---
title: "Datamart-Agent — paper by Wu et al. (2026)"
description: "They build EvoDM, an agent-based simulation of a three-layer user–platform–buyer marketplace extending the static model of… 73 references."
authors: ["Pangjing Wu", "Peter Q. Chen", "Xiaodong Li", "Wenqi Fan", "Li Qing"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1628/"
doi: "10.18653/v1/2026.findings-acl.1628"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wu-2026-datamart-agent-llm-driven-game/"
---
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# Datamart-Agent: LLM-Driven Game-Theoretic Agent for Data Marketplace Modeling

**Authors:** Pangjing Wu, Peter Q. Chen, Xiaodong Li, [Wenqi Fan](https://md.research.elamur.ai/free/en/authors/wenqi-fan/) and [Li Qing](https://md.research.elamur.ai/free/en/authors/li-qing/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1628/) · [PDF](https://aclanthology.org/2026.findings-acl.1628.pdf) · [DOI 10.18653/v1/2026.findings-acl.1628](https://doi.org/10.18653/v1/2026.findings-acl.1628) · [OpenAlex](https://openalex.org/W7166850796)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## 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:

  | Regime | Gini |
  |---|---|
  | No regulation | 0.256 |
  | Privacy ban | 0.435 |
  | Uniform mandate (σ=0.5) | 0.387 |
  | Cost-tiered mandate | 0.259 |

  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 University](https://md.research.elamur.ai/free/en/universities/hong-kong-polytechnic-university/) — University, Hong Kong, China · 106 works · 5,190 citations
- [Hohai University](https://md.research.elamur.ai/free/en/universities/hohai-university/) — University, Nanjing, China · 4 works · 5 citations

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## 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.

```bibtex
@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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*Updated 2 November 2026*