147165 20260217205540.0 doi 10.1016/j.plrev.2024.10.013 sideral 141001 ART-2024-141001 eng Lu, Yikang LLMs and generative agent-based models for complex systems research 2024 Access copy available to the general public Unrestricted The advent of Large Language Models (LLMs) offers to transform research across natural and social sciences, offering new paradigms for understanding complex systems. In particular, Generative Agent-Based Models (GABMs), which integrate LLMs to simulate human behavior, have attracted increasing public attention due to their potential to model complex interactions in a wide range of artificial environments. This paper briefly reviews the disruptive role LLMs are playing in fields such as network science, evolutionary game theory, social dynamics, and epidemic modeling. We assess recent advancements, including the use of LLMs for predicting social behavior, enhancing cooperation in game theory, and modeling disease propagation. The findings demonstrate that LLMs can reproduce human-like behaviors, such as fairness, cooperation, and social norm adherence, while also introducing unique advantages such as cost efficiency, scalability, and ethical simplification. However, the results reveal inconsistencies in their behavior tied to prompt sensitivity, hallucinations and even the model characteristics, pointing to challenges in controlling these AI-driven agents. Despite their potential, the effective integration of LLMs into decision-making processes —whether in government, societal, or individual contexts— requires addressing biases, prompt design challenges, and understanding the dynamics of human-machine interactions. Future research must refine these models, standardize methodologies, and explore the emergence of new cooperative behaviors as LLMs increasingly interact with humans and each other, potentially transforming how decisions are made across various systems. info:eu-repo/grantAgreement/ES/DGA/E36-20R info:eu-repo/grantAgreement/ES/MICINN/PID2023-149409NB-I00 info:eu-repo/grantAgreement/ES/MICINN/RYC2021-033226-I info:eu-repo/semantics/openAccess by-nc https://creativecommons.org/licenses/by-nc/4.0/deed.es 14.3 2024 BIOPHYSICS 1 / 79 = 0.013 2024 Q1 T1 BIOLOGY 1 / 107 = 0.009 2024 Q1 T1 2.384 2024 Agricultural and Biological Sciences (miscellaneous) 2024 Q1 Physics and Astronomy (miscellaneous) 2024 Q1 Artificial Intelligence 2024 Q1 17.2 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Aleta, Alberto Universidad de Zaragoza (orcid)0000-0002-1192-8707 Du, Chunpeng Shi, Lei Moreno, Yamir Universidad de Zaragoza (orcid)0000-0002-0895-1893 2004 405 Universidad de Zaragoza Dpto. Física Teórica Área Física Teórica 51 (2024), 283-293 Physics of Life Reviews Physics of Life Reviews 1571-0645 1068259 http://zaguan.unizar.es/record/147165/files/texto_completo.pdf Versión publicada 1735557 http://zaguan.unizar.es/record/147165/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:147165 articulos driver 2026-02-17-20:35:53 ARTICLE