Alviona Mancho | Searcharxiv

arXiv2026

Delegated Fair Division

Motivated by recently introduced problems on delegated resource allocation, we study a model of fair division, where a set of indivisible goods is to be allocated to some agents, each of which belonging to some bigger central entity. Our model captures the general framework of allocating resources to organizational units, which subsequently distribute them to their affiliated members. A particularly relevant application of this framework, with immense social impact, arises in the allocation of food donations through charitable organizations. In essence, every center acts as the representative of the agents belonging to it, aligning their generally different preferences. Our goal is to distribute the goods in a way that is simultaneously fair both with respect to the centers and the agents. We distinguish two different information structures depending on whether the agents compare their bundles against every agent or only those belonging to the same center. For each one of them we provide efficient algorithms that produce allocations that satisfy envy-based fairness guarantees at both levels.

cs.GT↗

arXiv2025

Fairness under Equal-Sized Bundles: Impossibility Results and Approximation Guarantees

We study the fair allocation of indivisible goods under cardinality constraints, where each agent must receive a bundle of fixed size. This models practical scenarios, such as assigning shifts or forming equally sized teams. Recently, variants of envy-freeness up to one/any item (EF1, EFX) were introduced for this setting, based on flips or exchanges of items. Namely, one can define envy-freeness up to one/any flip (EFF1, EFFX), meaning that an agent $i$ does not envy another agent $j$ after performing one or any one-item flip between their bundles that improves the value of $i$. We explore algorithmic aspects of this notion, and our contribution is twofold: we present both algorithmic and impossibility results, highlighting a stark contrast between the classic EFX concept and its flip-based analogue. First, we explore standard techniques used in the literature and show that they fail to guarantee EFFX approximations. On the positive side, we show that we can achieve a constant factor approximation guarantee when agents share a common ranking over item values, based on the well-known envy cycle elimination technique. This idea also leads to a generalized algorithm with approximation guarantees when agents agree on the top $n$ items and their valuation functions are bounded. Finally, we show that an algorithm that maximizes the Nash welfare guarantees a 1/2-EFF1 allocation, and that this bound is tight.

cs.GT↗