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September 12, 2025Open Access

Humanized Agent-based Models: a Framework

Key Points

The proposed framework enhances realism by integrating large language models into agent-based models, improving simulation outcomes.
The comparison with other agent proposals indicates that humanized agent-based models represent a notable advance in simulation techniques.
A detailed survey of existing literature reveals the lack of a structured approach for integrating LLMs into agent-based models effectively.
The framework outlines specific guidelines to define agents and their roles within the context of agent-based models.

Abstract

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

Humanized Agent-based Models: a Framework

Key Points

Abstract

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