「釋義」

人類(lèi)大腦是一個(gè)分類(lèi)機(jī)器，將海量的混亂數(shù)據(jù)簡(jiǎn)化和格式化處理，以便理解這個(gè)世界。但在商業(yè)世界中，分類(lèi)思維往往會(huì)創(chuàng)造出錯(cuò)覺(jué)，導(dǎo)致錯(cuò)誤決策。

分類(lèi)思維的危害體現(xiàn)在四個(gè)重要方面：臉譜化同類(lèi)別下的成員，忽略彼此間的差異；夸大不同類(lèi)別成員的差異；歧視、偏愛(ài)某些類(lèi)別；將人為制造的類(lèi)別構(gòu)架視為恒定不變。

?

「應(yīng)用場(chǎng)景」

在大數(shù)據(jù)和用戶(hù)畫(huà)像時(shí)代，基于分類(lèi)思維產(chǎn)生的夸大尤為令人擔(dān)憂(yōu)。例如，F(xiàn)acebook根據(jù)用戶(hù)瀏覽記錄（“中立”“保守”“自由”），為其打上政治標(biāo)簽，并為廣告主提供這類(lèi)信息。這會(huì)讓廣告主覺(jué)得Facebook不同類(lèi)型用戶(hù)間的區(qū)別比實(shí)際要大，而諷刺的是，廣告主因此為每個(gè)群體量身打造廣告內(nèi)容，反而會(huì)進(jìn)一步擴(kuò)大真實(shí)差異。2016年美國(guó)總統(tǒng)大選和英國(guó)脫歐政治活動(dòng)中，似乎就發(fā)生了這樣的情況：Facebook為“保守派”和“自由派”用戶(hù)提供了數(shù)千條加深分裂的內(nèi)容。

Amplification due to categorical thinking is especially worrisome in today’s age of big data and customer profiling. Facebook, for example, is known to assign political labels to its users according to their browsing history (“moderate,” “conservative,” or “l(fā)iberal”) and to provide that information to advertisers. That can lead advertisers to assume that differences among Facebook’s categories of users are bigger than they actually are—which, ironically, can widen the true differences, by giving advertisers an incentive to deliver a highly tailored message to each group. That’s what seems to have happened in 2016, during the U.S. presidential election and the Brexit campaign, when Facebook fed “conservatives” and “l(fā)iberals” thousands of divisive communications.

?

很多公司的內(nèi)部也飽受類(lèi)似的夸大效應(yīng)之苦。不同部門(mén)發(fā)揮協(xié)同效應(yīng)往往會(huì)給企業(yè)帶來(lái)成功。但是分類(lèi)思維會(huì)讓人嚴(yán)重低估團(tuán)隊(duì)跨部門(mén)合作的成效。如果你覺(jué)得公司的數(shù)據(jù)科學(xué)家很懂技術(shù)，但是對(duì)商業(yè)運(yùn)作一竅不通，公司營(yíng)銷(xiāo)經(jīng)理很懂營(yíng)銷(xiāo)，但面對(duì)數(shù)據(jù)一籌莫展，也許你根本不會(huì)想要讓他們合作。這也是很多分析項(xiàng)目停擺的原因之一。

Many companies struggle internally with similar amplification dynamics. Success often hinges on creating interdepartmental synergies. But categorical thinking may cause you to seriously underestimate how well your teams can do cross-silo work together. If, say, you assume that your data scientists have lots of technical expertise but little understanding of how the business works, and that your marketing managers have the domain knowledge but can’t wrangle data, you might rarely think about having them team up. That’s one reason so many analytics initiatives fail.

以上文字選自《哈佛商業(yè)評(píng)論》中文版2019年10月刊《分類(lèi)思維的危害》

巴特·德朗格（Bart de Langhe）菲利普·菲恩巴赫（Philip Fernbach）丨文

馬冰侖?丨編輯?