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    <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@ε 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.</abstract>
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%0 Conference Proceedings
%T Datamart-Agent: LLM-Driven Game-Theoretic Agent for Data Marketplace Modeling
%A Wu, Pangjing
%A Chen, Peter Q.
%A Li, Xiaodong
%A Fan, Wenqi
%A Qing, Li
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wu-etal-2026-datamart
%X 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@ε 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.
%U https://aclanthology.org/2026.findings-acl.1628/
%P 32509-32531

Markdown (Informal)

[Datamart-Agent: LLM-Driven Game-Theoretic Agent for Data Marketplace Modeling](https://aclanthology.org/2026.findings-acl.1628/) (Wu et al., Findings 2026)

• Datamart-Agent: LLM-Driven Game-Theoretic Agent for Data Marketplace Modeling (Wu et al., Findings 2026)

ACL

• Pangjing Wu, Peter Q. Chen, Xiaodong Li, Wenqi Fan, and Li Qing. 2026. Datamart-Agent: LLM-Driven Game-Theoretic Agent for Data Marketplace Modeling. In Findings of the Association for Computational Linguistics: ACL 2026, pages 32509–32531, San Diego, California, United States. Association for Computational Linguistics.