Incentive-Aware Dynamic Resource Allocation under Long-Term Cost Constraints

2025-07-12

AI Summary

This paper studies the dynamic allocation of reusable resources to strategic agents with private valuations under multi-dimensional long-term budget constraints, aiming to simultaneously maximize social welfare, strictly satisfy all cost constraints, and incentivize truthful reporting. We propose the first incentive-compatible primal-dual online mechanism for this setting, integrating epoch-based delayed dual updates, lazy updates, randomized exploration, and an online learning subroutine to restore the robustness of primal-dual methods in strategic environments. Theoretically, our mechanism achieves a social welfare regret of $ ilde{mathcal{O}}(sqrt{T})$, satisfies all long-term budget constraints strictly at every time step, and is Bayesian incentive compatible (BIC). Its performance asymptotically approaches that of the optimal offline benchmark in the non-strategic setting—marking the first result that attains near-optimal long-term resource allocation while provably guaranteeing incentive compatibility.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Machine Learning: Online Learning & Bandits

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 $ ilde{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.

Problem

• Dynamic allocation of reusable resources to strategic agents
• Maximize welfare while satisfying long-term cost constraints
• Ensure truthful reporting and prevent manipulation in allocation

Innovation

• Incentive-aware primal-dual framework for robustness
• Epoch-based lazy updates with randomized exploration
• Online learning subroutines for dual updates