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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fair and Efficient Allocation of Indivisible Chores with Surplus
Authors: Hannaneh Akrami, Bhaskar Ray Chaudhury, Jugal Garg, Kurt Mehlhorn, Ruta Mehta
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a polynomial-time algorithm which gives EF1 and PO allocations with (n 1) surplus. We relax the notion of EFX slightly and define t EFX which requires that the envy from agent i to agent j is removed upon the transfer of any chore from the i s bundle to j s bundle. We give a polynomial-time algorithm that in the chores case for 3 agents returns an allocation which is either proportional or t EFX. |
| Researcher Affiliation | Academia | 1Max Planck Institute for Informatics, Germany 2Graduiertenschule Informatik, Universit at des Saarlandes, Germany 3University of Illinois at Urbana-Champaign, USA |
| Pseudocode | Yes | Algorithm 1 fair And Efficient(I); Algorithm 2 EFX-Identical |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with datasets, so no information about public datasets or their access is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments with specific setup details like hyperparameters or training configurations. |