通过劳动力迁移建模城市化动态
Modeling urbanization dynamics by labor force migration
AI总结：
该研究基于主动布朗粒子建立劳动力迁移微观模型，再现聚集、Zipf 定律与 Okun 定律，并推导扩展 Keller–Segel 系统以解释城市化的聚集—扩散转变。
摘要：
人类社会中的个体参与者会集体表现出聚集行为。在本研究中，我们基于主动布朗粒子框架提出了一个简单的劳动力迁移微观模型。具体而言，基于智能体的模拟表明，该模型从随机初始分布产生了智能体集群。此外，研究观察到两条被称为 Zipf 定律和 Okun 定律的经验规律。为揭示所再现聚集现象背后的机制，我们利用微观模型推导了一个扩展的 Keller–Segel 系统，该系统是描述生物有机体被称为趋性的聚集行为的经典模型。所得宏观系统表明，现实世界中劳动力的集中可以通过一种对人类行为至关重要的新型趋性来解释，这突显了城市化与所推导 PDE 系统中爆发现象之间的关联。随后，我们从解析和计算两方面刻画了聚集态与扩散态之间的转变。研究预测的长期城市化动态——源于就业与失业智能体的非对称性质——与全球经验数据进行了比较，尤其是在劳动力统计和城市指标领域。
英文摘要：
Individual participants in human society collectively exhibit aggregation behavior. In this study, we present a simple microscopic model of labor force migration based on the active Brownian particles framework. In particular, agent-based simulations show that the model produces clusters of agents from a random initial distribution. Furthermore, two empirical regularities called Zipf's and Okun's laws were observed. To reveal the mechanism underlying the reproduced aggregation phenomena, we use our microscopic model to derive an extended Keller--Segel system, which is a classic model describing the aggregation behavior of biological organisms called taxis. The obtained macroscopic system indicates that the concentration of the workforce in the real world can be explained through a new type of taxis central to human behavior, highlighting the relevance of urbanization to blow-up phenomena in the derived PDE system. We then characterize the transition between the aggregation and diffusion regimes both analytically and computationally. The predicted long-term dynamics of urbanization -- originating in the asymmetric natures of employed and unemployed agents -- are compared with global empirical data, particularly in the realms of labor statistics and urban indicators.
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