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home / papers / 2026 / Datamart-Agent: LLM-Driven Game-Theoretic Agent for Data Marketplace Modeling

Datamart-Agent: LLM-Driven Game-Theoretic Agent for Data Marketplace Modeling

Paper · 2026 · Pangjing Wu, Peter Q. Chen, Xiaodong Li et al.

Summary

Most theoretical work on data marketplaces assumes a single round of trading and full information. That makes realistic settings, with repeated interaction and hidden buyer valuations, analytically hard to solve. The authors ask whether LLM-driven agents can still make decisions that agree with game-theoretic equilibria in such markets. They build EvoDM, an agent-based simulation of a three-layer user–platform–buyer marketplace extending the static model of Fallah et al. (2024). For simplified CI, Evolving and β-INC regimes they derive closed-form equilibria, which serve as ground truth. On top of this they design Datamart-Agent. It relies on role-conditioned prompts, a dynamic game tree memory with retrieval of similar past trajectories, and mechanism-guided reflection, and it needs no fine-tuning. Across the tractable settings, the agent comes closest to the theoretical equilibria, outperforming RL-based and LLM-based baselines. The validated agents are then used for exploratory studies of competition and privacy regulation where no exact solution exists. This shows how LLM simulations can support data-market policy analysis.

Key points

  • EvoDM models the market as a multi-agent Markov game over T rounds, with platforms entering and exiting. It gives analytical equilibrium benchmarks for the CI, Evolving and β-INC settings (β is the buyer's private valuation). Utilities are based on mutual information under Gaussian signals.

  • Datamart-Agent achieves the lowest Utility Gap of all compared methods. Its Pass@ϵ is more than 20% higher than strong ABM baselines.

  • In the simulations, strict data-deletion rules raise user utility, and dynamic pricing helps both buyers and platforms.

  • Platform lifespan inequality was measured with the Gini coefficient under four privacy regulation regimes:

    RegimeGini
    No regulation0.256
    Privacy ban0.435
    Uniform mandate (σ=0.5)0.387
    Cost-tiered mandate0.259

    The cost-tiered mandate is the fairest of the three regulated options.

  • In the experiments, privacy-sensitive (high-α) users concentrate their sharing on a few trusted privacy-first platforms as such platforms become more common. Low-α users show little sensitivity to market composition.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Hong Kong Polytechnic UniversityHong Kong · China106 works · 5,190 citations🎓Hohai UniversityNanjing · China4 works · 5 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksDecision Sciences · 249 worksManagement Science and Operations Research · 134 worksEconomics, Econometrics and Finance · 23 worksEconomics and Econometrics · 16 works

Cite

Cite as Pangjing Wu, Peter Q. Chen, Xiaodong Li, Wenqi Fan and Li Qing. 2026. Datamart-Agent: LLM-Driven Game-Theoretic Agent for Data Marketplace Modeling. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1628

@inproceedings{wu2026datamart,
  title = {{Datamart-Agent: LLM-Driven Game-Theoretic Agent for Data Marketplace Modeling}},
  author = {Pangjing Wu, Peter Q. Chen, Xiaodong Li, Wenqi Fan and Li Qing},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1628},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1628}
}

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