Simulation and prediction of rural population changes using agent-based modeling
Shanshan Huang, Yao Huang, Shitai Bao, Jianfang Wang, Siying Chen, Muhammad Umer Arshad, Muhammad Umer Arshad, Muhammad Umer Arshad, Muhammad Umer Arshad
TL;DR
This paper uses agent-based modeling to simulate and predict rural population changes in China, focusing on micro-level behaviors like migration and birth rates.
Contribution
The study introduces a novel agent-based model to capture micro-level rural population dynamics and predict future trends.
Findings
Agent-based modeling effectively captures rural population dynamics at the micro level.
Economically disadvantaged villages show significant declines in population, labor force, and youth proportion.
Migration and birth rates are key factors influencing rural demographic trends.
Abstract
Rural population change is a critical element of the strategy for rural revitalization in China. Many studies emphasize large-scale macro-population trends, but a noticeable gap exists in micro-level simulations and predictions regarding rural population size and structure. This study employs an agent-based model(ABM), defining a population agent and its behavioral rules. By modeling individual-level birth, death, and migration behaviors, it generates agent-based outputs that aggregate to capture population dynamics and forecast rural demographic trends over the next 11 years. Using two representative villages as study areas, the results were validated by comparing them with actual population data and predictions made by the Leslie model. The findings demonstrate the following: 1) the agent-based modeling effectively captures the dynamics of births, deaths, and migrations at the micro…
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Taxonomy
Topicsdemographic modeling and climate adaptation · Urban, Neighborhood, and Segregation Studies · Rural development and sustainability