Global Population Growth as Socio-Economic Soft Matter System Dynamics Evolution

TL;DR

This paper models global population growth as a socio-economic soft matter system, revealing a crossover from Malthusian exponential growth to a powered exponential pattern around 1970, driven by innovations and network effects.

Contribution

It introduces a novel soft matter framework and derivative-based analysis to describe population dynamics and identifies a key crossover point influenced by technological progress.

Findings

Population growth follows a Malthusian trend until ca. 1200.

A crossover to powered exponential growth occurs near 1970.

Post-1970, growth slows down compared to earlier trends.

Abstract

The report considers the dynamics of the global population as the unique case of the Socio-Economic Soft Matter system. This category was introduced for complex systems dominated by mesoscale assemblies, emerging due to the inherent tendency for local self-organization. The hypothesis is validated by studying population growth evolution using universalistic scaling patterns developed in Soft Matter science. It is supported by the innovative derivative-based and distortions-sensitive analysis, showing the extended Malthus-type trend from 10 000 B till ca. the year 1200. Subsequently, the explicit evidence of the powered exponential population rise pattern is shown, with the unique crossover near 1970. Following this year, the population growth systematically slows down compared to earlier trends. Population growth is confronted with global food demand evolution, which changes and also follows an exponential pattern. The rise of networking and innovations are indicated as the driving force leading to the crossover from the Malthus-type exponential behavior to the powered exponential one. It is supported by the analysis of the number of patents for innovations. The authors introduced the derivative-based and distortions-sensitive analysis for the optimal implementation of the powered exponential function for describing dynamic data.