Barriers to Healthcare: Agent-Based Modeling to Mitigate Inequity

2025-07-31

AI Summary

This study addresses health inequities in healthcare access for people experiencing homelessness (PEH) in Barcelona by proposing a novel policy evaluation paradigm integrating the Capability Approach (CA) with multi-agent reinforcement learning. Methodologically, it introduces “actual functioning achievement” as the core evaluation dimension, constructing an agent-based model (ABM) simulation environment calibrated with real-world data and domain expert knowledge; the ABM explicitly models capability deprivation, dynamically measures inequality, and simulates policy interventions. Contributions include: (1) the first formal integration of CA theory into agent-based modeling; (2) generation of quantifiable, interpretable evidence on fairness impacts to inform parliamentary policy deliberations; and (3) development of a prototype policy evaluation system grounded in local stakeholder collaboration and designed for cross-city scalability.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Learning Human Values and Preferences

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

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.

Problem

• Evaluate healthcare policies using agent-based modeling to reduce inequity
• Integrate capability approach to guide simulations and measure well-being
• Assess policy impact on homeless population health inequity in Barcelona

Innovation

• Agent-based modeling for policy evaluation
• Capability approach framework integration
• Reinforcement learning for capability restoration