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].
Stable Invitations
Authors: Hooyeon Lee, Yoav Shoham
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate the computational complexity of ο¬nding such an invitation when agents are truthful, as well as the mechanism design problem when agents act strategically. If we assume truthful agents, we have an algorithm design problem, and we obtain positive results in this case. If we assume strategic agents, we have a mechanism design problem, and we obtain an impossibility result in general as well as positive results for a special case of the problem. |
| Researcher Affiliation | Academia | Hooyeon Lee Computer Science Department Stanford University EMAIL Yoav Shoahm Computer Science Department Stanford University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access (e.g., links or explicit statements of release) to open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use or describe any datasets for training or analysis, thus no information about public availability or access. |
| Dataset Splits | No | The paper is theoretical and does not involve data processing with train/validation/test splits. |
| Hardware Specification | No | The paper does not describe any hardware specifications, as it focuses on theoretical analysis and algorithm design without empirical experimentation. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or versions required for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or training configurations. |