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新论文 · v1arXiv:2611.05073

Toward Constant-Factor Maximin Share Guarantees for Subadditive Valuations

Bo Li, Ankang Sun

原文摘要

For $n$ agents and $m$ indivisible goods, an agent's maximin share (MMS) is the largest minimum bundle value she can obtain by partitioning the goods into $n$ bundles. Constant MMS approximations are known for additive, submodular, or XOS valuations, but whether such a guarantee exists for general subadditive valuations remains open. We advance this question in two directions. For general subadditive valuations, we exponentially improve the guarantee from $Ω(1/\log\log n)$ to $Ω(1/\log^* n)$, where $\log^* n$ is the iterated logarithm. We then seek constant MMS guarantees under additional structure on subadditive valuations by studying $q$-partitioning valuations: every partition of a bundle into at most $q$ groups admits nonnegative group prices that sum to the bundle's value and whose sum on any collection of groups is at most the value of their union. This hierarchy ranges from subadditivity at $q=2$ to XOS for $q\ge m$. We give the first study of MMS in the intermediate classes and prove constant guarantees whenever $q/n$ has a fixed positive lower bound, including $1/6$ for $q\ge n$ and $1/4$ for $q\ge2n$. For XOS valuations, we further improve the universal guarantee from $4/17\approx 0.2353$ to $5/16=0.3125$. For subadditive valuations, we prove a bidding amplification theorem that gives an exponential bound on each agent's probability of receiving less than half her MMS, while satisfying her budget in every outcome. For $q$-partitioning valuations, we give a scalar criterion that converts bounds on the value lost from disjoint deletions within a bundle into bounds on potential losses, even when an agent contributes zero below the target bundle value. For XOS valuations, we prove a reservation theorem that selects goods for integral assignment together with auxiliary additive valuations and accounts for the loss in rounding.

cs.GT

版本更新 · v2arXiv:2610.10336

Settling PROPm and PROPavg in Graphical Resource Allocation

Bo Li, Ankang Sun, Ruijie Wang

原文摘要

We study proportional fairness in graphical resource allocation, where agents are vertices, indivisible items are edges, and each item must be allocated to one of its two endpoints. It has been proved that PROP1 orientations always exist and PROPx orientations may not, but it has remained open whether the intermediate relaxations PROPm and PROPavg (both can be satisfied without graphical constraints) can always be satisfied. In this paper, we resolve this gap. We prove that PROPm orientations always exist for goods on multigraphs and can be computed efficiently. The guarantee extends to chores and a mixture of goods and chores. In sharp contrast, we show that PROPavg orientations need not exist, even for simple graphs with binary valuations, and that deciding their existence is NP-complete. We further quantify the efficiency loss of PROPm: for goods, the price of PROPm is exactly $2$, which is one of the few settings where a constant bound on the price of fairness can be obtained. We complement these results with hardness results for welfare optimization under PROPm and for the existence of orientations that are simultaneously PROPm and Pareto optimal.

cs.GT