From Survey Personas to LLM Agents: A Generative Agent-based Simulation of Mobility Policy Preference Dynamics

Research from arXiv introduces a novel Generative Agent-based Modeling (GABM) framework that translates real survey respondents into generative Large Language Model (LLM) agents. This framework aims to enhance the realism of decision-making simulations by addressing the limitation of hand-crafted personas, which have historically shaped agent interpretation and decisions. The core objective is to demonstrate that meticulous persona design, grounded in empirical data, can facilitate more accurate behavioral experiments using LLMs.

Why it matters

This development is strategically important as it enhances the fidelity of simulations for understanding complex human behaviors and policy impacts. By grounding LLM agents in real survey data, organizations can develop more accurate predictive models for policy effectiveness, market responses, and social dynamics, leading to more informed strategic planning and decision-making.

Key insights

• Large Language Models (LLMs) are increasingly used for simulating complex social interactions.
• Existing LLM simulation studies often rely on hand-crafted personas, which can limit accuracy.
• Persona design significantly influences how LLM agents interpret context and make decisions.
• A new survey-grounded GABM framework has been proposed to translate real survey respondents into LLM agents.
• The framework aims to improve the realism of decision-making simulations and behavioral experiments through empirically-grounded persona design.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.07519