Agents Trusting Agents? Restoring Lost Capabilities with Inclusive Healthcare

TL;DR

This paper uses agent-based simulations and Bayesian inverse reinforcement learning to model and evaluate policies aimed at improving healthcare equity for homeless populations in Barcelona.

Contribution

It integrates the capability approach into an RL environment to assess trust and engagement between homeless individuals and social workers.

Findings

Model shows how trust influences policy success.

Simulation suggests building trust can mitigate health inequity.

Calibrated parameters reflect real-world behavioral profiles.

Abstract

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.