Group Fairness for the Allocation of Indivisible Goods
Authors: Vincent Conitzer, Rupert Freeman, Nisarg Shah, Jennifer Wortman Vaughan1853-1860
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments on real and synthetic data, we show that the local search algorithm converges quickly, and is likely to output an efficient allocation. |
| Researcher Affiliation | Collaboration | Vincent Conitzer Duke University conitzer@cs.duke.edu; Rupert Freeman Microsoft Research rupert.freeman@microsoft.com; Nisarg Shah University of Toronto nisarg@cs.toronto.edu; Jennifer Wortman Vaughan Microsoft Research jenn@microsoft.com |
| Pseudocode | No | The paper describes the local search algorithm in paragraph text but does not provide it in a structured pseudocode block or algorithm environment. |
| Open Source Code | No | The paper does not provide any links to open-source code for the described methodology or make a clear statement about its availability. |
| Open Datasets | No | The paper mentions using a dataset from Spliddit.org and generating synthetic data, but it does not provide concrete access information (like a specific link or formal citation with author/year) for these datasets to be considered publicly available or open for reproduction. |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits. It describes how instances are generated for simulations but not how they are partitioned for model training/evaluation in a typical machine learning context. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | We vary the number of players (n) from 3 to 10. For each n in this range, we vary the number of goods (m) from n to 5n in increments of n. To explore the effect of the magnitude of player valuations on the running time, we additionally vary a parameter K controlling this magnitude from 100 to 1000 in increments of 100. For each combination of n, m, and K, we generate 1000 instances in which the valuation vi of each player i is sampled i.i.d. from the uniform distribution over all integral valuations that sum to K (i.e., uniformly at random subject to vi(M) = K). |