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.

翻译：基于智能体的模拟具有尚未开发的潜力，能够以非侵入性方式为紧迫的人类发展挑战提供社会政策参考，从而在现实人群中实施之前进行预评估。本文响应非营利组织和政府机构的请求，评估巴塞罗那市正在讨论的旨在改善无家可归者医疗保健服务公平性的政策。为此，我们整合了能力方法的概念框架——该框架明确旨在促进和评估人类福祉——以建模和评估代表无家可归者和社会工作者的智能体行为。我们定义了一个强化学习环境，其中智能体在现有环境和法律约束下致力于恢复其核心人类能力。我们采用贝叶斯逆强化学习来校准无家可归者智能体中依赖于个体特征的行为参数，建模其对社会工作者的信任与参与程度——据报道这是相关政策成功实施的关键要素。我们的研究结果为通过建立社会服务工作者与无家可归者之间的信任关系来缓解健康不平等开辟了路径。