Title: A constant factor approximation for Nash social welfare with subadditive valuations
| Speaker: | Mahtab Alghasi |
| Affiliation: | University of Waterloo |
| Location: | MC 6029 |
Abstract:Social welfare refers to a class of optimization problems where the goal is to allocate subsets of resources $\mathcal{I}$ among agents $\mathcal{A}$ (or people) such that maximizes the overall "happiness" of society in a fair and efficient manner. More specifically, each agent $i \in \mathcal{I}$ has an intrinsic \emph{valuation} function $v_i: 2^{\mathcal{I}}\rightarrow \mathbb{R}$, which is a monotone function with $v_i(\emptyset)=0$, and $v_i$ quantifies the intrinsic value for subsets of items $S\subseteq \mathcal{I}$.
Variations of allocation with different valuation and objective functions has been studied in different areas of computer science, economies, and game theory. In this talk we focus on the Nash social welfare welfare (NSW) problem; given an allocation $\mathcal{S}= (S_i)_{i\in \mathcal{A}}$ the goal is to maximize the geometric mean of agents valuations.
Unfortunately, Nash social welfare problem is NP-hard already in the case of two agents with identical additive valuations, and it is NP-hard to approximate within a factor better than 0.936 for additive valuations and $(1-\frac{1}{e})$ for submodular valuation.
Moreover, the current best approximation factors of $\simeq 0.992$ for additive valuations and $(\frac{1}{4}-\epsilon)$ for submodular valuations were found by Barman et al (2018) and Garg et al. (2023), respectively.
In this talk, we present a sketch of the algorithm in recent work by Dobzinski et al., which proves the first constant-factor approximation algorithm (with a fairly large constant $\sim \frac{1}{375,000}$) for NSW problem with subadditive valuations accessible via demand queries.