@inproceedings{wu-etal-2026-datamart,
    title = "Datamart-Agent: {LLM}-Driven Game-Theoretic Agent for Data Marketplace Modeling",
    author = "Wu, Pangjing  and
      Chen, Peter Q.  and
      Li, Xiaodong  and
      Fan, Wenqi  and
      Qing, Li",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1628/",
    pages = "32509--32531",
    ISBN = "979-8-89176-395-1",
    abstract = "Data marketplaces analyze strategic data exchanges among users, platforms, and buyers. However, most existing studies model static equilibria and complete information, which limits their realism. In this work, we study whether large language model (LLM)-driven agents can make equilibrium-consistent decisions in analytically tractable data marketplaces with evolving and incomplete-information. Specifically, we introduce EvoDM, an agent-based modeling framework that extends the static data marketplace to dynamic and incomplete-information settings while providing tractable equilibrium benchmarks for evaluating agent decisions. Building upon EvoDM, we propose Datamart-Agent, an LLM-driven game-theoretic agent that improves equilibrium-consistent decision execution through dynamic game tree memory and mechanism-guided reflection, without requiring parameter updates. Experiments demonstrate that Datamart-Agent closely matches equilibrium-consistent decision-making, achieving the lowest utility gap and over 20{\%} higher Pass@$\epsilon$ than strong baselines. After validating its effectiveness, we employ EvoDM with Datamart-Agent to analyze competition and regulation in assumption-relaxed settings where closed-form ground truth is unavailable, providing exploratory simulation-based insights into market dynamics and regulatory effects."
}