Agents Trusting Agents? Restoring Lost Capabilities with Inclusive Healthcare
For policymakers and social service organizations, this provides a non-invasive simulation tool to evaluate policies for improving healthcare equity among homeless populations.
This paper uses agent-based simulations with Bayesian inverse reinforcement learning to model trust between people experiencing homelessness and social workers in Barcelona, aiming to evaluate policies for improving healthcare equity. The approach calibrates behavioral parameters to restore capabilities, offering a path to mitigate health inequity.
Agent-based simulations have an untapped potential to inform social policies on urgent human development challenges in a non-invasive way, before these are implemented in real-world populations. This paper responds to the request from non-profit and governmental organizations to evaluate policies under discussion to improve equity in health care services for people experiencing homelessness (PEH) in the city of Barcelona. With this goal, we integrate the conceptual framework of the capability approach (CA), which is explicitly designed to promote and assess human well-being, to model and evaluate the behaviour of agents who represent PEH and social workers. We define a reinforcement learning environment where agents aim to restore their central human capabilities, under existing environmental and legal constraints. We use Bayesian inverse reinforcement learning (IRL) to calibrate profile-dependent behavioural parameters in PEH agents, modeling the degree of trust and engagement with social workers, which is reportedly a key element for the success of the policies in scope. Our results open a path to mitigate health inequity by building relationships of trust between social service workers and PEH.