Alex Clinton

Alex Clinton, Yiding Chen, Xiaojin Zhu et al.

We study a collaborative learning problem where $m$ agents aim to estimate a vector $μ=(μ_1,\ldots,μ_d)\in \mathbb{R}^d$ by sampling from associated univariate normal distributions $\{\mathcal{N}(μ_k, σ^2)\}_{k\in[d]}$. Agent $i$ incurs a cost $c_{i,k}$ to sample from $\mathcal{N}(μ_k, σ^2)$. Instead of working independently, agents can exchange data, collecting cheaper samples and sharing them in return for costly data, thereby reducing both costs and estimation error. We design a mechanism to facilitate such collaboration, while addressing two key challenges: ensuring individually rational (IR) and fair outcomes so all agents benefit, and preventing strategic behavior (e.g. non-collection, data fabrication) to avoid socially undesirable outcomes. We design a mechanism and an associated Nash equilibrium (NE) which minimizes the social penalty-sum of agents' estimation errors and collection costs-while being IR for all agents. We achieve a $\mathcal{O}(\sqrt{m})$-approximation to the minimum social penalty in the worst case and an $\mathcal{O}(1)$-approximation under favorable conditions. Additionally, we establish three hardness results: no nontrivial mechanism guarantees (i) a dominant strategy equilibrium where agents report truthfully, (ii) is IR for every strategy profile of other agents, (iii) or avoids a worst-case $Ω(\sqrt{m})$ price of stability in any NE. Finally, by integrating concepts from axiomatic bargaining, we demonstrate that our mechanism supports fairer outcomes than one which minimizes social penalty.

Keran Chen, Alex Clinton, Kirthevasan Kandasamy

We study a data marketplace where a broker intermediates between buyers, who seek to estimate the mean \(μ\) of an unknown normal distribution \(\Ncal(μ, σ^2)\), 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: \emph{(i)} no nontrivial dominant-strategy incentive-compatible mechanism exists in this problem, and \emph{(ii)} no mechanism outperforms ours in a NE.

Alex Clinton, Thomas Zeng, Yiding Chen et al.

Modern data marketplaces and data sharing consortia increasingly rely on incentive mechanisms to encourage agents to contribute data. However, schemes that reward agents based on the quantity of submitted data are vulnerable to manipulation, as agents may submit fabricated or low-quality data to inflate their rewards. Prior work has proposed comparing each agent's data against others' to promote honesty: when others contribute genuine data, the best way to minimize discrepancy is to do the same. Yet prior implementations of this idea rely on very strong assumptions about the data distribution (e.g. Gaussian), limiting their applicability. In this work, we develop reward mechanisms based on a novel, two-sample test inspired by the Cramér-von Mises statistic. Our methods strictly incentivize agents to submit more genuine data, while disincentivizing data fabrication and other types of untruthful reporting. We establish that truthful reporting constitutes a (possibly approximate) Nash equilibrium in both Bayesian and prior-agnostic settings. We theoretically instantiate our method in three canonical data sharing problems and show that it relaxes key assumptions made by prior work. Empirically, we demonstrate that our mechanism incentivizes truthful data sharing via simulations and on real-world language and image data.