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    <abstract>Language-driven generative agents have enabled large-scale social simulations with transformative uses, from interpersonal training to aiding global policy-making. However, recent studies indicate that generative agent behaviors often deviate from expert expectations and real-world data—a phenomenon we term the *Behavior-Realism Gap*. To address this, we introduce a theoretical framework called Persona-Environment Behavioral Alignment (PEBA), formulated as a distribution matching problem grounded in Lewin’s behavior equation stating that behavior is a function of the person and their environment. Leveraging PEBA, we propose PersonaEvolve (PEvo), an LLM-based optimization algorithm that iteratively refines agent personas, implicitly aligning their collective behaviors with realistic expert benchmarks within a specified environmental context. We validate PEvo in an active shooter incident simulation we developed, achieving an 84% average reduction in distributional divergence compared to no steering and a 34% improvement over explicit instruction baselines. Results also show PEvo-refined personas generalize to novel, related simulation scenarios. Our method greatly enhances behavioral realism and reliability in high-stakes social simulations. More broadly, the PEBA-PEvo framework provides a principled approach to developing trustworthy LLM-driven social simulations.</abstract>
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%0 Conference Proceedings
%T Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations
%A Wang, Yunzhe
%A Lucas, Gale
%A Becerik-Gerber, Burcin
%A Ustun, Volkan
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F wang-etal-2025-implicit
%X Language-driven generative agents have enabled large-scale social simulations with transformative uses, from interpersonal training to aiding global policy-making. However, recent studies indicate that generative agent behaviors often deviate from expert expectations and real-world data—a phenomenon we term the *Behavior-Realism Gap*. To address this, we introduce a theoretical framework called Persona-Environment Behavioral Alignment (PEBA), formulated as a distribution matching problem grounded in Lewin’s behavior equation stating that behavior is a function of the person and their environment. Leveraging PEBA, we propose PersonaEvolve (PEvo), an LLM-based optimization algorithm that iteratively refines agent personas, implicitly aligning their collective behaviors with realistic expert benchmarks within a specified environmental context. We validate PEvo in an active shooter incident simulation we developed, achieving an 84% average reduction in distributional divergence compared to no steering and a 34% improvement over explicit instruction baselines. Results also show PEvo-refined personas generalize to novel, related simulation scenarios. Our method greatly enhances behavioral realism and reliability in high-stakes social simulations. More broadly, the PEBA-PEvo framework provides a principled approach to developing trustworthy LLM-driven social simulations.
%U https://aclanthology.org/2025.emnlp-main.1562/
%P 30669-30686

Markdown (Informal)

[Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations](https://aclanthology.org/2025.emnlp-main.1562/) (Wang et al., EMNLP 2025)

• Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations (Wang et al., EMNLP 2025)

ACL

• Yunzhe Wang, Gale Lucas, Burcin Becerik-Gerber, and Volkan Ustun. 2025. Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 30669–30686, Suzhou, China. Association for Computational Linguistics.