Game-Theoretic Vaccination Against Networked SIS Epidemics and Impacts of Human Decision-Making

被引:48
作者
Hota, Ashish R. [1 ,2 ]
Sundaram, Shreyas [3 ]
机构
[1] Swiss Fed Inst Technol, Automat Control Lab, CH-8092 Zurich, Switzerland
[2] Indian Inst Technol Kharagpur, Dept Elect Engn, Kharagpur 721302, W Bengal, India
[3] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA
来源
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS | 2019年 / 6卷 / 04期
基金
美国国家科学基金会;
关键词
Behavioral decision-making; cyber-physical and human systems; epidemics; game theory; network security; prospect theory; PROSPECT-THEORY; INTERDEPENDENT SECURITY; PROTECTION; SYSTEMS;
D O I
10.1109/TCNS.2019.2897904
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we study decentralized protection strategies against susceptible-infected-susceptible epidemics on networks. We consider a population game framework where nodes choose whether or not to vaccinate themselves, and the epidemic risk is defined as the infection probability at the endemic state of the epidemic under a degree-based mean-field approximation. Motivated by studies in behavioral economics showing that humans perceive probabilities and risks in a nonlinear fashion, we specifically examine the impacts of such misperceptions on the Nash equilibrium protection strategies. We first establish the existence and uniqueness of a threshold equilibrium where nodes with degrees larger than a certain threshold vaccinate. When the vaccination cost is sufficiently high, we show that behavioral biases cause fewer players to vaccinate, and vice versa. We quantify this effect for a class of networks with power-law degree distributions by proving tight bounds on the ratio of equilibrium thresholds under behavioral and true perceptions of probabilities. We further characterize the socially optimal vaccination policy and investigate the inefficiency of Nash equilibrium.
引用
收藏
页码:1461 / 1472
页数:12
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