English

Maximin Fairness with Mixed Divisible and Indivisible Goods

Computer Science and Game Theory 2021-07-02 v3

Abstract

We study fair resource allocation when the resources contain a mixture of divisible and indivisible goods, focusing on the well-studied fairness notion of maximin share fairness (MMS). With only indivisible goods, a full MMS allocation may not exist, but a constant multiplicative approximate allocation always does. We analyze how the MMS approximation guarantee would be affected when the resources to be allocated also contain divisible goods. In particular, we show that the worst-case MMS approximation guarantee with mixed goods is no worse than that with only indivisible goods. However, there exist problem instances to which adding some divisible resources would strictly decrease the MMS approximation ratio of the instance. On the algorithmic front, we propose a constructive algorithm that will always produce an α\alpha-MMS allocation for any number of agents, where α\alpha takes values between 1/21/2 and 11 and is a monotone increasing function determined by how agents value the divisible goods relative to their MMS values.

Keywords

Cite

@article{arxiv.2002.05245,
  title  = {Maximin Fairness with Mixed Divisible and Indivisible Goods},
  author = {Xiaohui Bei and Shengxin Liu and Xinhang Lu and Hongao Wang},
  journal= {arXiv preprint arXiv:2002.05245},
  year   = {2021}
}

Comments

Appears in the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021

R2 v1 2026-06-23T13:40:10.613Z