The two types of society: Computationally revealing recurrent social formations and their evolutionary trajectories.

Lux Miranda,Jacob Freeman

doi:10.1371/journal.pone.0232609

Abstract

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.

Highlights

The emerging model of “recurrent social formations” postulates that only a small number of stable social-ecological configurations exist for human societies [1, 2]
In the remainder of this paper, we provide background on the model of recurrent social formations, use clustering to reveal and explore statistically significant typologies of past societies, and we discuss our findings in the context of the conceptual model of recurrent social formations
In the remainder of this article, we ask: Do we find recurrent social formations in the Seshat database that replicate the trajectories of change in social complexity identified by Turchin and colleagues? our study directly builds on the studies above by using a novel clustering algorithm to evaluate how robust the observation of super-clusters and recurrent changes in social complexity are to a change in method and dataset

Summary

Introduction

The emerging model of “recurrent social formations” postulates that only a small number of stable social-ecological configurations exist for human societies [1, 2]. Computational analyses on datasets encoding information on social formations have proven fruitful in the study of social complexity [1, 5, 6] As these kinds of datasets and analyses continue to emerge, the robustness of previous results to changes in data and method should be explored. We use a multi-dimensional clustering algorithm to explore “clumps” in data on human societies indicative of recurrent social formations, and we compare our results with those identified by researchers using alternative methods and datasets. Societies in entirely different superclusters can have similar social complexity factor scores This nuance provided by a combination of PC1 and cluster trajectories may be of importance for certain research questions. Multiple methods can provide an important check against confirmation bias and open-up a broader range of research questions for comparative social scientists

Background

4: Perform spectral clustering on W

Results

Discussion & conclusion