On the Distortion of Voting With Multiple Representative Candidates
Authors: Yu Cheng, Shaddin Dughmi, David Kempe
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is a clean and tight characterization of positional voting rules that have constant expected distortion (independent of the number of candidates and the metric space). Our characterization result immediately implies constant expected distortion for Borda Count and elections in which each voter approves a constant fraction of all candidates. On the other hand, we obtain super-constant expected distortion for Plurality, Veto, and approving a constant number of candidates. These results contrast with previous results on voting with metric preferences: When the candidates are chosen adversarially, all of the preceding voting rules have distortion linear in the number of candidates or voters. Thus, the model of representative candidates allows us to distinguish voting rules which seem equally bad in the worst case. |
| Researcher Affiliation | Academia | Yu Cheng Duke University Shaddin Dughmi University of Southern California David Kempe University of Southern California |
| Pseudocode | No | The paper contains mathematical derivations and proofs but no pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of source code. |
| Open Datasets | No | This is a theoretical paper analyzing voting systems with candidates drawn from a theoretical distribution in a metric space. It does not use or provide access information for a public dataset. |
| Dataset Splits | No | This is a theoretical paper. It does not describe empirical experiments with training, validation, or test splits. |
| Hardware Specification | No | This is a theoretical paper that does not involve empirical experiments requiring specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper and does not mention specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or training settings. |