Nash Social Welfare Approximation for Strategic Agents
Not on the map. Why?
Abstract (arXiv)
The fair division of resources is an important age-old problem that has led to a rich body of literature. At the center of this literature lies the question of whether there exist fair mechanisms despite strategic behavior of the agents. A fundamental objective function used for measuring fair outcomes is the Nash social welfare, defined as the geometric mean of the agent utilities. This objective function is maximized by widely known solution concepts such as Nash bargaining and the competitive equilibrium with equal incomes. In this work we focus on the question of (approximately) implementing the Nash social welfare. The starting point of our analysis is the Fisher market, a fundamental model of an economy, whose benchmark is precisely the (weighted) Nash social welfare. We begin by studying two extreme classes of valuations functions, namely perfect substitutes and perfect complements, and find that for perfect substitutes, the Fisher market mechanism has a constant approximation: at most 2 and at least e1e. However, for perfect complements, the Fisher market does not work well, its bound degrading linearly with the number of players. Strikingly, the Trading Post mechanism---an indirect market mechanism also known as the Shapley-Shubik game---has significantly better performance than the Fisher market on its own benchmark. Not only does Trading Post achieve an approximation of 2 for perfect substitutes, but this bound holds for all concave utilities and becomes arbitrarily close to optimal for Leontief utilities (perfect complements), where it reaches (1+ε) for every ε > 0. Moreover, all the Nash equilibria of the Trading Post mechanism are pure for all concave utilities and satisfy an important notion of fairness known as proportionality.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Jul 2016.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 2; 2025: 2; 2026: 2.
9 papers.
- 2021Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Gamescited by 4
- 2021Allocating Indivisible Goods to Strategic Agents: Pure Nash Equilibria and Fairnesscited by 3
- 2022Stochastic Online Fisher Markets: Static Pricing Limits and Adaptive Enhancementscited by 2
- 2025Fairness Aware Reinforcement Learning via Proximal Policy Optimizationcited by 2
- 2024Online Fair Allocation of Perishable Resourcescited by 1
- 2024Intuitions of Compromise: Utilitarianism vs. Contractualismcited by 1
- 2025Regularized Proportional Fairness Mechanism for Resource Allocation Without Moneycited by 0
- 2026Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfarecited by 0
- 2026Fair Stable Matching: A Nash Social Welfare Approachcited by 0
- 2026Fair Stable Matching: A Nash Social Welfare Approach2 Sep 2026
- 2026Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare3 May 2026
- 2025Fairness Aware Reinforcement Learning via Proximal Policy Optimization6 Feb 2025
- 2025Regularized Proportional Fairness Mechanism for Resource Allocation Without Money2 Jan 2025
- 2024Intuitions of Compromise: Utilitarianism vs. Contractualism7 Oct 2024
- 2024Online Fair Allocation of Perishable Resources4 Jun 2024
- 2022Stochastic Online Fisher Markets: Static Pricing Limits and Adaptive Enhancements27 Apr 2022
- 2021Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games15 Dec 2021
- 2021Allocating Indivisible Goods to Strategic Agents: Pure Nash Equilibria and Fairness17 Sep 2021