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Imagine a police lineup where ten witnesses are asked to identify a bank robber they glimpsed fleeing the crime scene.
想象一个“列队指认”，十位目击者被要求指认他们瞥见的逃离犯罪现场的银行抢劫犯。
If six of them pick out the same person, there's a good chance that's the real culprit,
如果其中6个指向了同一个人，那这就是很有可能是真正的罪犯，
and if all ten make the same choice, you might think the case is rock solid, but you'd be wrong.
如果十个人都做出相同的选择，你可能会认为这是毋庸置疑的了，但是你也许错了。
For most of us, this sounds pretty strange.
对于我们大多数人来说，这可能听起来难以置信。
After all, much of our society relies on majority vote and consensus, whether it's politics, business, or entertainment.
毕竟，我们社会中大多都依赖于多数表决或一致的结论，无论是政治、商业还是休闲娱乐。
So it's natural to think that more consensus is a good thing.
所以人们通常认为更多的共识是好事。
And up until a certain point, it usually is.
从某种程度来说，通常是这样的。
But sometimes, the closer you start to get to total agreement, the less reliable the result becomes.
但有时候，越接近于全票赞同，结论就变得越不可靠。
This is called the paradox of unanimity.
这就是所谓的一致性悖论。
The key to understanding this apparent paradox
理解这个显而易见的悖论的方法，
is in considering the overall level of uncertainty involved in the type of situation you're dealing with.
就在于考虑整体水平的不确定性，它涉及你正在处理的情况类型中。
If we asked witnesses to identify the apple in this lineup, for example, we shouldn't be surprised by a unanimous verdict.
试想，如果我们让目击者们在这个队列中辨认出这个苹果，我们应该不会因一个完全一致的结论而感到惊讶。
But in cases where we have reason to expect some natural variance, we should also expect varied distribution.
但是当我们有理由期待一些正常差异的时候，我们也应该预期会有一些不同的意见分布。
If you toss a coin one hundred times, you would expect to get heads somewhere around 50% of the time.
如果你掷一枚硬币100次，你会期望它大概有50%的可能人头朝上。
But if your results started to approach 100% heads, you'd suspect that something was wrong,
但是如果你的结果是百分百人头朝上，你会怀疑出现了错误，
not with your individual flips, but with the coin itself.
不是因为你的投掷，而在于硬币本身。
Of course, suspect identifications aren't as random as coin tosses,
当然，犯罪嫌疑人身份的辨认不像掷硬币一样随机，
but they're not as clear cut as telling apples from bananas, either.
但他们也不像从香蕉中找出苹果那样清晰明显。
In fact, a 1994 study found that up to 48% of witnesses tend to pick the wrong person out of a lineup,
实际上，1994年的一个研究发现，高达48%的目击者往往会做出错误的选择，
even when many are confident in their choice.
即使很多人对他们的选择十分自信。
Memory based on short glimpses can be unreliable, and we often overestimate our own accuracy.
基于短短一瞥的记忆经常会不可靠，而我们经常会高估我们选择的精确性。
Knowing all this, a unanimous identification starts to seem less like certain guilt,
了解了这些，一次一致通过的嫌疑人指认开始看起来不一定有罪，
and more like a systemic error, or bias in the lineup.
而更像一次体制上的错误，或是对队列里的人有偏见。
And systemic errors don't just appear in matters of human judgement.
体制上的错误不仅会出现在人的判断上。
From 1993-2008, the same female DNA was found in multiple crime scenes around Europe,
从1993年到2008年，在欧洲的多个犯罪现场都发现了同样的女性DNA，
incriminating an elusive killer dubbed the Phantom of Heilbronn.
这位涉罪却行踪难以捉摸的杀手被称为“海尔布隆幽灵”。
But the DNA evidence was so consistent precisely because it was wrong.
而这DNA是如此的一致，正因为这个猜想是错误的。
It turned out that the cotton swabs used to collect the DNA samples
结果是，用于DNA采样的棉签
had all been accidentally contaminated by a woman working in the swab factory.
全部被棉签厂的一个女员工意外地污染了。
In other cases, systematic errors arise through deliberate fraud,
在其它案例中，系统性的错误源于蓄意诈骗，
like the presidential referendum held by Saddam Hussein in 2002,
像萨达姆侯赛因在2002年举行的总统全民公投就宣称，
which claimed a turnout of 100% of voters with all 100% supposedly voting in favor of another seven-year term.
100%的选民都投票赞同他下一个7年任期的继任。
When you look at it this way, the paradox of unanimity isn't actually all that paradoxical.
当你以这样的方式去看待它，一致性悖论实际上并不全是矛盾的。
Unanimous agreement is still theoretically ideal,
一致的赞同依然在理论上是理想的，
especially in cases when you'd expect very low odds of variability and uncertainty,
特别是当人们期望尽可能小的变数和不确定性的时候，
but in practice, achieving it in situations where perfect agreement is highly unlikely should tell us
但是在实践中，在极不可能出现完全一致的情况下达成一致时，这告诉我们，
that there's probably some hidden factor affecting the system.
可能有些隐藏的因素在影响了体制。
Although we may strive for harmony and consensus, in many situations, error and disagreement should be naturally expected.
虽然我们可能会力争和谐与共识，在很多情况下，错误和分歧也应该是意料之中的。
And if a perfect result seems too good to be true, it probably is.
如果一个完美的结果看上去难以令人置信，那其中可能就有错误了。
