Epidemic population games and evolutionary dynamics
Epidemic population games and evolutionary dynamics
复制标题
流行病群体博弈与进化动力学
DOI：
10.1016/j.automatica.2023.111016
复制
发表时间：
2023
期刊：
影响因子：
6.4
通讯作者：
La, Richard J.
中科院分区：
文献类型：
--
作者：
Martins, Nuno C.;Certório, Jair;La, Richard J.
我们提出了一种系统理论方法来选择和稳定 SIRS 流行病模型的流行平衡，其中策略性相互作用的主体群体的决策决定了传播率。具体来说，民众的代理人会不断地从一系列影响不同程度传播率的策略中修改他们的选择。扣除策略的内在成本后，量化规划者为每个策略提供的激励的回报向量会影响修订过程。进化动力学模型通过将代理人的选择流向具有更高回报的策略的速率指定为回报向量的函数，来捕获群体在修正过程中的偏好。我们的主要结果是一个动态的回报机制，保证在成本限制下将流行病变量（通过对人群的激励）引导到具有最小感染比例的流行平衡。我们使用李雅普诺夫函数不仅可以建立收敛性，还可以获取群体感染部分峰值大小的（任意）上限。
We propose a system theoretic approach to select and stabilize the endemic equilibrium of an SIRS epidemic model in which the decisions of a population of strategically interacting agents determine the transmission rate. Specifically, the population’s agents recurrently revise their choices out of a set of strategies that impact to varying levels the transmission rate. A payoff vector quantifying the incentives provided by a planner for each strategy, after deducting the strategies’ intrinsic costs, influences the revision process. An evolutionary dynamics model captures the population’s preferences in the revision process by specifying as a function of the payoff vector the rates at which the agents’ choices flow toward strategies with higher payoffs. Our main result is a dynamic payoff mechanism that is guaranteed to steer the epidemic variables (via incentives to the population) to the endemic equilibrium with the smallest infectious fraction, subject to cost constraints. We use a Lyapunov function not only to establish convergence but also to obtain an (anytime) upper bound for the peak size of the population’s infectious portion.