Fair Division with Two-Sided Preferences
Authors: Ayumi Igarashi, Yasushi Kawase, Warut Suksompong, Hanna Sumita
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that an allocation satisfying EF1, swap stability, and individual stability always exists and can be computed in polynomial time, even when teams may have positive or negative values for players. Similarly, a balanced and swap stable allocation that satisfies a relaxation of EF1 can be computed efficiently. When teams have nonnegative values for players, we prove that an EF1 and Pareto optimal allocation exists and, if the valuations are binary, can be found in polynomial time. We also examine the compatibility between EF1 and justified envy-freeness. |
| Researcher Affiliation | Academia | Ayumi Igarashi1 , Yasushi Kawase1 , Warut Suksompong2 and Hanna Sumita3 1University of Tokyo 2National University of Singapore 3Tokyo Institute of Technology |
| Pseudocode | Yes | Algorithm 1: For computing an EF[1,1], swap stable, and balanced allocation; Algorithm 2: For computing an EF1, swap stable, and individually stable allocation |
| Open Source Code | No | The paper is theoretical and does not mention or provide access to any open-source code for its described methodology. |
| Open Datasets | No | The paper is theoretical, focusing on the existence and computability of fair division allocations, and does not use datasets for training or evaluation in an empirical sense. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical data or dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper focuses on theoretical proofs and algorithm design, and thus does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical, presenting algorithms and proofs. It does not describe an empirical experimental setup with hyperparameters or training configurations. |