The computational power of a human society: a new model of social evolution

TL;DR

The paper reframes long-term human societal growth as the evolution of collective computational power, proposing that both society and its environment function as interacting computational devices. The core construct is the Multiple Communicating Machines (MCM) framework, where a society is a collection of $N^S$ machines with state spaces of size $|X^S|$, exchanging $|M^S|$-sized messages and evolving across iterations with a gross free energy harvest rate (GFER) that couples thermodynamics and information processing. It identifies occupational specialization as a practical proxy for the stock of information and develops a co-evolutionary model that formalizes energy harvesting, computation costs, and parameter evolution, aiming to connect MET dynamics with observable scaling laws. By bridging big history, archaeology, and computational theory, the framework seeks to enable quantitative insights into the Anthropocene and other major evolutionary transitions through the interplay of energy, information, and social computation.

Abstract

Social evolutionary theory seeks to explain increases in the scale and complexity of human societies, from origins to present. Over the course of the twentieth century, social evolutionary theory largely fell out of favor as a way of investigating human history, just when advances in complex systems science and computer science saw the emergence of powerful new conceptions of complex systems, and in particular new methods of measuring complexity. We propose that these advances in our understanding of complex systems and computer science should be brought to bear on our investigations into human history. To that end, we present a new framework for modeling how human societies co-evolve with their biotic environments, recognizing that both a society and its environment are computers. This leads us to model the dynamics of each of those two systems using the same, new kind of computational machine, which we define here. For simplicity, we construe a society as a set of interacting occupations and technologies. Similarly, under such a model, a biotic environment is a set of interacting distinct ecological and environmental processes. This provides novel ways to characterize social complexity, which we hope will cast new light on the archaeological and historical records. Our framework also provides a natural way to formalize both the energetic (thermodynamic) costs required by a society as it runs, and the ways it can extract thermodynamic resources from the environment in order to pay for those costs -- and perhaps to grow with any left-over resources.

Figures (7)

• Figure 1: Scaling of metabolic rate to cell volume. The y-axis represents the power harnessed per unit of information, in watts per gene, with prokaryote and eukaryote averages in grey lines. Figure from kempes_evolutionary_2016.
• Figure 2: Average income rises with occupational specialization. US Metropolitan Statistical Areas, 2021; three outliers (Odessa, San Jose, Midland) omitted; BLS data, which uses the US SOC (Standard Occupational Classification) 2018 version, with 867 occupations at the most granular level.
• Figure 3: Number of unique words (minus articles, prepositions) used by census respondents in US cities to describe occupation; each dot is a US city. Data source: IPUMS (Integrated Public Use Microdata Series). This measure is independent of any classification system, and relies only on the respondent's direct answers, here in a one percent sample.
• Figure 4: Urban populations (millions) and unique occupations, US cities, present (data source: IPUMS, based on the American Community Survey occupation classes).
• Figure 5: Schematic representation of social evolution and scaling of occupational diversity in cities by population (hypothesized).