Two-Sided Matching Meets Fair Division
Authors: Rupert Freeman, Evi Micha, Nisarg Shah
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our simulations, we observe that there is a sharp contrast for envy-freeness (one-sided EF is almost always achievable while two-sided DEF almost always isn t). For the maximin share guarantee, however, there is no contrast: both one-sided MMS and two-sided DMMS are almost always achievable. |
| Researcher Affiliation | Academia | 1University of Virginia 2University of Toronto |
| Pseudocode | Yes | Algorithm 1 Round-Robin-Ordering(n, a, x); Algorithm 2 Restricted-Round-Robin-Coprime(n, d) |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that open-source code for the described methodology is available. |
| Open Datasets | No | The paper primarily presents theoretical results and uses constructed instances (e.g., in Theorem 3 proof) rather than external, publicly available datasets. No access information for any dataset is provided. |
| Dataset Splits | No | The paper does not describe dataset splits (training, validation, test) as it focuses on theoretical analysis and proofs rather than empirical evaluation on specific datasets requiring such splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameters or system-level training settings, as its focus is theoretical and algorithm design rather than empirical model training. |