Anthropology Program, Utah State University, Logan, Utah, United States of America.
PLoS One. 2020 May 13;15(5):e0232609. doi: 10.1371/journal.pone.0232609. eCollection 2020.
Comparative social science has a long history of attempts to classify societies and cultures in terms of shared characteristics. However, only recently has it become feasible to conduct quantitative analysis of large historical datasets to mathematically approach the study of social complexity and classify shared societal characteristics. Such methods have the potential to identify recurrent social formations in human societies and contribute to social evolutionary theory. However, in order to achieve this potential, repeated studies are needed to assess the robustness of results to changing methods and data sets. Using an improved derivative of the Seshat: Global History Databank, we perform a clustering analysis of 271 past societies from sampling points across the globe to study plausible categorizations inherent in the data. Analysis indicates that the best fit to Seshat data is five subclusters existing as part of two clearly delineated superclusters (that is, two broad "types" of society in terms of social-ecological configuration). Our results add weight to the idea that human societies form recurrent social formations by replicating previous studies with different methods and data. Our results also contribute nuance to previously established measures of social complexity, illustrate diverse trajectories of change, and shed further light on the finite bounds of human social diversity.
比较社会科学长期以来一直试图根据共同特征对社会和文化进行分类。然而，直到最近，才有可能对大型历史数据集进行定量分析，从而从数学角度研究社会复杂性并对共同的社会特征进行分类。这些方法有可能识别人类社会中反复出现的社会形态，并为社会进化理论做出贡献。但是，为了实现这一潜力，需要进行重复的研究，以评估结果对变化的方法和数据集的稳健性。我们使用 Seshat：全球历史数据库的改进衍生工具，对来自全球采样点的 271 个过去社会进行聚类分析，以研究数据中固有的合理分类。分析表明，Seshat 数据的最佳拟合是五个子群，它们作为两个明显界定的超级群的一部分存在（即，根据社会生态配置，存在两种广泛的“社会类型”）。我们的研究结果进一步证实了人类社会通过复制以前的研究来形成反复出现的社会形态的观点，这些研究采用了不同的方法和数据集。我们的研究结果还为先前建立的社会复杂性衡量标准增添了细微差别，说明了不同的变化轨迹，并进一步揭示了人类社会多样性的有限界限。
