Stochastic Constraints and Strategic Selection: Exploring Tight Guarantees in Multi-Unit Online Allocation
Keywords:
Online allocation, stochastic constraints, strategic agents, multi-unit auctions, competitive ratio, mechanism design, truthful mechanisms, fairness, real-time resource allocation, adversarial inputs.Abstract
This research explores the challenges of multi-unit online allocation in environments governed by both stochastic constraints and strategic agent behavior. In such settings, resources arrive sequentially and must be allocated in real time to agents who may act selfishly to maximize their individual gains. Traditional online allocation mechanisms often struggle to maintain efficiency and fairness when faced with uncertainty in item arrivals and the potential for misreported preferences. We propose new algorithmic frameworks that incorporate stochastic information into allocation decisions while preserving strategic robustness. Our mechanisms, designed to be truthful and fair, achieve tight competitive guarantees even in hybrid stochastic-adversarial models. We establish theoretical bounds that highlight the fundamental trade-offs between performance, truthfulness, and adaptivity, and support our analysis with simulation results demonstrating near-optimal behavior under realistic constraints. This work contributes to the broader understanding of how to design resilient and efficient online allocation systems in complex, uncertain, and strategic environments.
