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].
A Dictatorship Theorem for Cake Cutting
Authors: Simina Brânzei, Peter Bro Miltersen
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main results of our paper are impossibility theorems closely releated to the result of Kurokawa et al., but rather than stating that no fair allocation can computed, we essentially state that no reasonable allocation can be computed at all; thus, the unfairness conclusions of our theorems are stronger. |
| Researcher Affiliation | Academia | Simina Brˆanzei and Peter Bro Miltersen Department of Computer Science Aarhus University, Denmark |
| Pseudocode | Yes | Algorithm 1: Cut-and-Choose protocol; Algorithm 2: Randomized Robertson-Webb protocol that is truthful in expectation and almost perfect |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | This paper is theoretical and does not conduct experiments with datasets, thus no training data is mentioned or made available. |
| Dataset Splits | No | This paper is theoretical and does not conduct empirical experiments with datasets, so no validation dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper focuses on theoretical proofs and algorithm descriptions (pseudocode), not empirical implementations. Therefore, it does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments or specific implementations that would require detailing an experimental setup, hyperparameters, or training configurations. |