Barriers to Healthcare: Agent-Based Modeling to Mitigate Inequity

Abstract: Agent-based simulations have an enormous potential as tools to evaluate social policies in a non-invasive way, before these are implemented to real-world populations. However, the recommendations that these computational approaches may offer to tackle urgent human development challenges can vary substantially depending on how we model agents' (people) behaviour and the criteria that we use to measure inequity. In this paper, we integrate the conceptual framework of the capability approach (CA), which is explicitly designed to promote and assess human well-being, to guide the simulation and evaluate the effectiveness of policies. We define a reinforcement learning environment where agents behave to restore their capabilities under the constraints of a specific policy. Working in collaboration with local stakeholders, non-profits and domain experts, we apply our model in a case study to mitigate health inequity among the population experiencing homelessness (PEH) in Barcelona. By doing so, we present the first proof of concept simulation, aligned with the CA for human development, to assess the impact of policies under parliamentary discussion.