Search

This paper proposes an agent-based model to investigate the role of parental relationships and intergenerational reproduction of cultural capital to understand the long-term professional success of an elite family line during the Ming and Qing dynasties in imperial China. We implemented the model by a new method: The pattern-oriented inverse simulation (POIS) method, where multiple patterns observed in an elite family line are employed to guide the model design and test alternative assumptions as family strategies. A genetic algorithm (GA) based inverse technique is applied to fit the simulation outputs with actual data grouped in time intervals as patterns. The simulation results discovered family strategies sustained by the examination systems in imperial China, which relates to important sociological theories on the impact of the intergenerational reproduction of cultural capital within family circles on social inequality in the individual entry in labor and professional markets. The case study also proved that the new methodology of POIS can improve our current practices for systematically exploring simulation parameter space and fit model output with actual data. [ABSTRACT FROM AUTHOR]

In this study, we investigate what would happen in a Chinese historical family line. We have analyzed a particular family line which had a great many candidates who passed the very tough examinations for Chinese government officials over 500 years. First, we studied the genealogical records Zokufu in China. Second, based on the study, we implemented an agent-based model with the family line network as an adjacency matrix, and the personal profile data as an attribution matrix. Third, using the "inverse simulation" technique, we optimized the agent-based model in order to fit the simulation profiles to the real profile data. From the intensive experiments, we have found that both grandfather and mother have a profound impact within a family in (1) transmitting cultural capital to the children, and (2) maintaining the norm system of the family. We conclude that advanced agent-based models are able to contribute to the discovery of new knowledge in the fields of historical science. [ABSTRACT FROM AUTHOR]