Fair Division of Time: Multi-layered Cake Cutting
Authors: Hadi Hosseini, Ayumi Igarashi, Andrew Searns
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate the existence and computation of envy-free and proportional allocations. We show that envyfree allocations that are both feasible and contiguous are guaranteed to exist for up to three agents with two types of preferences, when the number of layers is two. We further devise an algorithm for computing proportional allocations for any number of agents when the number of layers is factorable to three and/or some power of two. |
| Researcher Affiliation | Academia | 1Rochester Institute of Technology, USA 2National Institute of Informatics, Japan hhvcs@rit.edu, ayumi igarashi@nii.ac.jp, abs2157@rit.edu |
| Pseudocode | No | The paper describes protocols (e.g., 'Cut-and-choose protocol', 'Moving-knife protocol', 'A protocol for proportionality') with numbered steps, but these are presented as prose descriptions rather than structured pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper presenting mathematical proofs and algorithms. It does not use or refer to any datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical validation with dataset splits. |
| Hardware Specification | No | This is a theoretical paper presenting mathematical proofs and algorithms. It does not describe any experimental setup or hardware used for computations. |
| Software Dependencies | No | This is a theoretical paper and does not mention any specific software or libraries with version numbers used for implementation or experimentation. |
| Experiment Setup | No | This is a theoretical paper presenting mathematical proofs and algorithms. It does not describe any experimental setup, hyperparameters, or training configurations. |