Fair and Efficient Allocation of Indivisible Chores with Surplus
Authors: Hannaneh Akrami, Bhaskar Ray Chaudhury, Jugal Garg, Kurt Mehlhorn, Ruta Mehta
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | 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. |