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Maximin Fairness with Mixed Divisible and Indivisible Goods

Xiaohui Bei, Shengxin Liu, Xinhang Lu, Hongao Wang

2021Year
21Citations
3Top-tier citations

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 ratios of the instances. On the algorithmic front, we propose a constructive algorithm that will always produce an αminimal amsmath wasysym amsfonts amssymb amsbsy mathrsfs upgreek -69pt documentα\alphadocument-MMS allocation for any number of agents, where αminimal amsmath wasysym amsfonts amssymb amsbsy mathrsfs upgreek -69pt documentα\alphadocument takes values between 1/2 and 1 and is a monotonically increasing function determined by how agents value the divisible goods relative to their MMS values.

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