Peng Lu

To promote Chinese-style modernization, we must unremittingly promote the all-round revitalization of the countryside. Industry and agriculture are inseparable. The light industry is a traditionally dominant industry in China and is closely linked to the revitalization of rural areas. The coordination and resonance between the high-quality development of the light industry and rural revitalization is a significant area of research. This study developed models for coupling coordination degree and fuzzy set qualitative comparative analysis. It utilized data from 2012 to 2022 to empirically analyze the relationship between the high-quality development of China’s light industry and rural revitalization. Both of the systems had reached a condition of moderate coupling and showed dynamic interactive coupling. The degree of coupling coordination is affected by multiple variables, like economic development and scientific and technological innovation. There are two ways to promote the two to achieve a highly coupled and coordinated state. Therefore, this study opens up a novel methodological perspective and innovative path for research in related fields.

After the Axial Age, the West moved toward continuous disunity, but China had successfully maintained a persistent unity pattern. Conventional case (history event) studies are subject to selection bias and theoretical frameworks, which is not objective narrative. We use agent-based modeling (ABM) to reveal the historical dynamics of why civilizations take on distinct patterns (unity versus disunity). In China, the Qin Dynasty (initial unity) opened the Great Unity tradition in 221 BC. Before this, there was a major chaotic period (770 BC to 221 BC) with two periods. The first period, the Spring and Autumn (770 BC to 221 BC), opened this chaotic process and indirectly led to the initial unity. Then, the second period, the Warring States period (475 BC to 221 BC), directly led to this initial unity. This work models the second period and focuses on the question of why human civilizations take on different patterns in history. Finally, we have solved the conditions and boundaries of two patterns. Based on the second period, we have different conclusions. The bellicosity threshold is around 0.2 (for the previous period, this is 0.3), and the alliance propensity threshold is around 0.8 (for the previous period, this is 0.7). Moreover, the higher winner cost (beyond 5%) makes it impossible to achieve Unity. This work has one new contribution, such as solving social knowledge. We use BP neural networks to evaluate the knowledge graph to support history learning. It explains civilization patterns for humankind.

The laws and regulations in human history can be revealed by computational models. From 221 before Christ (BC) to 1912 Anno Domini (AD), the unification pattern has dominated the main part of Chinese history for 2132 years. Before the emergence of the first unified empire, the Qin Empire in 221 BC, there existed the Eastern Zhou dynasty (770 BC to 221 BC). This long dynasty has two stages, and here we focus on the first stage. This Spring–Autumn stage was from 770 BC (with 148 states) to 476 BC (with 32 states). The whole country (China) is modelled as a multi‐agent system, which contains multiple local states. They behave autonomously under certain action rules (wars and conflicts), which forms the main reason for the annexations and disappearance of most states. Key factors (power, loyalty, bellicosity and alliance) have been considered in our model settings, and simulation outcomes will be monitored and collected. Eventually, an optimal solution is obtained, which well unveils the internal mechanism and statistical features of real big history. Furthermore, counterfactuals are used to explore the non‐linear effects of the key factors, which deepens the authors’ understanding of civilisation evolutions in human history.