{"ID":2895126,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.09473","arxiv_id":"2507.09473","title":"Incentive-Aware Dynamic Resource Allocation under Long-Term Cost Constraints","abstract":"Motivated by applications such as cloud platforms allocating GPUs to users or governments deploying mobile health units across competing regions, we study the dynamic allocation of a reusable resource to strategic agents with private valuations. Our objective is to simultaneously (i) maximize social welfare, (ii) satisfy multi-dimensional long-term cost constraints, and (iii) incentivize truthful reporting. We begin by numerically evaluating primal-dual methods widely used in constrained online optimization and find them to be highly fragile in strategic settings -- agents can easily manipulate their reports to distort future dual updates for future gain. To address this vulnerability, we develop an incentive-aware framework that makes primal-dual methods robust to strategic behavior. Our design combines epoch-based lazy updates -- where dual variables remain fixed within each epoch -- with randomized exploration rounds that extract approximately truthful signals for learning. Leveraging carefully designed online learning subroutines that can be of independent interest for dual updates, our mechanism achieves $\\tilde{\\mathcal{O}}(\\sqrt{T})$ social welfare regret, satisfies all cost constraints, and ensures incentive alignment. This matches the performance of non-strategic allocation approaches while being robust to strategic agents.","short_abstract":"Motivated by applications such as cloud platforms allocating GPUs to users or governments deploying mobile health units across competing regions, we study the dynamic allocation of a reusable resource to strategic agents with private valuations. Our objective is to simultaneously (i) maximize social welfare, (ii) satis...","url_abs":"https://arxiv.org/abs/2507.09473","url_pdf":"https://arxiv.org/pdf/2507.09473v1","authors":"[\"Yan Dai\",\"Negin Golrezaei\",\"Patrick Jaillet\"]","published":"2025-07-13T03:18:02Z","proceeding":"cs.GT","tasks":"[\"cs.GT\",\"cs.LG\",\"stat.ML\"]","methods":"[\"LoRA\"]","has_code":false}