Game-Theoretic Vaccination Against Networked SIS Epidemics and Impacts of Human Decision-Making
For researchers modeling epidemic spread and human decision-making, this work provides a game-theoretic analysis of how behavioral biases affect vaccination equilibria, though the results are incremental as they extend existing frameworks.
This paper studies decentralized vaccination strategies against SIS epidemics on networks, incorporating human behavioral biases in risk perception. It shows that such biases can reduce vaccination uptake when costs are high, and quantifies the effect using tight bounds on equilibrium thresholds for power-law networks.
We study decentralized protection strategies against Susceptible-Infected-Susceptible (SIS) 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.