评论
打印
The dangers of data
数据的危险
Managers are better equipped than ever to make good decisions. They are more aware that human judgment is fallible. They have oodles of data about their customers and products.
管理者比以往任何时候都更有能力做出好的决策。他们更清楚地意识到人类的判断是容易出错的。他们拥有关于客户和产品的海量数据。
They can use artificial intelligence (AI) to analyse, summarise and synthesise information with unprecedented speed. But as the pendulum swings inexorably away from gut instinct and towards data-based decisions, firms need to be alive to a different set of dangers.
他们可以使用人工智能以前所未有的速度分析、总结、综合信息。但是在时代的钟摆不可阻挡地从直觉荡向基于数据的决策之际，公司需要意识到一系列不同的危险。
In a recent paper Linda Chang of the Toyota Research Institute and her co-authors identify a cognitive bias that they call “quantification fixation”.
在最近的一篇论文中，丰田研究所的琳达·张及其合著者发现了一种被称为“量化情结”的认知偏见。
The risk of depending on data alone to make decisions is familiar: it is sometimes referred to as the McNamara fallacy, after the emphasis that an American secretary of defence put on misleading quantitative measures in assessing the Vietnam war.
仅依靠数据来做决策的风险并不令人陌生：这有时被称为麦克纳马拉谬误，得名于美国国防部长麦克纳马拉在评估越南战争时极其看重具有误导性的量化指标。
But Ms Chang and her co-authors help explain why people put disproportionate weight on numbers. The reason seems to be that data are particularly suited to making comparisons.
但是张女士及其合著者帮助解释了为什么人们会过度重视数字。原因似乎是数据特别适合进行比较。
In one experiment, participants were asked to imagine choosing between two software engineers for a promotion. One engineer had been assessed as more likely to climb the ladder but less likely to stay at the firm; the other, by contrast, had a higher probability of retention but a lower chance of advancement.
在一项实验中，参与者被要求想象在两名软件工程师之间选择晋升人选。一位工程师被评估为更有可能晋升，但不太可能留在公司；相反，另一位工程师留在公司的概率更高，但晋升的概率更低。
The researchers varied the way that this information was presented. They found that participants were more likely to choose on the basis of future promotion prospects when only that criterion was quantified, and to select on retention probability when that was the thing with a number attached.
研究人员改变了信息呈现的方式。他们发现，当只有未来晋升前景被量化时，参与者更有可能根据晋升前景进行选择，而当只有留任概率被量化时，参与者更有可能根据留任概率进行选择。
One answer to this bias is to quantify everything. But, as the authors point out, some things are mushier than others. A firm’s culture is harder to express as a number for job-seekers than its salary levels. Data can tell an early-stage investor more about a startup’s financials than a founder’s resilience.
对这种偏见的一个回应办法是将一切量化。但是正如作者们指出的，有些事情比其他事情更模糊一团。企业文化比薪资水平更难以数字的形式传达给求职者。数据可以让早期投资者知道某个初创公司的财务状况，而不是创始人的坚韧品格。
Numbers allow for easy comparisons. The problem is that they do not always tell the whole story.
数字能够进行简单的比较。问题在于数字并不总是说明全貌。
