Scale and information-processing thresholds in Holocene social evolution

The characteristic time scale of cultural evolution

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 2,500 years. To analyze 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.

The potential to infer the historical pattern of cultural macroevolution

Phylogenetic analyses increasingly take centre-stage in our understanding of the processes shaping patterns of cultural diversity and cultural evolution over time. Just as biologists explain the origins and maintenance of trait differences among organisms using phylogenetic methods, so anthropologists studying cultural macroevolutionary processes use phylogenetic methods to uncover the history of human populations and the dynamics of culturally transmitted traits. In this paper, we revisit concerns with the validity of these methods. Specifically, we use simulations to reveal how properties of the sample (size, missing data), properties of the tree (shape) and properties of the traits (rate of change, number of variants, transmission mode) might influence the inferences that can be drawn about trait distributions across a given phylogeny and the power to discern alternative histories. Our approach shows that in two example datasets specific combinations of properties of the sample, of the tree and of the trait can lead to potentially high rates of Type I and Type II errors. We offer this simulation tool to help assess the potential impact of this list of persistent perils in future cultural macroevolutionary work. This article is part of the theme issue 'Foundations of cultural evolution'.

Scaling human sociopolitical complexity

Human societies exhibit a diversity of social organizations that vary widely in size, structure, and complexity. Today, human sociopolitical complexity ranges from stateless small-scale societies of a few hundred individuals to complex states of millions, most of this diversity evolving only over the last few hundred years. Understanding how sociopolitical complexity evolved over time and space has always been a central focus of the social sciences. Yet despite this long-term interest, a quantitative understanding of how sociopolitical complexity varies across cultures is not well developed. Here we use scaling analysis to examine the statistical structure of a global sample of over a thousand human societies across multiple levels of sociopolitical complexity. First, we show that levels of sociopolitical complexity are self-similar as adjacent levels of jurisdictional hierarchy see a four-fold increase in population size, a two-fold increase in geographic range, and therefore a doubling of population density. Second, we show how this self-similarity leads to the scaling of population size and geographic range. As societies increase in complexity population density is reconfigured in space and quantified by scaling parameters. However, there is considerable overlap in population metrics across all scales suggesting that while more complex societies tend to have larger and denser populations, larger and denser populations are not necessarily more complex.