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.

翻译：报告认为,全球人口动态是社会经济软物质系统的独特案例。这一类别是针对以中尺度组装为主的复杂系统采用的,这些系统因地方自我组织的固有趋势而出现。通过利用软物质科学中发展的普遍规模模式研究人口增长演变,这一假设得到证实。它得到创新衍生物和扭曲敏感分析的支持,显示马尔萨斯型指数行为从10 000 B到12年的延伸趋势。随后,显示了具有动力的指数式人口增长模式的明确证据,在1970年前后出现了独特的交叉。今年之后,人口增长与早先的趋势相比,系统地放慢了速度。人口增长面对全球粮食需求的变化,而全球粮食需求的变化也遵循了指数式模式。联网和创新的兴起是导致从马勒萨斯型指数行为向动力指数型变化的驱动力。它得到创新专利数量分析的支持。作者介绍了基于衍生物和扭曲敏感度的分析,以优化地执行描述动态数据的动力指数功能。