Name: 【TED-Ed】我們是否該相信一致性的決定 (Should you trust unanimous decisions? - Derek Abbott)
Uploaded: 2016-09-21T23:00:00.000Z
Duration: 4 min 3 s

Imagine a police lineup where ten witnesses are asked to identify a bank robber they glimpsed fleeing the crime scene.

想像警察讓數人排成一排，讓十位目擊者指出看到的、逃離犯罪現場的銀行搶匪

關係子句

If six of them pick out the same person, there's a good chance that's the real culprit, and if all ten make the same choice, you might think the case is rock-solid, but you'd be wrong.

假如其中六個人挑出同一個人，表示那個人是元兇的機率很大，而且，假設十個人全都做了同樣的選擇，你也許會認為這案子大勢已定，但你錯了!

For most of us,  this sounds pretty strange.

對我們大多數人來說，這聽起來很奇怪。

After all, much of our society relies on majority vote and consensus, whether it's politics, business, or entertainment.

畢竟，我們的社會很多是依賴於多數表決和共識，不論是有關政治、商業或是娛樂。

So it's natural to think that more consensus is a good thing.

因此，很自然地會想說更多的共識是件好事

And up until a certain point, it usually is.

直到某個情況出現前，通常來說是如此沒錯

But sometimes, the closer you start to get to total agreement, the less reliable the result becomes.

但有時候，愈開始接近達到全體同意，出來的結果反而愈不可靠

The key to understanding this apparent paradox is in considering the overall level of uncertainty involved in the type of situation you're dealing with.

了解這個明顯矛盾的關鍵，在於考慮涉及到你正在處理的情況類型，不確定性的整體水平。

If we asked witnesses to identify the apple in this lineup, for example, we shouldn't be surprised by a unanimous verdict.

舉例來說，假設我們要求見證人指出這一排中的蘋果，我們應該不會對一致性的裁決感到驚訝。

But in cases where we have reason to expect some natural variance, we should also expect varied distribution.

但是，在我們有理由預期一些自然變異的案例中，我們也應該預期有不同的分配。

If you toss a coin one hundred times, you would expect to get heads somewhere around 50% of the time.

假如你投擲一枚硬幣一百次，你會預期大約有百分之五十的機會，會得到人頭那一面。

But if your results started to approach 100% heads, you'd suspect that something was wrong, not with your individual flips, but with the coin itself.

但是，假如你的結果開始接近百分之百都是人頭那一面的話，你會懷疑事情不太對勁，不是跟你個人彈硬幣有關，而是跟硬幣本身有關。

Of course, suspect identifications aren't as random as coin tosses, but they're not as clear-cut as telling apples from bananas, either.

當然，嫌疑犯的辨識不像投擲硬幣那樣隨機，但他們也不像從一排香蕉中找出蘋果那麼明確。

In fact, a 1994 study found that up to 48% of witnesses tend to pick the wrong person out of a lineup, even when many are confident in their choice.

事實上，一份 1994 年的研究發現有高達百分之四十八的見證人，傾向從行列中挑選出錯的人，即使許多人對他們的選擇很有自信。

Memory based on short glimpses can be unreliable, and we often overestimate our own accuracy.

建立在短暫匆匆一瞥的記憶可能不太可靠

Knowing all this, a unanimous identification starts to seem less like certain guilt, and more like a systemic error, or bias in the lineup.

知道這一切，一致性辨識開始看起來不太像一定有罪，反而比較像系統性誤差，或是對行列中的人帶有偏見。

And systemic errors don't just appear in matters of human judgement.

而系統性誤差不只出現在人為判斷的事件中

From 1993-2008, the same female DNA was found in multiple crime scenes around Europe, incriminating an elusive killer dubbed the Phantom of Heilbronn.

從 1993 年到 2008 年，在歐洲多個犯罪現場中，發現了相同的女性 DNA（脫氧核糖核酸），歸罪於一位神出鬼沒、被稱為「海爾布隆幽靈」的殺人犯。

But the DNA evidence was so consistent precisely because it was wrong.

但是 DNA 證據如此一致，正是因為它是錯的

It turned out that the cotton swabs used to collect the DNA samples had all been accidentally contaminated by a woman working in the swab factory.

結果發現，用來蒐集 DNA 樣本的棉籤，全都被一位在棉籤工廠工作的女士不慎意外汙染

In other cases, systematic errors arise through deliberate fraud, like the presidential referendum held by Saddam Hussein in 2002, which claimed a turnout of 100% of voters with all 100% supposedly voting in favor of another seven-year term.

在其他的案件中，系統誤差經由舞弊情況出現，像是在 2002 年，由 Saddam Hussein 舉行的總統全民公投，宣稱拿到百分之百的投票率和百分之百的投票支持，成功獲得下一個七年總統任期。

When you look at it this way, the paradox of unanimity isn't actually all that paradoxical.

當你這樣看時，一致性矛盾並不總是那麼矛盾。

Unanimous agreement is still theoretically ideal, especially in cases when you'd expect very low odds of variability and uncertainty, but in practice, achieving it in situations where perfect agreement is highly unlikely, should tell us that there's probably some hidden factor affecting the system.

一致同意仍然是理論上的理想，尤其是那些你預期變異性和不確定性機率很低的案例，但實際上，不大可能達成完美同意的情況，告訴我們也許有影響系統的一些隱藏因素。

Although we may strive for harmony and consensus, in many situations, error and disagreement should be naturally expected.

儘管我們會去爭取和諧和共識，在許多情況下，錯誤和不一致是再自然不過的事。

And if a perfect result seems too good to be true, it probably is.

假如有一個完美的結果看起來太過美好，不像是真的，它也許就不是真的。