Incentivizing Truthful Submissions in a Data Marketplace for Mean Estimation

Keran Chen, Alex Clinton, Kirthevasan Kandasamy
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:2152-2160, 2026.

Abstract

We study a data marketplace where a broker intermediates between buyers, who seek to estimate the mean $\mu$ of an unknown normal distribution $N(\mu, \sigma^2)$, and contributors, who can collect data from this distribution at a cost. The broker delegates data collection work to contributors, aggregates reported datasets, sells it to buyers, and redistributes revenue as payments to contributors. We aim to maximize welfare or profit under key constraints: individual rationality for buyers and contributors, incentive compatibility (contributors are incentivized to comply with data collection instructions and truthfully report the collected data), and budget balance (total contributor payments equals total revenue). We first compute welfare/profit-optimal prices under truthful reporting; however, to incentivize data collection and truthful data reporting, we adjust them based on discrepancies in contributors’ reported data. This yields a Nash equilibrium (NE) where the two lowest-cost contributors collect all data. We complement this with two hardness results: $\mathcal{(i)}$ no nontrivial dominant-strategy incentive-compatible mechanism exists in this problem, and $\mathcal{(ii)}$ no mechanism outperforms ours in a NE.

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-chen26c, title = { Incentivizing Truthful Submissions in a Data Marketplace for Mean Estimation }, author = {Chen, Keran and Clinton, Alex and Kandasamy, Kirthevasan}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {2152--2160}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/chen26c/chen26c.pdf}, url = {https://proceedings.mlr.press/v300/chen26c.html}, abstract = { We study a data marketplace where a broker intermediates between buyers, who seek to estimate the mean $\mu$ of an unknown normal distribution $N(\mu, \sigma^2)$, and contributors, who can collect data from this distribution at a cost. The broker delegates data collection work to contributors, aggregates reported datasets, sells it to buyers, and redistributes revenue as payments to contributors. We aim to maximize welfare or profit under key constraints: individual rationality for buyers and contributors, incentive compatibility (contributors are incentivized to comply with data collection instructions and truthfully report the collected data), and budget balance (total contributor payments equals total revenue). We first compute welfare/profit-optimal prices under truthful reporting; however, to incentivize data collection and truthful data reporting, we adjust them based on discrepancies in contributors’ reported data. This yields a Nash equilibrium (NE) where the two lowest-cost contributors collect all data. We complement this with two hardness results: $\mathcal{(i)}$ no nontrivial dominant-strategy incentive-compatible mechanism exists in this problem, and $\mathcal{(ii)}$ no mechanism outperforms ours in a NE. } }
Endnote
%0 Conference Paper %T Incentivizing Truthful Submissions in a Data Marketplace for Mean Estimation %A Keran Chen %A Alex Clinton %A Kirthevasan Kandasamy %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-chen26c %I PMLR %P 2152--2160 %U https://proceedings.mlr.press/v300/chen26c.html %V 300 %X We study a data marketplace where a broker intermediates between buyers, who seek to estimate the mean $\mu$ of an unknown normal distribution $N(\mu, \sigma^2)$, and contributors, who can collect data from this distribution at a cost. The broker delegates data collection work to contributors, aggregates reported datasets, sells it to buyers, and redistributes revenue as payments to contributors. We aim to maximize welfare or profit under key constraints: individual rationality for buyers and contributors, incentive compatibility (contributors are incentivized to comply with data collection instructions and truthfully report the collected data), and budget balance (total contributor payments equals total revenue). We first compute welfare/profit-optimal prices under truthful reporting; however, to incentivize data collection and truthful data reporting, we adjust them based on discrepancies in contributors’ reported data. This yields a Nash equilibrium (NE) where the two lowest-cost contributors collect all data. We complement this with two hardness results: $\mathcal{(i)}$ no nontrivial dominant-strategy incentive-compatible mechanism exists in this problem, and $\mathcal{(ii)}$ no mechanism outperforms ours in a NE.
APA
Chen, K., Clinton, A. & Kandasamy, K.. (2026). Incentivizing Truthful Submissions in a Data Marketplace for Mean Estimation . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:2152-2160 Available from https://proceedings.mlr.press/v300/chen26c.html.

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