Humanized Agent-based Models: a Framework
Parfait Atchade-Adelomou, Adrian Mora, Luis Alonso-Pastor, Arnaud Grignard, Ariel Noyman, Leticia Izquierdo, Carlo Adornetto, Kai Hu, Fernando Fernandez, Hossein Rahnama, Margaret Church, Markus Elkatsha, Kent Larson · 2025
This paper introduces Humanized Agent-Based Models (h-ABM), a conceptual approach integrating Large Language Models (LLMs) into Agent-Based Models (ABMs) to enhance the realism of human behavior in simulations. Recent works have demonstrated the capabilities of LLMs for creating powerful agents; nevertheless, they don’t propose a framework for defining agents and guiding their integration into ABMs. Surveying previous work in the field, and from the findings of LLM agents, ABM literature, and cognitive frameworks, h-ABM proposes a modular framework for LLM agents in the context of ABMs. We finally compare the framework with other LLM agents’ proposals to see how they fit intdfsfdsdfssfdm