Should you trust unanimous decisions Derek Abbott

Imagine a police lineup
where ten witnesses

are asked to identify a bank robber
they glimpsed fleeing the crime scene.

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.

For most of us,
this sounds pretty strange.

After all, much of our society
relies on majority vote and consensus,

whether it’s politics,

business,

or entertainment.

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

And up until a certain point,
it usually is.

But sometimes, the closer you start to get
to total agreement,

the less reliable the result becomes.

This is called the paradox of unanimity.

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.

If we asked witnesses to identify
the apple in this lineup, for example,

we shouldn’t be surprised
by a unanimous verdict.

But in cases where we have
reason to expect some natural variance,

we should also expect varied distribution.

If you toss a coin one hundred times,

you would expect to get heads
somewhere around 50% of the time.

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.

Of course, suspect identifications aren’t
as random as coin tosses,

but they’re not as clear cut
as telling apples from bananas, either.

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.

Memory based on short glimpses
can be unreliable,

and we often overestimate
our own accuracy.

Knowing all this,

a unanimous identification starts to seem
less like certain guilt,

and more like a systemic error,

or bias in the lineup.

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

From 1993-2008,

the same female DNA was found
in multiple crime scenes around Europe,

incriminating an elusive killer
dubbed the Phantom of Heilbronn.

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

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.

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.

When you look at it this way,

the paradox of unanimity isn’t actually
all that paradoxical.

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.

Although we may strive for harmony
and consensus,

in many situations, error and disagreement
should be naturally expected.

And if a perfect result seems too good
to be true,

it probably is.

想象一个警察阵容
,其中

要求十名证人识别
他们瞥见逃离犯罪现场的银行劫匪。

如果其中六个人选择了同一个人,

那很有可能
是真正的罪魁祸首

,如果所有十个人都做出相同的选择,

你可能会认为这个案子是坚如磐石的,

但你错了。

对于我们大多数人来说,
这听起来很奇怪。

毕竟,我们社会的
大部分都依赖于多数票和共识,

无论是政治、

商业

还是娱乐。

所以很自然地
认为更多的共识是一件好事。

直到某一点,
它通常是。

但有时,您越
接近完全一致

,结果就越不可靠。

这被称为一致的悖论。

理解
这个明显悖论

的关键在于考虑你所处理

的情况类型所涉及的总体不确定性水平

例如,如果我们让目击者
在这个阵容中识别苹果,

我们不应该对
一致的判决感到惊讶。

但在我们
有理由期待一些自然变化的情况下,

我们也应该期待不同的分布。

如果你抛硬币一百次,

你会期望在
大约 50% 的时间里得到正面。

但是,如果您的结果开始
接近 100% 正面,

您会怀疑出了什么问题,

不是您的个人翻转,

而是硬币本身。

当然,嫌疑人的识别并不
像抛硬币那样随机,

但也
不像区分苹果和香蕉那样明确。

事实上,1994 年的一项研究发现
,多达 48% 的证人

倾向于从排队中挑选错误的
人,

即使许多
人对自己的选择充满信心。

基于短暂一瞥的记忆
可能不可靠

,我们经常高估
自己的准确性。

知道了这一切

,一致的认同开始看起来
不像是某种内疚,

而更像是系统性错误,

或者阵容中的偏见。

系统性错误不仅仅出现
在人类判断的问题上。

从 1993 年到 2008 年,

在欧洲各地的多个犯罪现场发现了相同的女性 DNA,从而

将一名难以捉摸的杀手
定为海尔布隆魅影。

但 DNA 证据之所以如此一致,
正是因为它是错误的。

原来,
用于采集 DNA 样本的棉签

都被
一名在棉签厂工作的妇女意外污染了。

在其他情况下,系统性错误是
由于蓄意欺诈而产生的,

例如
萨达姆·侯赛因在 2002 年举行的总统公投,

该公投要求 100% 的选民投票
,而所有 100% 的选民都应该投票

支持另一个七年任期。

当你这样看时

,一致的悖论实际上
并不是那么自相矛盾。

一致同意
在理论上仍然是理想的,

尤其是在您预计
可变性和不确定性非常低的情况下,

但在实践中,

在极不可能完全一致的情况下实现它

应该告诉我们可能存在
影响系统的一些隐藏因素。

尽管我们可能会努力争取和谐
和共识,但

在许多情况下,错误和分歧
应该是自然而然的。

如果一个完美的结果看起来好
得令人难以置信,

它可能就是这样。