2025/10/20 by Man-Lin Chu, Chu, Man-Lin, Lucian Terhorst +9 · 1 voice · 1 citation
Decision Sciences · Physics and Astronomy · Social Sciences · #Innovation Diffusion and Forecasting #Opinion Dynamics and Social Influence #Language and cultural evolution
paper · pdf · doi:10.48550/arxiv.2510.18155
Simulating consumer decision-making is vital for designing and evaluating marketing strategies before costly real-world deployment. However, post-event analyses and rule-based agent-based models (ABMs) struggle to capture the complexity of human behavior and social interaction. We introduce an LLM-powered multi-agent simulation framework that models consumer decisions and social dynamics. Building on recent advances in large language model simulation in a sandbox environment, our framework enables generative agents to interact, express internal reasoning, form habits, and make purchasing decisions without predefined rules. In a price-discount marketing scenario, the system delivers actionable strategy-testing outcomes and reveals emergent social patterns beyond the reach of conventional methods. This approach offers marketers a scalable, low-risk tool for pre-implementation testing, reducing reliance on time-intensive post-event evaluations and lowering the risk of underperforming campaigns.