Improving Nash Social Welfare Approximations
Authors: Peter McGlaughlin, Jugal Garg
JAIR 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We address this issue by presenting a 2 max NSW, Prop-1, 1/(2n) MMS, and Pareto optimal allocation in strongly polynomial time. Our techniques are based on a market interpretation of a fractional max NSW allocation. We present novel deļ¬nitions of fairness concepts in terms of market prices, and design a new scheme to round a market equilibrium into an integral allocation in a way that provides most of the fairness properties of an integral max NSW allocation. [...] Algorithm 1: Rounding Algorithm |
| Researcher Affiliation | Academia | Jugal Garg EMAIL Peter Mc Glaughlin EMAIL University of Illinois Urbana-Champaign, Urbana, IL 61801 USA |
| Pseudocode | Yes | Algorithm 1: Rounding Algorithm |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that source code for the described methodology is publicly available. Phrases like "We release our code...", "The source code for our method is available at...", or links to code repositories are absent. |
| Open Datasets | No | The paper describes a theoretical problem of fair allocation of goods with additive valuations and does not involve experiments on specific datasets. The examples provided (e.g., in Section 5) are constructed scenarios for illustrative purposes, not actual datasets used for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on datasets, thus there are no dataset splits to describe. |
| Hardware Specification | No | The paper is theoretical and does not report on experimental results, therefore, no specific hardware specifications are mentioned for running experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and proofs. It does not mention any specific software dependencies with version numbers required to implement or run the described algorithms. |
| Experiment Setup | No | The paper is theoretical and presents an algorithm with proofs of its properties. It does not include any experimental results, thus there are no experimental setup details, hyperparameters, or training configurations described. |