Efficient, Private, and eps-Strategyproof Elicitation of Tournament Voting Rules
Authors: David Timothy Lee
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we give algorithms which elicit approximate winners in a way which provably satisfies all three of these requirements simultaneously. Our results hold for tournament voting rules... Our results significantly expand the set of voting rules for which efficient elicitation was known to be possible and improve the known approximation factors for ϵstrategyproof voting in the regime where the number of candidates is large. |
| Researcher Affiliation | Academia | David T. Lee Stanford University davidtlee@stanford.edu |
| Pseudocode | Yes | ALGORITHM 1: BORDA+RANDOM... ALGORITHM 2: BORDA... ALGORITHM 3: SAMPLE-VOTERS+RANDOM... ALGORITHM 4: SAMPLE-VOTERS... ALGORITHM 5: SAMPLED-SUBSETS |
| Open Source Code | No | The paper mentions that detailed proofs can be found in the Appendix section of the full version via a URL, but it does not state that source code for the methodology is released. |
| Open Datasets | No | The paper does not mention using a specific publicly available dataset for experiments. It discusses theoretical models of voters and preferences. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with specific dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper describes theoretical algorithms and proofs, and therefore does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper describes theoretical algorithms and does not list any specific software dependencies with version numbers required for reproduction. |
| Experiment Setup | No | The paper focuses on theoretical algorithm design and does not provide details on experimental setup such as hyperparameters or training configurations. |