【管理辞典】分类思维 / categorical thinking

「释义」

人类大脑是一个分类机器，将海量的混乱数据简化和格式化处理，以便理解这个世界。但在商业世界中，分类思维往往会创造出错觉，导致错误决策。

分类思维的危害体现在四个重要方面：脸谱化同类别下的成员，忽略彼此间的差异；夸大不同类别成员的差异；歧视、偏爱某些类别；将人为制造的类别构架视为恒定不变。

「应用场景」

在大数据和用户画像时代，基于分类思维产生的夸大尤为令人担忧。例如，Facebook根据用户浏览记录（“中立”“保守”“自由”），为其打上政治标签，并为广告主提供这类信息。这会让广告主觉得Facebook不同类型用户间的区别比实际要大，而讽刺的是，广告主因此为每个群体量身打造广告内容，反而会进一步扩大真实差异。2016年美国总统大选和英国脱欧政治活动中，似乎就发生了这样的情况：Facebook为“保守派”和“自由派”用户提供了数千条加深分裂的内容。

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 “liberal”) 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 “liberals” thousands of divisive communications.

很多公司的内部也饱受类似的夸大效应之苦。不同部门发挥协同效应往往会给企业带来成功。但是分类思维会让人严重低估团队跨部门合作的成效。如果你觉得公司的数据科学家很懂技术，但是对商业运作一窍不通，公司营销经理很懂营销，但面对数据一筹莫展，也许你根本不会想要让他们合作。这也是很多分析项目停摆的原因之一。

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.

以上文字选自《哈佛商业评论》中文版2019年10月刊《分类思维的危害》

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

马冰仑 丨编辑