噪声如何影响我们的决策

Books and Arts -- Book Review

文学与艺术——书评

Decision-making -- Noise pollution

决策——噪声污染

Noise. By Daniel Kahneman.

《噪声》，作者：丹尼尔·卡纳曼。

Noise is unwanted variation in judgments that should be identical, which leads to inaccurate and unfair decisions.

噪声是本应一致的判断中多余的变数，它会导向不准确、不公正的决策。

It is all around people all the time, though individuals fail to notice it.

它时时刻刻存在于人们周围，尽管人们并没有注意到它。

To get a sense of how it happens, perform a “noise audit” right now: open your phone’s stopwatch app and practice counting ten seconds.

要了解它是如何出现的，现在就可以进行一次“噪音审查”：打开手机里的秒表软件，练习数十秒。

Now, with your eyes closed, count several times, hitting the lap button each time you believe ten seconds have elapsed.

现在，闭上眼睛，数几次，每当你认为已经过去了10秒就按下按钮计为一次。

Your answers weren’t perfect but noisy: slightly above or below the ten-second mark.

你的答案并不完美，而是充满噪声：比10秒长一点或短一点。

And if they were consistently wrong in one direction, then there is bias too, which is a different form of error (you counted too quickly or slowly).

如果结果总是在某一个方向上产生错误，那么这里还存在了偏差，这是另一种形式的错误（你数得太快或太慢）。

The problem of bias in decisions is well known and there are strategies that people can adopt to minimise it.

决策中存在偏差是众所周知的问题，人们可以采取一些策略将其影响将至最低。

For example, customers may be “anchored” on the first price they are presented with in a transaction, so they learn to consciously discard it before they negotiate.

例如，客户可能会“锁定”他们在交易中看到的第一个价格，所以他们学会了在谈判之前有意地放弃这个价格。

But noise is different precisely because it is less apparent.

但噪声之所以不同，恰恰是因为它不那么明显。

“It becomes visible only when we think statistically about an ensemble of similar judgments. Indeed, it then becomes hard to miss,” Daniel Kahneman, Olivier Sibony and Cass Sunstein write in their new book.

“只有当我们从统计学的角度考虑一系列相似的判断时，它才会显现出来。事实上，到那时，它会变得很难被忽视。”丹尼尔·卡尼曼、奥利维尔·西伯尼和卡斯·桑斯坦在他们的新书中写道。

The divergences are stark.

差异是显而易见的。

In a courthouse in Miami, one judge would grant refugees asylum in 88% of cases while another would do so 5% of the time.

在迈阿密的一家法院，一名法官会在88%的案件中给予难民庇护，而另一名法官则只会在5%的案件中给予这样的结果。

A large study of radiologists found that the false-positive rate ranged from 1% to 64%, meaning that two-thirds of the time, a radiologist said a mammogram showed cancer when it was not cancerous.

一项针对放射科医生的大型研究发现，假阳性率从1%到64%不等，放射科医生说，这意味着三分之二的情况下，乳房X光片会将非癌症的结果显示为癌症。

Doctors are more likely to prescribe opioids at the end of a long day.

医生在漫长的一天即将结束的时段更有可能开出阿片类药物。

Judges made harsher decisions leading up to their breaks and on hotter days.

法官在间歇休息前和炎热的日子里会做出更严厉的判决。

An insurance firm’s underwriters assessed premiums that varied by 55%, a difference that was five times greater than its management had imagined.

一家保险公司的承保人评估的保费相差达到55%，这一差额比管理层预想的要大五倍。

Not only do individuals differ with their peers, they often fail to agree with themselves.

一个人的决策不仅常与同伴不同，而且常常无法与自己达成一致。

Wine experts tasting the same samples for a second time scored fewer than one in five identically.

葡萄酒专家在第二次品尝相同样品时，与之前评判一致的不到五分之一。

Four out of five fingerprint examiners altered their original identification decision when presented with contextual information that should not have been a factor in matching prints.

五分之四的指纹鉴定员在看到一些相关信息后改变了最初的识别决定，而这些信息本不应该成为影响指纹匹配的因素。

In one medical study, assessing angiograms, physicians disagreed with their earlier judgments more than half the time.

在一项评估血管造影的医学研究中，医生们在超过一半的情况下不同意他们早期的判断。

Noise is sometimes good.

噪声有时是有利的。

When different investors size up a trade or book reviewers reach different assessments, the diversity of opinion is beneficial.

当不同的投资者评估同一笔交易，或者书评家得出不同的评论时，意见的多样性是有益的。

But more commonly it creates problems.

但多数情况下，噪声会带来问题。

In law noise means unfairness.

在法律上，噪声意味着不公平。

In business it can be costly.

在商业上，噪声可能让人付出高昂的代价。

Yet it can be reduced.

不过，噪声是可以被削弱的。

The authors’ remedies include a “noise audit” to measure the degree of disagreement on the same cases, to quantify the variation that is usually invisible.

作者们的补救措施包括用“噪音审查”来衡量对同一事件的不一致程度，将通常看不见的差异量化。

They also call for better “decision hygiene” such as designating an observer for group decisions, to prevent common biases and noisy judgments.

他们还提倡改善“决策清洁”，比如在集体决策中指定一名观察员，以防止出现常见的偏差和噪声下产生的判断。

For example, they can ensure that participants in a team reach independent assessments before coming together as a group to aggregate their decisions.

例如，他们可以确保团队中的参与者在聚集起来进行决策之前先进行独立的评估。

Another solution is to dispense with people altogether.

另一个解决办法是完全去除人的参与。

Statistical models, pre-determined rules and algorithms in many cases are more accurate than human judgment.

在许多情况下，统计模型、预先确定的规则和算法比人类的判断更准确。

The authors welcome artificial intelligence to make many decisions in society, but acknowledge that people are predisposed to resisting their answers, for lack of the personal, emotional quality in decision-making—even if it leads to inferior, or at least variable, decisions.

作者乐于看到人工智能在社会上做出许多决定，但也承认人们倾向于拒绝人工智能给出的答案，因其在决策中缺乏人性的和感性的特质——即使这会导致产生较差的，或至少是多变的决策。

The trio speaks with credibility.

三位作者的说法都很有可信度。

Mr Kahneman is a Nobel laureate whose ideas on bias in human reasoning have reshaped economics and society; Mr Sunstein is a polymath scholar at Harvard and occasional government official putting his ideas into policy; Mr Sibony is a former McKinsey partner who teaches decision science at a French business school.

卡尼曼是一位诺贝尔奖获得者，他关于人类推论中的偏差的观点重塑了经济学和社会；桑斯坦是哈佛大学一位博学多才的学者，偶尔会担任政府官员的角色将自己的想法付诸政策；西伯尼曾是麦肯锡的合伙人，现在法国一所商学院教授决策科学。

Yet despite the book’s title, the authors struggled to extract the signal from the noise, so to speak, needing some 400 pages to make their case.

然而，尽管这本书讲的就是噪声，作者们还是得艰难地从噪声中提取信息，这么说吧，他们需要用大约400页的篇幅才能阐明他们的观点。

A tighter argument would have enhanced the ideas they present.

他们本应提供更严谨的论据来加强自己的观点。