Modeling Vaccination Decisions in Heterogeneous Populations

In classical vaccination game theory, population groups are typically treated as homogeneous, with individuals assumed to share identical disease risks, cost perceptions, and beliefs about vaccine performance. In this talk, we depart from this assumption and introduce a vaccination game model designed for an infinite population whose members exhibit heterogeneous preferences and characteristics. Specifically, we allow individuals to differ in three key dimensions: (1) their susceptibility to infection, (2) their perceived costs associated with both contracting the disease and receiving the vaccine, and (3) their subjective assessment of vaccine efficacy. We encode this heterogeneity using a quantile function that captures the distribution of agents’ net relative vaccination costs, providing a flexible and analytically tractable representation of diverse behavioral responses. Within this framework, we derive an explicit closed-form expression for the Nash equilibrium vaccination rate, offering transparent insights into how population-level behavior emerges from heterogeneous individual incentives. We then demonstrate how our model can be calibrated using real-world survey data to generate empirical predictions about vaccination uptake. A systematic comparison with the standard homogeneous model reveals that our heterogeneous approach yields consistently improved predictive accuracy, particularly in data-sparse settings where only a limited number of survey responses are available. Across a range of scenarios, the heterogeneous model not only reduces prediction error but also exhibits greater robustness to sampling variability and model misspecification. Overall, our results highlight the importance of accounting for preference diversity in vaccination games and illustrate how heterogeneity can significantly enhance the realism and reliability of epidemiological behavior models.