Worst-Case Voting When the Stakes Are High
Authors: Anson Kahng, Gregory Kehne5100-5107
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we evaluate the additive distortion of a range of rules on real-world election data. ... We evaluated the performance of various SCFs on four datasets of election data from Pref Lib (Mattei and Walsh 2013): Vermont consists of data from public office elections in 2014 ... Glasgow consists of data from the 2007 Glasgow City Council elections ... Debian consists of votes for the Debian logo ... and APA consists of election data from the American Psychological Association ... |
| Researcher Affiliation | Academia | Anson Kahng1, Gregory Kehne2 1 University of Toronto 2 Harvard University |
| Pseudocode | Yes | Algorithm 1: ADDITIVEOPTIMAL Input: Ranking σ Sn A Output: Distribution p m minimizing dist+(p, σ) for a, b A do wb a P i(σ 1 i ) 11{b i a} end for wa (wb a)b A for each a A p arg minp{D : wa a p T wa D a A, p A} return p |
| Open Source Code | No | The paper does not provide a direct link or an explicit statement about the release of its source code for the described methodology. |
| Open Datasets | Yes | We evaluated the performance of various SCFs on four datasets of election data from Pref Lib (Mattei and Walsh 2013): Vermont consists of data from public office elections in 2014 ... Glasgow consists of data from the 2007 Glasgow City Council elections ... Debian consists of votes for the Debian logo ... and APA consists of election data from the American Psychological Association |
| Dataset Splits | No | The paper describes the datasets used (Vermont, Glasgow, Debian, APA) and characteristics like number of candidates and voters, but does not provide specific details on how the data was split into training, validation, and test sets. No percentages, sample counts, or cross-validation methods are mentioned for reproduction. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, processors, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, or other libraries/solvers) that would be needed to reproduce the experiments. |
| Experiment Setup | No | The paper describes the definitions and properties of the social choice functions (e.g., scoring vectors) but does not provide specific experimental setup details such as hyperparameters, optimizer settings, or training configurations for any computational models or algorithms. |