Abstract
We study the problem of fairly allocating a set of indivisible goods to a set of n agents with additive valuation functions. We focus on the very demanding notion of \textit{groupwise maximin share fairness} (GMMS), which requires that each agent i receives value comparable to their maximin share, where the latter is computed \textit{with respect to any subset of agents that contains i}. We show that it is possible to compute (φ-1)-approximate GMMS allocations in polynomial time, where φ ≈ 1.618 is the golden ratio). This improves on the previously known guarantee of 4/7 of Chaudhury et al. [SICOMP; 2021] and Amanatidis et al. [TCS; 2020]. We propose a simple algorithm that maintains the same main properties as the Draft-and-Eliminate algorithm of Amanatidis et al. [TCS, 2020] and we improve on the approximation guarantee analysis by carefully bounding the relevant value within any subinstance induced by the restriction of our allocation to a subset of agents. Our analysis is asymptotically tight for algorithms that share these properties and has the additional benefit of giving improved guarantees for restricted settings; in particular, when the agents agree on the top n goods or when the number of agents is small. To illustrate the challenges of going beyond the guarantees of our algorithm, we also present a variant with an improved approximation of (√10-1)/3 ≈ 0.72 for the case of three agents. To achieve this improvement we partially characterize the maximin share guarantees of short picking sequences for a small number of goods.