Approximating Nash Social Welfare under Submodular Valuations through (Un)Matchings

• Published in: ACM transactions on algorithms 2023-09, Vol.19 (4), p.1-25, Article 36
• Main Authors: Garg, Jugal, Kulkarni, Pooja, Kulkarni, Rucha
• Format: Article
• Language: English
• Subjects: Algorithmic game theory and mechanism design; Approximation algorithms analysis; Theory of computation
• ISSN: 1549-6325; 1549-6333
• DOI: 10.1145/3613452

We study the problem of approximating maximum Nash social welfare (NSW) when allocating m indivisible items among n asymmetric agents with submodular valuations. The NSW is a well-established notion of fairness and efficiency, defined as the weighted geometric mean of agents’ valuations. For special cases of the problem with symmetric agents and additive(-like) valuation functions, approximation algorithms have been designed using approaches customized for these specific settings, and they fail to extend to more general settings. Hence, no approximation algorithm with a factor independent of m was known either for asymmetric agents with additive valuations or for symmetric agents beyond additive(-like) valuations before this work. In this article, we extend our understanding of the NSW problem to far more general settings. Our main contribution is two approximation algorithms for asymmetric agents with additive and submodular valuations. Both algorithms are simple to understand and involve non-trivial modifications of a greedy repeated matchings approach. Allocations of high-valued items are done separately by un-matching certain items and re-matching them by different processes in both algorithms. We show that these approaches achieve approximation factors of O(n) and O(n log n) for additive and submodular cases, independent of the number of items. For additive valuations, our algorithm outputs an allocation that also achieves the fairness property of envy-free up to one item (EF1). Furthermore, we show that the NSW problem under submodular valuations is strictly harder than all currently known settings with an \(\frac{\mathrm{e}}{\mathrm{e}-1}\) factor of the hardness of approximation, even for constantly many agents. For this case, we provide a different approximation algorithm that achieves a factor of \(\frac{\mathrm{e}}{\mathrm{e}-1}\) , hence resolving it completely.