Can ABM-PDE hybrids speed up disease spread simulations?
A HYBRID ABM-PDE FRAMEWORK FOR REAL-WORLD INFECTIOUS DISEASE SIMULATIONS
April 14, 2025
https://arxiv.org/pdf/2504.08430This paper proposes a hybrid model combining agent-based modeling (ABM) and partial differential equations (PDEs) to simulate infectious disease spread more efficiently than a pure ABM. The PDE models densely populated urban areas (Berlin) at a macro level, while the ABM models the surrounding rural areas (Brandenburg) with individual agents, using mobility data from mobile phones. The two models are coupled by exchanging agents/density at the interface between regions.
While not directly about LLMs, key relevant points for multi-agent systems include:
- Hybrid modeling: Combining different modeling approaches (like agents and PDEs) can improve efficiency and address varying scales of complexity in a multi-agent system, especially when dealing with heterogeneous environments or limited computational resources.
- Coupling mechanisms: Dynamically exchanging information and entities between different sub-models is crucial for maintaining consistency and realistic interactions within a hybrid multi-agent system. The paper explores techniques for agent/density conversion at interfaces.
- Scalability: The hybrid approach aims to improve the scalability of agent-based simulations, a common challenge in complex multi-agent systems. This is relevant to LLM-based agents, which can be computationally expensive.
- Calibration and Validation: The paper highlights the challenges of parameter fitting in hybrid models and discusses using real-world data for validation, important aspects for building robust multi-agent systems with LLMs.
- Emergent Behavior: Although not explicitly addressed, the interaction of individual agents in the ABM and their influence on the PDE demonstrates a form of emergent behavior relevant to multi-agent LLM systems where collective agent behavior shapes the system's evolution.