The paper introduces a new online fair division framework where a learner must allocate indivisible items to agents in real time, balancing fairness and efficiency. Traditional methods rely on many copies of each item to estimate utilities, but this is unrealistic for platforms with many users and few interactions. By treating utility as an unknown function of item-agent features and framing the problem as a contextual bandit, the authors propose algorithms that achieve sublinear regret and demonstrate their effectiveness experimentally.
By Arun Verma, Indrajit Saha, Makoto Yokoo, Bryan Kian Hsiang Low
The paper investigates the compatibility of envy-freeness and equitability in fair division, focusing on both indivisible goods and chores. It shows that the relaxed notions EF1+EQ1 may not exist even for normalized additive valuations, but provides an algorithm that finds an EF1+EQ1 allocation for up to seven agents with binary goods. For chores, the authors prove that a stronger EFX+EQX guarantee always exists, regardless of normalization, and they also explore cross-notion ex‑ante and ex‑post fairness guarantees.
By Hadi Hosseini, Shraddha Pathak, Lirong Xia, Chengkai Zhang
arXiv:2601. 17944v2 Announce Type: replace-cross Abstract: We study repeated allocation of shared resources among agents with time-varying demands and capped linear utilities.
By Seyed Majid Zahedi, Rupert Freeman
arXiv:2608.24400v1 Announce Type: cross
Abstract: We study multilevel fair resource allocation with tree-structured hierarchical relations among agents. At each level, the problem can be viewed local...
By Maxime Lucet, Nawal Benabbou, Aur\'elie Beynier, Nicolas Maudet
arXiv:2607. 23367v1 Announce Type: cross Abstract: We study whether strictly positive marginal values restore the compatibility of envy-freeness up to one good (EF1) and Pareto optimality (PO) for indivisible goods.
By Nicholas Teh
arXiv:2609. 03846v1 Announce Type: cross Abstract: We study the allocation of indivisible goods among agents with identical additive valuations, focusing on envy-freeness up to one good (EF1) and Nash social welfare (NSW).
By Zih-Sian Yang, Yi-Hao Chen, Yu-Te Kuan, Cheng-Jui Wu, Chuang-Chieh Lin, Po-An Chen
arXiv:2606. 10472v1 Announce Type: cross Abstract: Dynamic multi-resource allocation is a central problem in shared computing environments, where users' demands arrive sequentially and resources must be distributed fairly without knowledge of future demands.
By Kaiqi Jiang, Karim El Husseini, Wenzhe Fan, Xinhua Zhang
arXiv:2605. 01961v2 Announce Type: replace Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents.
By Maheed H. Ahmed, Mahsa Ghasemi
arXiv:2406. 12413v3 Announce Type: replace-cross Abstract: We study the problem of allocating a set of indivisible goods to a set of agents with additive valuation functions, aiming to achieve approximate envy-freeness up to any good ($\alpha$-EFX).
By Georgios Amanatidis, Aris Filos-Ratsikas, Alkmini Sgouritsa
arXiv:2507. 09473v2 Announce Type: replace-cross Abstract: We study the dynamic allocation of indivisible resources to strategic agents under long-term constraints, where the planner aims to maximize social welfare, satisfy multiple constraints, and elicit near-truthful reports.
By Yan Dai, Negin Golrezaei, Patrick Jaillet
arXiv:2603. 14372v2 Announce Type: replace Abstract: The rise of AI amplifies the economic phenomenon of \emph{positive spillovers}: when creators contribute content that can be reused and adapted by LLMs, one creator's effort may improve the content quality of others through recombination.
By Sagi Ohayon, Boaz Taitler, Omer Ben-Porat
The paper introduces a learning‑augmented algorithm for online allocation that handles unreliable predictions. It addresses finite candidate sets, irreversible decisions, and exposure constraints by combining advice with a conservative fallback and a fairness correction. The authors prove consistency and robustness under bounded‑error assumptions and demonstrate experimentally that the method remains stable against adversarial advice while substantially reducing exposure disparity.
By Fredy Pokou (MRE, CRIStAL)