Can ABM simulate homelessness policy using the Capability Approach?
Agent-based Modeling meets the Capability Approach for Human Development: Simulating Homelessness Policy-making
March 25, 2025
https://arxiv.org/pdf/2503.18389This paper proposes a novel approach to simulating homelessness policy-making by combining agent-based modeling (ABM) with the Capability Approach (CA) from human development theory. The CA focuses on assessing policies based on their impact on individuals' real opportunities (capabilities) rather than just resource allocation.
Key points for LLM-based multi-agent systems:
- CA as MDP: The CA is framed as a Markov Decision Process (MDP), providing a structured way to model agent decision-making based on capabilities, resources, and conversion factors. This aligns well with reinforcement learning and planning algorithms used in LLM-based agents.
- Values and Needs Integration: The framework incorporates values and needs as choice factors influencing agent behavior, adding a layer of psychological realism and complexity to agent motivations beyond simple reward maximization. LLMs could be leveraged to model these values and needs based on individual profiles and social context.
- Policy Evaluation: The ABM simulates the impact of different policies on individuals' capabilities, allowing for comparison and evaluation of policy effectiveness. This approach could leverage LLMs to generate and evaluate a wide range of policy options.
- Social Simulation: The focus on social context, interactions between stakeholders (PEH, social workers, non-profits), and legal/social norms opens up opportunities for complex social simulations using LLM-powered agents. This allows for studying emergent behavior and the complex interplay of individual actions and social structures.
- Real-World Data Integration: The plan to use real-world anonymized data and context-specific information suggests a data-driven approach to agent modeling and simulation. This is relevant to LLM-based agents, which can be trained and fine-tuned on such data to enhance their realism and predictive accuracy.