arXiv:2512.24105cs.GTcs.AI2025-12被引 1

解决树形结构中多层级资源分配的公平与效率问题。

Multilevel Fair Allocation with Matroid-Rank Preferences

  • 采用自顶向下迭代方法,结合局部分配机制。
  • 保证理论上的效率与公平性,支持多种本地算法。
  • 扩展广义野马交换法,实测公平性优异。

本文研究具有树状层级结构的多层级资源公平分配问题。每个中间节点可独立执行局部分配,最终结果由从根到叶的迭代过程决定。假设叶子节点具有拟阵秩效用函数,内部节点效用为子节点效用之和。提出两种新算法:其一为通用多项式时间的顺序算法,具备效率与公平性的理论保证,支持多种局部策略;其二将近期提出的广义野马交换(General Yankee Swap)扩展至多层级场景,虽仅保证效率,但实证显示其公平性表现极佳。

原文摘要 · Abstract (English)

We introduce the concept of multilevel fair allocation of resources with tree-structured hierarchical relations among agents. While at each level it is possible to consider the problem locally as an allocation of an agent to its children, the multilevel allocation can be seen as a trace capturing the fact that the process is iterated until the leaves of the tree. In principle, each intermediary node may have its own local allocation mechanism. The main challenge is then to design algorithms which can retain good fairness and efficiency properties. In this paper we propose two original algorithms under the assumption that leaves of the tree have matroid-rank utility functions and the utility of any internal node is the sum of the utilities of its children. The first one is a generic polynomial-time sequential algorithm that comes with theoretical guarantees in terms of efficiency and fairness. It operates in a top-down fashion -- as commonly observed in real-world applications -- and is compatible with various local algorithms. The second one extends the recently proposed General Yankee Swap to the multilevel setting. This extension comes with efficiency guarantees only, but we show that it preserves excellent fairness properties in practice.

资源分配公平性树结构

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