Randomized and Deterministic Maximin-Share Approximations for Fractionally Subadditive Valuations

Abstract

We consider the problem of guaranteeing maximin-share ($\MMS$) when allocating a set of indivisible items to a set of agents with fractionally subadditive ($\XOS$) valuations. For $\XOS$ valuations, it has been previously shown that for some instances no allocation can guarantee a fraction better than $1/2$ of maximin-share to all the agents. Also, a deterministic allocation exists that guarantees $0.219225$ of the maximin-share of each agent. Our results involve both deterministic and randomized allocations. On the deterministic side, we improve the best approximation guarantee for fractionally subadditive valuations to $3/13 = 0.230769$. We develop new ideas on allocating large items in our allocation algorithm which might be of independent interest. Furthermore, we investigate randomized algorithms and the Best-of-both-worlds fairness guarantees. We propose a randomized allocation that is $1/4$-$\MMS$ ex-ante and $1/8$-$\MMS$ ex-post for $\XOS$ valuations. Moreover, we prove an upper bound of $3/4$ on the ex-ante guarantee for this class of valuations.

Cite

Text

Akrami et al. "Randomized and Deterministic Maximin-Share Approximations for Fractionally Subadditive Valuations." Neural Information Processing Systems, 2023.

Markdown

[Akrami et al. "Randomized and Deterministic Maximin-Share Approximations for Fractionally Subadditive Valuations." Neural Information Processing Systems, 2023.](https://mlanthology.org/neurips/2023/akrami2023neurips-randomized/)

BibTeX

@inproceedings{akrami2023neurips-randomized,
  title     = {{Randomized and Deterministic Maximin-Share Approximations for Fractionally Subadditive Valuations}},
  author    = {Akrami, Hannaneh and Mehlhorn, Kurt and Seddighin, Masoud and Shahkarami, Golnoosh},
  booktitle = {Neural Information Processing Systems},
  year      = {2023},
  url       = {https://mlanthology.org/neurips/2023/akrami2023neurips-randomized/}
}