Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations

• URL: http://arxiv.org/abs/2509.16457v1
• Date: Fri, 19 Sep 2025 22:35:13 GMT
• Title: Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations
• Authors: Yunzhe Wang, Gale M. Lucas, Burcin Becerik-Gerber, Volkan Ustun,
• Abstract summary: Language-driven generative agents have enabled social simulations with transformative uses, from interpersonal training to aiding global policy-making.<n>Recent studies indicate that generative agent behaviors often deviate from expert expectations and real-world data--a phenomenon we term the Behavior-Realism Gap.<n>We introduce a theoretical framework called Persona-Environment Behavioral Alignment (PEBA), formulated as a distribution matching problem grounded in Lewin's behavior equation.<n>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.
• Score: 3.0112218223206173
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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