Time series 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 18 geographic areas under consideration. To analyse the endogenous growth of social complexity, each time series is restricted to a central time interval without discontinuous polity changes. The resulting regression shows convincing out-of-sample predictions and via bootstrapping, its period of rapidly growing social complexity can be identified as a time interval of roughly 800 years.

翻译：从Seshat获得的时间序列数据:全球历史数据库被改变,这样重叠的时间序列就能够适应所有18个审议地理区域的单一后勤回归模型。为了分析社会复杂性的内在增长,每个时间序列都限于一个中央时间间隔,不发生不连续的政体变化。由此得出的回归表明有说服力的抽样外预测和通过靴子,其迅速增长的社会复杂性时期可以被确定为大约800年的时间间隔。