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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Communication, Distortion, and Randomness in Metric Voting
Authors: David Kempe2087-2094
AAAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,765 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,488 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that any one-round deterministic voting mechanism in which each voter communicates only the candidates she ranks in a given set of k positions must have distortion at least 2n k k ; we give a mechanism achieving an upper bound of O(n/k), which matches the lower bound up to a constant. |
| Researcher Affiliation | Academia | David Kempe University of Southern California |
| Pseudocode | No | The paper describes algorithms in prose, such as 'The mechanism M is as follows...' or 'Return the first choice of a uniformly random voter', but does not present them in formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | This is a theoretical paper and does not involve the use of datasets, training, or empirical evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not involve the use of datasets or their splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper and does not discuss hardware specifications used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not mention software dependencies with specific version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not provide experimental setup details or hyperparameters. |