺忘的條件。

Because data allows you to learn,

因為資料讓你能學習，

but learning is only relative to a starting point.

但學習只是個相對的起點。

If you started with complete certainty that your pet theory must be true,

如果你一開始就完全肯定

你特別鍾愛的理論是對的，

then your view won't change --

那麼你的看法不會改變——

regardless of what data you see.

不論你看見什麼資料。

Only if you are truly open to the possibility of being wrong

只有當你放開心胸接受 自己有犯錯的可能時，

can you ever learn.

你才能學習。

As Leo Tolstoy wrote,

如托爾斯泰所寫的：

"The most difficult subjects

「最困難的問題

can be explained to the most slow-witted man

能夠解釋給最遲鈍的人瞭解，

if he has not formed any idea of them already.

只要他沒有任何先入為主的概念。

But the simplest thing

但最簡單的事，

cannot be made clear to the most intelligent man

反而無法對最睿智的人說明清楚，

if he is firmly persuaded that he knows already."

如果他堅信自身已經知道了答案。」

Tip number two is "listen to experts."

秘訣二是「聽專家的」。

Now, that's perhaps the most unpopular advice that I could give you.

這可能是我所能給你的建議當中 最不受歡迎的了。

(Laughter)

（笑聲）

British politician Michael Gove famously said that people in this country

英國政治家麥可戈夫有句名言是：

這國家的人民已經受夠了專家。

have had enough of experts.

A recent poll showed that more people would trust their hairdresser --

一項近期的調查顯示， 更多人選擇相信他們的理髮師——

(Laughter)

（笑聲）

or the man on the street

或街上的路人，

than they would leaders of businesses, the health service and even charities.

勝過相信企業領導人、 保健服務甚至是慈善事業。

So we respect a teeth-whitening formula discovered by a mom,

所以，我們重視某個媽媽 發現的牙齒美白配方，

or we listen to an actress's view on vaccination.

或會聽女演員對於接種疫苗的看法。

We like people who tell it like it is, who go with their gut,

我們喜歡那些有話直說、 憑著直覺走的人，

and we call them authentic.

我們會說這些人很真。

But gut feel can only get you so far.

但直覺沒辦法帶你走到多遠。

Gut feel would tell you never to give water to a baby with diarrhea,

直覺會告訴你， 千萬不要給腹瀉的寶寶喝水，

because it would just flow out the other end.

因為喝下去的水就只會被拉出來。

Expertise tells you otherwise.

專家意見卻是相反的。

You'd never trust your surgery to the man on the street.

如果事關你本人要動的手術， 你不會信任街上的路人。

You'd want an expert who spent years doing surgery

你會想要有位手術經驗豐富

且技術優異的專業醫師。

and knows the best techniques.

But that should apply to every major decision.

但那該應用在所有重大的決策上。

Politics, business, health advice

政治、商業、保健的建議

require expertise, just like surgery.

都需要專家，和動手術一樣。

So then, why are experts so mistrusted?

那麼，為什麼專家如此不被信任？

Well, one reason is they're seen as out of touch.

嗯，其中一個理由是， 他們似乎被認為和群眾脫節。

A millionaire CEO couldn't possibly speak for the man on the street.

百萬富翁執行長 不可能為街上的人發聲。

But true expertise is found on evidence.

但真正的專家是基於證據說話的。

And evidence stands up for the man on the street

證據會支持捍衛街上的人，

and against the elites.

並對抗精英。

Because evidence forces you to prove it.

因為證據會強迫你去證明它。

Evidence prevents the elites from imposing their own view

證據讓精英在沒有佐證的情況下，

無法將自己的想法強加在他人身上。

without proof.

A second reason why experts are not trusted

專家不被信任的第二個理由，

is that different experts say different things.

是因為不同的專家，所說各有不同。

For every expert who claimed that leaving the EU would be bad for Britain,

只要有專家聲稱 脫離歐盟對英國不是好事，

another expert claimed it would be good.

就會有其他專家聲稱這是好事。

Half of these so-called experts will be wrong.

這些所謂的專家，有半數會是錯的。

And I have to admit that most papers written by experts are wrong.

我必須承認專家寫的論文， 大部分是錯的。

Or at best, make claims that the evidence doesn't actually support.

充其量，他們會做出 證據不見得支持的一些主張。

So we can't just take an expert's word for it.

所以，我們不能 就這樣相信專家的話。

In November 2016, a study on executive pay hit national headlines.

2016 年 11 月，

一項關於主管薪資的研究 上了全國的頭條。

Even though none of the newspapers who covered the study

儘管報導這項研究的報社， 壓根沒看過該項研究。

had even seen the study.

It wasn't even out yet.

它甚至尚未發表出刊。

They just took the author's word for it,

它們只是相信了作者的話，

just like with Belle.

就像貝兒的例子。

Nor does it mean that we can just handpick any study

那並不表示我們可以挑選任何

that happens to support our viewpoint --

剛好支持我們觀點的研究——

that would, again, be confirmation bias.

同樣的，那也是確認偏誤。

Nor does it mean that if seven studies show A

那也不表示，如果 有 7 項研究顯示是 A，

and three show B,

3 項顯示是 B，

that A must be true.

則 A 就一定是對的。

What matters is the quality,

重要的是專家意見的品質，

and not the quantity of expertise.

而不是數量。

So we should do two things.

所以，我們應該要做兩件事。

First, we should critically examine the credentials of the authors.

第一，我們應該很嚴苛地 檢驗作者的資歷。

Just like you'd critically examine the credentials of a potential surgeon.

就像你會嚴苛地檢驗 準外科醫生的資歷一樣。

Are they truly experts in the matter,

他們真的是那方面的專家？

or do they have a vested interest?

或是他們有著既得利益？

Second, we should pay particular attention

第二，我們應該要特別注意

to papers published in the top academic journals.

在頂尖學術期刊中的論文。

Now, academics are often accused of being detached from the real world.

學術圈常常被批評脫離真實世界。

But this detachment gives you years to spend on a study.

但這樣的脫離，讓你可以 花數年的時間投入一項研究。

To really nail down a result,

得出一個確切的結果，

to rule out those rival theories,

把那些對立理論給排除，

and to distinguish correlation from causation.

區別出相關性和因果關係。

And academic journals involve peer review,

學術期刊需要同儕審查，

where a paper is rigorously scrutinized

論文會被嚴格地仔細審查，

(Laughter)

（笑聲）

by the world's leading minds.

被世界上最有聰明才智的人檢查。

The better the journal, the higher the standard.

越好的期刊，標準越高。

The most elite journals reject 95 percent of papers.

最優秀的期刊 會退回 95% 的論文。

Now, academic evidence is not everything.

學術證據並不代表一切。

Real-world experience is critical, also.

真實世界的經驗也很重要。

And peer review is not perfect, mistakes are made.

同儕審查並不完美， 也會有錯誤。

But it's better to go with something checked

但有檢查總比沒檢查好。

than something unchecked.

If we latch onto a study because we like the findings,

如果我們偏好一篇研究 是因為我們喜歡它的研究結果，

without considering who it's by or whether it's even been vetted,

而沒考量作者為誰或是否經過檢驗，

there is a massive chance that that study is misleading.

那這篇研究就很有可能造成誤導。

And those of us who claim to be experts

我們當中宣稱自己是專家的人

should recognize the limitations of our analysis.

應該知道我們的分析是有極限的。

Very rarely is it possible to prove or predict something with certainty,

能夠確切地證明或預測 某樣事物是極罕見的情況，

yet it's so tempting to make a sweeping, unqualified statement.

但做出一概而論的陳述 卻是如此地誘人。

It's easier to turn into a headline or to be tweeted in 140 characters.

轉換成頭條或是用 140 個字 寫在推特上，是比較容易的。

But even evidence may not be proof.

但即使證據也不見得能證明什麼。

It may not be universal, it may not apply in every setting.

它可能無法放諸四海而皆準， 它不見得適用在任何情況。

So don't say, "Red wine causes longer life,"

所以不要說「紅酒能延壽」，

when the evidence is only that red wine is correlated with longer life.

因為證據只是顯示紅酒 和長壽有相關性而已。

And only then in people who exercise as well.

且只限於同時也在運動的人才會。

Tip number three is "pause before sharing anything."

秘訣三是 「分享任何東西之前，請三思。」

The Hippocratic oath says, "First, do no harm."

醫科學生的誓約說道： 「首先，不要造成傷害。」

What we share is potentially contagious,

我們分享的內容有可能會擴散蔓延，

so be very careful about what we spread.

所以要格外小心我們所發散的內容。

Our goal should not be to get likes or retweets.

我們的目標不應該是 要得到「讚」或被轉推。

Otherwise, we only share the consensus; we don't challenge anyone's thinking.

不然，我們就只是在分享共識， 沒有去挑戰別人的想法。

Otherwise, we only share what sounds good,

不然，我們就只是分享 聽起來很棒的內容，

regardless of whether it's evidence.

不管它是不是證據。

Instead, we should ask the following:

反之，我們應該要問下列問題：

If it's a story, is it true?

如果它是一個故事，它是真的嗎？

If it's true, is it backed up by large-scale evidence?

如果它是真的， 有大規模的證據來支持它嗎？

If it is, who is it by, what are their credentials?

如果有，證據是誰提的？ 他們的背景資歷為何？

Is it published, how rigorous is the journal?

它發表了嗎？ 這個期刊有多嚴謹？

And ask yourself the million-dollar question:

並且問你自己這個 重要但難答的問題：

If the same study was written by the same authors with the same credentials

如果同樣資格的同一位作者 寫了同樣的研究，

but found the opposite results,

但研究的發現卻是相反的，

would you still be willing to believe it and to share it?

你仍然願意相信並分享它嗎？

Treating any problem --

處理任何問題 ——

a nation's economic problem or an individual's health problem,

一個國家的經濟問題 或者個人的健康問題 ——

is difficult.

是很困難的。

So we must ensure that we have the very best evidence to guide us.

所以我們必須確保 有最佳的證據來引導我們。

Only if it's true can it be fact.

只有真的，才能成為事實。

Only if it's representative can it be data.

只有具代表性，才能成為資料。

Only if it's supportive can it be evidence.

只有具支持性，才能成為證據。

And only with evidence can we move from a post-truth world

只有證據， 才能讓我們從後真相的世界

to a pro-truth world.

走向擁抱真相的世界。

Thank you very much.

非常謝謝。

(Applause)

（掌聲）