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].
Cake Cutting: Envy and Truth
Authors: Xiaohui Bei, Ning Chen, Guangda Huzhang, Biaoshuai Tao, Jiajun Wu
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that no deterministic truthful envy-free mechanism exists in the connected piece scenario, and the same impossibility result for the general setting with some additional mild assumptions on the allocations. Finally, we study a large market model where the economy is replicated and demonstrate that truth-telling converges to a Nash equilibrium. |
| Researcher Affiliation | Academia | School of Physical and Mathematical Sciences, Nanyang Technological University EMAIL, EMAIL, EMAIL, EMAIL Department of Computer Science and Engineering, University of Michigan EMAIL |
| Pseudocode | No | No pseudocode or algorithm block found. |
| Open Source Code | No | The paper does not provide any statement or link for open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not list software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setup details or hyperparameters. |