Temporal Fair Division
Authors: Benjamin Cookson, Soroush Ebadian, Nisarg Shah
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In the most general setting, we prove that there always exists an allocation that is stochastically-dominant envy-free up to one good (SD-EF1) per day and proportional up to one good (PROP1) overall, and when all the agents have identical preferences, we show that SD-EF1 per day and SD-EF1 overall can be guaranteed. For the case of two agents, we prove that SD-EF1 per day and EF1 up to each day can be guaranteed using an envy balancing technique. We provide counterexamples for other combinations that establish our results as among the best guarantees possible, but also leave open some tantalizing questions. |
| Researcher Affiliation | Academia | University of Toronto EMAIL |
| Pseudocode | Yes | Algorithm 1: SD-EF1 per day + PROP1 Overall |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating the release of source code for the methodology described. |
| Open Datasets | No | The paper discusses theoretical models of fair division and does not mention the use of any specific datasets for empirical evaluation. |
| Dataset Splits | No | The paper does not use any datasets for experiments, therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper focuses on theoretical proofs and algorithms, without reporting on experimental results that would require hardware specifications. |
| Software Dependencies | No | The paper focuses on theoretical algorithms and proofs, and does not specify any software dependencies or versions. |
| Experiment Setup | No | The paper focuses on theoretical algorithms, proofs, and impossibility results, and therefore does not describe any experimental setup or hyperparameters. |