The Price of Fairness for Indivisible Goods
Authors: Xiaohui Bei, Xinhang Lu, Pasin Manurangsi, Warut Suksompong
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We mostly provide tight or asymptotically tight bounds on the worst-case efficiency loss for allocations satisfying these notions. (from Abstract) and Table 1: Summary of our results. LB denotes lower bound and UB denotes upper bound. |
| Researcher Affiliation | Academia | 1 School of Physical and Mathematical Sciences, Nanyang Technological University 2Department of Electrical Engineering and Computer Sciences, UC Berkeley 3Department of Computer Science, University of Oxford |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use datasets or conduct experiments, thus no information on public datasets for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not use datasets or conduct experiments, thus no information on dataset splits for validation is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments, thus no specific hardware details are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments, thus no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe experiments or their setup, thus no specific experimental setup details are provided. |