Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA.
Department of Psychology, Northwestern University, Evanston, IL, USA.
Nat Hum Behav. 2018 Oct;2(10):735-742. doi: 10.1038/s41562-018-0419-z. Epub 2018 Sep 17.
Understanding human personality has been a focus for philosophers and scientists for millennia. It is now widely accepted that there are about five major personality domains that describe the personality profile of an individual. In contrast to personality traits, the existence of personality types remains extremely controversial. Despite the various purported personality types described in the literature, small sample sizes and the lack of reproducibility across data sets and methods have led to inconclusive results about personality types. Here we develop an alternative approach to the identification of personality types, which we apply to four large data sets comprising more than 1.5 million participants. We find robust evidence for at least four distinct personality types, extending and refining previously suggested typologies. We show that these types appear as a small subset of a much more numerous set of spurious solutions in typical clustering approaches, highlighting principal limitations in the blind application of unsupervised machine learning methods to the analysis of big data.
几千年来，理解人类个性一直是哲学家和科学家关注的焦点。现在人们普遍认为，大约有五个主要的个性领域可以描述个体的个性特征。与个性特征不同，个性类型的存在仍然存在很大争议。尽管文献中描述了各种所谓的个性类型，但由于样本量小，以及在数据集和方法之间缺乏可重复性，导致关于个性类型的结果尚无定论。在这里，我们提出了一种识别个性类型的替代方法，并将其应用于包含超过 150 万参与者的四个大型数据集。我们发现了至少四种不同个性类型的有力证据，扩展和完善了以前提出的类型学。我们表明，这些类型出现在典型聚类方法中更多虚假解决方案的一小部分中，突出了在大数据分析中盲目应用无监督机器学习方法的主要局限性。
