Numerous researchers from various disciplines have explored commonalities and divergences in the evolution of complex social formations. Here, we explore whether there is a 'characteristic' time-course for the evolution of social complexity in a handful of different geographic areas. Data from the Seshat: Global History Databank is shifted so that the overlapping time series can be fitted to a single logistic regression model for all 23 geographic areas under consideration. The resulting regression shows convincing out-of-sample predictions and its period of extensive growth in social complexity can be identified via bootstrapping as a time interval of roughly 2500 years. To analyse the endogenous growth of social complexity, each time series is restricted to a central time interval without major disruptions in cultural or institutional continuity and both approaches result in a similar logistic regression curve. Our results suggest that these different areas have indeed experienced a similar course in the their evolution of social complexity, but that this is a lengthy process involving both internal developments and external influences.

翻译：来自不同学科的众多研究者探索了复杂社会形态演化中的共性与差异。本文探讨了在几个不同地理区域中，社会复杂性演化是否具有"特征性"时间进程。通过将来自"Seshat：全球历史数据库"的数据进行时间平移，使得重叠的时间序列能够拟合为一个适用于全部23个地理区域的单一逻辑回归模型。该回归模型展现出令人信服的样本外预测能力，且通过自助法可将其社会复杂性快速增长的时期识别为大约2500年的时间区间。为分析社会复杂性的内生增长，我们将每个时间序列限制在一个文化或制度连续性未受重大干扰的中央时间区间内，两种方法均得出相似的逻辑回归曲线。我们的结果表明，这些不同地区在社会复杂性演化过程中确实经历了相似的进程，但这是一个既包含内部发展又包含外部影响的漫长过程。