Improving Nash Social Welfare Approximations

Abstract

We consider the problem of fairly allocating a set of indivisible goods among n agents. Various fairness notions have been proposed within the rapidly growing field of fair division, but the Nash social welfare (NSW) serves as a focal point. In part, this follows from the ‘unreasonable’ fairness guarantees provided, in the sense that a max NSW allocation meets multiple other fairness metrics simultaneously, all while satisfying a standard economic concept of efficiency, Pareto optimality. However, existing approximation algorithms fail to satisfy all of the remarkable fairness guarantees offered by a max NSW allocation, instead targeting only the specific NSW objective. We address this issue by presenting a 2 max NSW, Prop-1, 1/(2n) MMS, and Pareto optimal allocation in strongly polynomial time. Our techniques are based on a market interpretation of a fractional max NSW allocation. We present novel definitions of fairness concepts in terms of market prices, and design a new scheme to round a market equilibrium into an integral allocation in a way that provides most of the fairness properties of an integral max NSW allocation.

Cite

Text

McGlaughlin and Garg. "Improving Nash Social Welfare Approximations." Journal of Artificial Intelligence Research, 2020. doi:10.1613/JAIR.1.11618

Markdown

[McGlaughlin and Garg. "Improving Nash Social Welfare Approximations." Journal of Artificial Intelligence Research, 2020.](https://mlanthology.org/jair/2020/mcglaughlin2020jair-improving/) doi:10.1613/JAIR.1.11618

BibTeX

@article{mcglaughlin2020jair-improving,
  title     = {{Improving Nash Social Welfare Approximations}},
  author    = {McGlaughlin, Peter and Garg, Jugal},
  journal   = {Journal of Artificial Intelligence Research},
  year      = {2020},
  pages     = {225-245},
  doi       = {10.1613/JAIR.1.11618},
  volume    = {68},
  url       = {https://mlanthology.org/jair/2020/mcglaughlin2020jair-improving/}
}