Resource Allocation to Strategic Agents under Cost Constraints

We study a dynamic game where a planner, under cost constraints, repeatedly allocates a resource to a group of agents who may strategically hide their true utilities. Via lazy dual updates, dual-adjusted payment rules, and uniform exploration rounds, we design an incentive-aware primal allocation framework. Further plugging in a novel Online Learning algorithm called O-FTRL-FP to learn dual variables, we show that one can achieve allocation efficiency, incentivecompatibility, and constraint-compliance at the same time.