Dynamical approach to network theory yields new clues to social trends in Imperial China
DOI: 10.1063/10.0016930
Dynamical approach to network theory yields new clues to social trends in Imperial China lead image
Universality and scaling have been cornerstones for allowing the simulation critical physical systems to be collapsed on universal finite-size scaling functions. Most applications of these approaches have been focused on studying the behavior of molecules. New work has shown how such dynamical techniques can help explain human behavior, even the collapse of dynasties.
Pan et al. have demonstrated a combination of network theory with scaling to find that networks of people during two historical periods in China, separated by 500 years of history, exhibited similar universal and scaling behaviors.
“We open a new research field in the study of complex networks and human society,” said author Chin-Kun Hu. “The same ideas and methods can be applied to study other Chinese historical periods, historical periods of other countries that have good historical records, and novels which contain many events in chronological order and diverse numbers of participants.”
The first era, 209 BCE to 23 CE the early years of Imperial China. The later period covers the fall of Northern Wei and Sui dynasties from 515 to 618 CE. Investigators mapped out events and participants in each period, including 767 events and 970 participants in the earlier period alone.
They found trends showing that the Xin dynasty’s collapse was triggered by downgrading leaders of neighboring states. The data showed that the Sui dynasty’s fall was closely linked to the insistence of Emperor Sui Yang Guang that king of Korea at the time (Goryeo) come to the capital to pay respect.
The group looks to examine other periods of Chinese history as well as study the effects of epidemics, floods, and droughts.
Source: “Universality and scaling in complex networks from periods of Chinese history,” by Yi-Ru Pan, Pang Hsiao, Chen-Yu Chang, Wen-Jong Ma, Hsiang Hsiao, Pei-Jung Lin, Shih-Chieh Wang, Hui-Jie Yang, Ting-Ting Chi, and Chin-Kun Hu, Chaos (2023). The article can be accessed at https://doi.org/10.1063/5.0134923 .
