Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].

The Price of Fairness for Indivisible Goods

Authors: Xiaohui Bei, Xinhang Lu, Pasin Manurangsi, Warut Suksompong

IJCAI 2019 | Venue PDF | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We mostly provide tight or asymptotically tight bounds on the worst-case ef๏ฌciency 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.