The life cycle model of chinese empire dynamics (221 BC–1912 AD)

ABSTRACT

The life cycle pattern is pervasive for both natural and social sciences, from human behaviors to social systems. Based on the life cycle model of collective actions, the man–land relationship governs the rise and fall cycles, namely dynastic cycles. We combine agent-based modeling, systemic dynamics, and numerical simulations, to build the life cycle model of empires. It aims to investigate the rise and fall process of 18 major dynasties (empires) in history of China, from BC 221 to AD 1912. The core aim is to find optimal solutions, which achieve the best matching between simulations and real history. According to our algorithm, the optimal solutions can be obtained, when we have the minimal span differences (gaps) between simulated and real empires. First, we traverse all related parameters, and select simulations with 18 empires. Second, we select the cases with the total ticks between 2122 and 2132 years (ticks). Third, we select cases whose differences (gaps) are within 20 years. Finally, we obtain three optimal solutions (combinations of parameters) whose validity (100 simulations) and robustness (1000 simulations) have been checked. It seems that our life cycle model has achieved the best fitness to real empires in the history of China. For distributive matching of durations (spans), both discrete and continuous forms can be matched. Besides, the simulate and real durations can be matched as well, under counterfactual inferences of 16–17, 18 & 19–20 pairs. Based on our model, the whole history process of China can be back-calculated. Therefore, it seems that the trend of human history (society) may be an automatic process, which cannot be altered by man’s will.

KEYWORDS:

• Life cycle model
• empire dynamics
• agent-based modeling
• the optimal solution

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by the National Social Science Foundation of China (Grant No. 17ZDA117 & 19ZDA143).