C. Adornetto et al., "Generative Agents in Agent-Based Modeling: Overview, Validation, and Emerging Challenges," in IEEE Transactions on Artificial Intelligence, doi: 10.1109/TAI.2025.3566362.

Abstract

The advent of Generative Agents (GAs) based on Large Language Models (LLMs) has significantly influenced the evolution of Agent-Based Modeling (ABM), offering new perspectives across various domains, including engineering and social sciences. This paper provides an extensive overview of the integration of GAs into ABMs, emphasizing the advancements and emerging challenges in their validation. Traditional ABMs, characterized by their simplistic yet powerful approach to modeling complex systems, have been redefined with the introduction of GAs. This new generation of agents is often equipped with conversational capabilities. These agents, capable of simulating believable human behaviors and interactions, present unique opportunities and hurdles, especially in urban simulations and social dynamics. We explore the nuanced differences between traditional ABMs and ABMs populated by GAs—called GABMs. We delve into the state-of-the-art implementations of GAs, and review various validation methods. Through this comprehensive examination, we aim to shed light on the potential and limitations of GAs, advocating for the design of hybrid ABM-GABM approaches and systematic validation.