Modeling Presymptomatic Spread in Epidemics via Mean-Field Games

被引：0
|
作者：
Olmez, S. Yagiz [1 ]
Aggarwal, Shubham [1 ]
Kim, Jin Won [1 ]
Miehling, Erik [1 ]
Basar, Tamer [1 ]
West, Matthew [1 ]
Mehta, Prashant G. [1 ]
机构：
[1] Univ Illinois, Coordinated Sci Lab, Urbana, IL 61820 USA
来源：
2022 AMERICAN CONTROL CONFERENCE, ACC | 2022年
基金：
美国国家科学基金会;
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper is concerned with developing mean-field game models for the evolution of epidemics. Specifically, an agent's decision - to be socially active in the midst of an epidemic - is modeled as a mean-field game with health-related costs and activity-related rewards. By considering the fully and partially observed versions of this problem, the role of information in guiding an agent's rational decision is highlighted. The main contributions of the paper are to derive the equations for the mean-field game in both fully and partially observed settings of the problem, to present a complete analysis of the fully observed case, and to present some analytical results for the partially observed case.
引用
收藏
页码：3648 / 3655
页数：8
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