A random matrix perspective of cultural structure: groups or redundancies?

TL;DR

This study applies random matrix theory to empirical cultural data to differentiate between genuine cultural groups and redundancies, revealing that many observed structures may be artifacts rather than true groupings.

Contribution

It introduces a null model for cultural similarity matrices and proposes a method to distinguish between group signatures and redundancies using eigenvector uniformity analysis.

Findings

Eigenvalues can indicate either cultural groups or redundancies.

Eigenvector uniformity helps differentiate between genuine groups and artifacts.

Most empirical eigenvalues are compatible with redundancy, not true groups.

Abstract

Recent studies have highlighted interesting structural properties of empirical cultural states: collections of cultural traits sequences of real individuals. Matrices of similarity between individuals may be constructed from these states, allowing for further structural insights to be gained using concepts from random matrix theory, approach first exploited in this study. For generating random matrices that are appropriate as a structureless reference, we propose a null model that enforces, on average, the empirical occurrence frequency of each possible trait. With respect to this null model, the empirical matrices show deviating eigenvalues, which may be signatures of subtle cultural groups. However, they can conceivably also be artifacts of arbitrary redundancies between cultural variables. We first study this possibility in a highly simplified setting, using a toy model that enforces a certain level of redundancy in a minimally-biased way, in parallel with another toy model that enforces group structure. By analyzing and comparing cultural states generated with these toy models, we show that a deviating eigenvalue can indeed be a redundancy signature, which can be distinguished from a grouping signature by evaluating the uniformity of the entries of the respective eigenvector, as well as the uniformity-based compatibility with the null model. For empirical data, the eigenvector uniformities of all deviating eigenvalues are shown to be compatible with the null model, apparently suggesting that we are not dealing with genuine group structure. However, we demonstrate that some deviating eigenvalues might actually be due to authentic groups that are internally non-uniform. A generic procedure for distinguishing such groups from redundancy artifacts requires further research.