Online Resource Sharing via Dynamic Max-Min Fairness: Efficiency, Robustness and Non-Stationarity

We study the allocation of shared resources over multiple rounds among competing agents, via a dynamic max-min fair (DMMF) mechanism: the good in each round is allocated to the requesting agent with the least number of allocations received to date. Previous work has shown that when an agent has i.i.d. values across rounds, then in the worst case, she can never get more than a constant strictly less than $1$ fraction of her ideal utility -- her highest achievable utility given her nominal share of resources. Moreover, an agent can achieve at least half her utility under carefully designed `pseudo-market' mechanisms, even though other agents may act in an arbitrary (possibly adversarial and collusive) manner.

@article{fikioris2025_2310.08881,
  title={ Beyond Worst-Case Online Allocation via Dynamic Max-min Fairness },
  author={ Giannis Fikioris and Siddhartha Banerjee and Éva Tardos },
  journal={arXiv preprint arXiv:2310.08881},
  year={ 2025 }
}