English

Fair and Almost Truthful Mechanisms for Additive Valuations and Beyond

Computer Science and Game Theory 2024-07-24 v2

Abstract

We study the problem of fairly allocating indivisible goods among nn strategic agents. It is well-known that truthfulness is incompatible with any meaningful fairness notions. We bypass the strong negative result by considering the concept of incentive ratio, a relaxation of truthfulness quantifying agents' incentive to misreport. Previous studies show that Round-Robin, which satisfies envy-freeness up to one good (EF1), achieves an incentive ratio of 22 for additive valuations. In this paper, we explore the incentive ratio achievable by fair mechanisms for various classes of valuations besides additive ones. We first show that, for arbitrary ϵ>0\epsilon > 0, every (12+ϵ)(\frac{1}{2} + \epsilon)-EF1 mechanism for additive valuations admits an incentive ratio of at least 1.51.5. Then, using the above lower bound for additive valuations in a black-box manner, we show that for arbitrary ϵ>0\epsilon > 0, every ϵ\epsilon-EF1 mechanism for cancelable valuations admits an infinite incentive ratio. Moreover, for subadditive cancelable valuations, we show that Round-Robin, which satisfies EF1, achieves an incentive ratio of 22, and every (φ1)(\varphi - 1)-EF1 mechanism admits an incentive ratio of at least φ\varphi with φ=(1+5)/21.618\varphi = (1 + \sqrt{5}) / 2 \approx 1.618. Finally, for submodular valuations, we show that Round-Robin, which satisfies 12\frac{1}{2}-EF1, admits an incentive ratio of nn.

Keywords

Cite

@article{arxiv.2306.15920,
  title  = {Fair and Almost Truthful Mechanisms for Additive Valuations and Beyond},
  author = {Biaoshuai Tao and Mingwei Yang},
  journal= {arXiv preprint arXiv:2306.15920},
  year   = {2024}
}