Should you trust unanimous decisions? - Derek Abbott

4,349,047 views ・ 2016-04-18

TED-Ed


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翻译人员: William Lee 校对人员: Jenny Yang
00:06
Imagine a police lineup where ten witnesses
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想象一个“列队指认” 十位目击者
00:10
are asked to identify a bank robber they glimpsed fleeing the crime scene.
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被要求指认他们瞥见的逃离犯罪现场的银行抢劫犯
00:15
If six of them pick out the same person,
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如果其中6个指向了同一个人
00:18
there's a good chance that's the real culprit,
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那这就是很有可能是真正的罪犯
如果十个人都做出相同的选择
00:21
and if all ten make the same choice,
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你可能会认为这是毋庸置疑的了
00:23
you might think the case is rock solid,
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00:25
but you'd be wrong.
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但是你也许错了
00:27
For most of us, this sounds pretty strange.
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对于我们大多数人来说
这可能听起来难以置信
00:29
After all, much of our society relies on majority vote and consensus,
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毕竟
我们社会中大多都依赖于多数表决或一致的结论
00:34
whether it's politics,
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无论是政治
00:35
business,
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商业
00:36
or entertainment.
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还是休闲娱乐
00:37
So it's natural to think that more consensus is a good thing.
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所以人们通常认为更多的共识是好事
从某种程度来说,通常是这样的
00:42
And up until a certain point, it usually is.
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00:44
But sometimes, the closer you start to get to total agreement,
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但有时候
越接近于全票赞同
00:48
the less reliable the result becomes.
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结论就变得越不可靠
00:52
This is called the paradox of unanimity.
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这就是所谓的 一致性悖论
00:56
The key to understanding this apparent paradox
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理解这个显而易见的悖论的方法
00:58
is in considering the overall level of uncertainty
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就在于 考虑整体水平的不确定性
01:01
involved in the type of situation you're dealing with.
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它涉及你正在处理的情况类型中
01:05
If we asked witnesses to identify the apple in this lineup, for example,
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试想 如果我们让目击者们在这个队列中辨认出这个苹果
01:09
we shouldn't be surprised by a unanimous verdict.
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我们应该不会因一个完全一致的结论而感到惊讶
01:13
But in cases where we have reason to expect some natural variance,
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但是当我们有理由期待一些正常差异的时候
01:17
we should also expect varied distribution.
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我们也应该预期会有一些不同的意见分布
01:21
If you toss a coin one hundred times,
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如果你掷一枚硬币100次
01:23
you would expect to get heads somewhere around 50% of the time.
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你会期望它大概有50%的可能人头朝上
01:28
But if your results started to approach 100% heads,
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但是如果你的结果是百分百人头朝上
01:31
you'd suspect that something was wrong,
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你会怀疑出现了错误
01:34
not with your individual flips,
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不是因为你的投掷
01:35
but with the coin itself.
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而在于硬币本身
01:39
Of course, suspect identifications aren't as random as coin tosses,
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当然 犯罪嫌疑人身份的辨认不像掷硬币一样随机
01:43
but they're not as clear cut as telling apples from bananas, either.
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但他们也不像从香蕉中找出苹果那样清晰明显
01:48
In fact, a 1994 study found that up to 48% of witnesses
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实际上
1994年的一个研究发现
高达48%的目击者
01:54
tend to pick the wrong person out of a lineup,
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往往会做出错误的选择
01:56
even when many are confident in their choice.
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即使很多人对他们的选择十分自信
02:00
Memory based on short glimpses can be unreliable,
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基于短短一瞥的记忆经常会不可靠
02:03
and we often overestimate our own accuracy.
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而我们经常会高估我们选择的精确性
02:07
Knowing all this,
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了解了这些
02:08
a unanimous identification starts to seem less like certain guilt,
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一次一致通过的嫌疑人指认 开始看起来不一定有罪
02:12
and more like a systemic error,
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而更像一次体制上的错误
02:14
or bias in the lineup.
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或是对队列里的人有偏见
体制上的错误不仅会出现在人的判断上
02:17
And systemic errors don't just appear in matters of human judgement.
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02:21
From 1993-2008,
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从1993年到2008年
02:23
the same female DNA was found in multiple crime scenes around Europe,
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在欧洲的多个犯罪现场都发现了同样的女性DNA
02:28
incriminating an elusive killer dubbed the Phantom of Heilbronn.
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这位涉罪却行踪难以捉摸的杀手被称为“海尔布隆幽灵”
02:34
But the DNA evidence was so consistent precisely because it was wrong.
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而这DNA是如此的一致 正因为这个猜想是错误的
02:40
It turned out that the cotton swabs used to collect the DNA samples
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结果是
用于DNA采样的棉签
02:43
had all been accidentally contaminated by a woman working in the swab factory.
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全部被棉签厂的一个女员工意外地污染了
在其它案例中
02:50
In other cases, systematic errors arise through deliberate fraud,
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系统性的错误源于蓄意诈骗
02:54
like the presidential referendum held by Saddam Hussein in 2002,
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像萨达姆侯赛因在2002年举行的总统全民公投就宣称
100%的选民都投票赞同他
02:59
which claimed a turnout of 100% of voters with all 100% supposedly voting in favor
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03:06
of another seven-year term.
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下一个7年任期的继任
03:09
When you look at it this way,
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当你以这样的方式去看待它
03:10
the paradox of unanimity isn't actually all that paradoxical.
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一致性悖论实际上并不全是矛盾的
03:15
Unanimous agreement is still theoretically ideal,
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一致的赞同依然在理论上是理想的
03:18
especially in cases when you'd expect very low odds of variability and uncertainty,
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特别是当人们期望尽可能小的变数和不确定性的时候
03:23
but in practice,
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但是在实践中
03:24
achieving it in situations where perfect agreement is highly unlikely
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在极不可能出现完全一致的情况下达成一致时
03:29
should tell us that there's probably some hidden factor affecting the system.
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这告诉我们,可能有些隐藏的因素在影响了体制
虽然我们可能会力争和谐与共识
03:34
Although we may strive for harmony and consensus,
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在很多情况下,错误和分歧也应该是意料之中的
03:37
in many situations, error and disagreement should be naturally expected.
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如果一个完美的结果看上去难以令人置信
03:42
And if a perfect result seems too good to be true,
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03:44
it probably is.
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那其中可能就有错误了
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