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: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:2607. 23310v1 Announce Type: cross Abstract: We study an online variant of discrete fair division under generalized assignment budget constraints.
By Saar Cohen, Nicholas Teh, Paul W. Goldberg, Michael J. Wooldridge
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: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:2608. 03171v1 Announce Type: cross Abstract: We study fair allocations of indivisible goods among agents with heterogeneous monotone valuations.
By Thanasis Lianeas, Alkmini Sgouritsa, Minas Marios Sotiriou
arXiv:2306.00636v3 Announce Type: replace-cross
Abstract: Many fairness criteria constrain the policy or choice of predictors, which can have unwanted consequences, in particular, when optimizing the...
By Frederik Hytting J{\o}rgensen, Sebastian Weichwald, Jonas Peters
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
arXiv:2607. 26485v1 Announce Type: cross Abstract: Resource allocation across multiple agent groups arises in many applications including e-commerce recommendation systems, housing assignment, and course allocation, and is commonly formulated as an optimization problem with diversity constraints to ensure group fairness.
By Keke Huang, Yik Yu Ng, Laks V. S. Lakshmanan, Xiaokui Xiao
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
The paper introduces the AR Fairness Metamodel, a structured framework for representing, analyzing, and comparing fairness scenarios. It incorporates key elements such as agents, resources, and their attributes, and supports both discrete and continuous fairness measures—including equality, equity, group fairness, individual fairness, the Gini index, the Theil index, Jain's fairness index, and a specific measure for Australia's Child Care Subsidy. The metamodel builds on the Tiles framework, offering modular components that can be connected to capture diverse fairness definitions, and includes formal proofs of relationships among group fairness, individual fairness, and envy‑freeness. An open‑source implementation of the Tiles framework is provided to facilitate practical fairness modeling and evaluation across various applications.
By Julian Alfredo Mendez, Timotheus Kampik
arXiv:2601. 10600v2 Announce Type: replace-cross Abstract: In the context of multi-agent multi-armed bandits (MA-MAB), fairness is often reduced to outcomes: maximizing welfare, reducing inequality, or balancing utilities.
By Joshua Caiata, Carter Blair, Kate Larson