论文标题
部分可观测时空混沌系统的无模型预测
A Mean Field Game model for COVID-19 with human capital accumulation
论文作者
论文摘要
在本手稿中，我们提出了几种可能的方法来建模基于代理的方法，在疾病传播过程中，代理人的行为最大化了其跨期实用性。我们假设代理之间的相互作用是平均场类型，从而产生了问题的平均现场游戏描述。我们讨论了对模型的分析如何包括从一个流行病学状态到另一种的物种变化的机理，以及每个代理的优化问题都会导致综合行为不容易描述，并且有时会出现结构性问题。因此，我们最终在数值上提出和研究了一个SEIRD模型，其中感染率取决于人口的分布，作为对平均现场游戏系统的解决方案，作为对离散多代理经济模型的宏观描述，用于人类资本的积累。这种模型实际上是作为简化但可拖动的初始版本而产生的。
In this manuscript we present several possible ways of modeling human capital accumulation during the spread of a disease following an agent based approach, where agents behave maximizing their intertemporal utility. We assume that the interaction between agents is of mean field type, yielding a Mean Field Game description of the problem. We discuss how the analysis of a model including both the mechanism of change of species from one epidemiological state to the other and an optimization problem for each agent leads to an aggregate behavior that is not easy to describe, and that sometimes exhibits structural problems. Therefore we eventually propose and study numerically a SEIRD model in which the rate of infection depends on the distribution of the population, given exogenously as the solution to the the Mean Field Game system arising as the macroscopic description of the discrete multi-agent economic model for the accumulation of human capital. Such model arises in fact as a simplified but tractable version of the initial one.
