正文
In a recent paper Linda Chang of the Toyota Research Institute and her co-authors identify a cognitive bias that they call
“
quantification fixation
”
. 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. But Ms Chang and her co-authors help explain why people put disproportionate weight on numbers.
丰田研究院的常琳及其合作者在最新研究中揭示了一种名为
"
量化固着
"
的认知偏差。过度依赖数据决策的风险其实早有先例
——"
麦克纳马拉谬误
"
正是得名于美国前国防部长在评估越战时对误导性量化指标的偏执。这项新研究则揭示了数字为何总能获得超乎其理的权重。
The reason seems to be that data are particularly suited to making comparisons. 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.
究其根源，数据在比较分析中具有天然优势。在一项实验中，受试者需从两位软件工程师中抉择晋升人选：甲被评估为更具晋升潜力但更可能离职，乙则留任概率更高但晋升机会较低。
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.
当研究人员以不同方式呈现信息时发现，当只有晋升前景被量化呈现时，受试者更倾向以此为标准；反之当留任概率附上具体数值时，该指标就主导了选择。
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. Numbers allow for easy comparisons. The problem is that they do not always tell the whole story.
表面上看，将一切要素量化为数值似乎是解决之道。但研究者指出，某些要素天生难以数字化：对企业文化的量化远不如薪资水平直观，初创企业的财务数据也无法完整反映创始人的抗压能力。数字固然便于比较，却常常遗漏重要维度。
There are other risks, too. Humans bring the same cognitive biases to their analysis of numbers as they do to other decisions. Take confirmation bias, the propensity to interpret information as support for your point of view.
数据应用还存在其他隐患。人类在分析数据时会带入与其他决策相同的认知偏差。以确认偏误为例
——
人们倾向于将信息解读为对自身观点的佐证。
