A dynamic network model of societal complexity and resilience inspired by Tainter's theory of collapse

TL;DR

This paper develops a stylized network model inspired by Tainter's theory to understand how increasing societal complexity can lead to collapse under external stresses, highlighting the feedback mechanisms involved.

Contribution

It introduces a low-dimensional network model capturing societal complexity dynamics and analytically links increasing complexity to collapse risk.

Findings

Collapse likelihood rises with societal complexity under external stress.

Analytical approximation aligns with numerical simulations.

Network link density and social mobility influence societal resilience.

Abstract

In recent years, several global events have severely disrupted economies and social structures, undermining confidence in the resilience of modern societies. While empirical evidence on the dynamics and drivers of past societal collapse is mounting, a process-based understanding of these dynamics is still in its infancy. Here we aim to identify and illustrate the underlying drivers of such societal instability or even collapse. The inspiration for this work is Joseph Tainter's theory of the "collapse of complex societies", which postulates that the complexity of societies increases as they solve problems, leading to diminishing returns on complexity investments, and ultimately to collapse. In this work we have abstracted this theory into a low-dimensional and stylised model of two classes of networked agents, hereafter referred to as "laborers" and "administrators". We numerically modeled the dynamics of societal complexity, measured as the fraction of "administrators", which is assumed to affect the productivity of connected energy-producing "laborers". We show that collapse becomes increasingly likely as the complexity of the model society continuously increases in response to external stresses that emulate Tainter's abstract notion of problems that societies must solve. We also provide an analytical approximation of the system's dominant dynamics, which matches well with the numerical experiments, and use it to study the influence on network link density, social mobility and productivity. Our work advances the understanding of social-ecological collapse and illustrates its potentially direct link to an ever-increasing societal complexity in response to external shocks or stresses via a self-reinforcing feedback.