Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Envy-Free Mechanisms with Minimum Number of Cuts
Authors: Reza Alijani, Majid Farhadi, Mohammad Ghodsi, Masoud Seddighin, Ahmad Tajik
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide methods, namely expansion process and expansion process with unlocking, for dividing the cake under different assumptions on the valuation functions of the players. |
| Researcher Affiliation | Academia | Sharif University of Technology , Duke University , University of Michigan Ann Arbor , Institute for Research in Fundamental Sciences (IPM) School of Computer Science EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 EFISM algorithm |
| Open Source Code | No | The paper does not provide any concrete access information or links to open-source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper that does not describe empirical experiments involving datasets or training. Therefore, it does not provide information about publicly available datasets used for training. |
| Dataset Splits | No | This is a theoretical paper that does not describe empirical experiments involving datasets. Therefore, it does not provide information about training/validation/test dataset splits. |
| Hardware Specification | No | The paper describes theoretical algorithms and does not report on empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper describes theoretical algorithms and does not report on empirical experiments, thus no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper describes theoretical algorithms and does not report on empirical experiments, thus no experimental setup details like hyperparameters or system-level training settings are provided. |