The paper tackles the data market design problem, which seeks signaling schemes that maximize revenue for an information seller. It applies deep learning to learn these schemes, addressing both obedience and incentive constraints, and demonstrates that the framework can replicate known theoretical solutions, extend to more complex scenarios, and suggest new optimal designs. The study builds on prior auction‑design work and introduces a novel approach for revenue‑optimal data markets.
By Sai Srivatsa Ravindranath, Yanchen Jiang, David C. Parkes
arXiv:2607. 01185v1 Announce Type: new Abstract: Combinatorial optimization (CO) problems are difficult because certifiable discrete structure induces exponential search.
By Jingyi Chen, Xinyuan Zhang, Xinwu Qian
arXiv:2606. 28943v1 Announce Type: cross Abstract: Learning to bid in repeated multi-unit auctions with bandit feedback poses a fundamental challenge.
By Junhan Li, Yuxin Zhang, Haoran Wang, Minghao Chen
The paper introduces Correlation-Aware Affine Maximizer Auctions (CA-AMA), a new framework that extends traditional AMAs by incorporating a correlation-aware payment structure. CA-AMA maintains dominant-strategy incentive compatibility and is formulated as a constraint optimization problem with individual rationality constraints. The authors theoretically demonstrate that CA-AMA can achieve optimal revenue in scenarios where classic AMAs perform poorly, and they present a practical two-stage training algorithm that empirically finds near-optimal CA-AMA solutions with improved revenue and minimal IR violations.
By Haoran Sun, Xuanzhi Xia, Xu Chu, Xiaotie Deng
arXiv:2606. 29252v1 Announce Type: new Abstract: We study repeated bidding in multi-unit discriminatory (pay-as-bid) auctions for a single bidder with per-round utility equal to value minus $\alpha$ times payment, where $\alpha\in[0,1]$ is a cost-of-capital parameter.
By Negin Golrezaei, Sourav Sahoo
We study repeated bidding in multi-unit discriminatory (pay-as-bid) auctions for a single bidder with per-round utility equal to value minus $α$ times payment, where $α\in[0,1]$ is a cost-of-capital parameter. The bidder aims to maximize cumulative utility over $T$ rounds subject to a total budget $B$.