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].
Comparing Election Methods Where Each Voter Ranks Only Few Candidates
Authors: Matthias Bentert, Piotr Skowron2218-2225
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We establish theoretical bounds on the approximation ratios and complement our theoretical analysis with computer simulations. |
| Researcher Affiliation | Academia | 1Algorithmics and Computational Complexity, Faculty IV, TU Berlin, Berlin, Germany EMAIL 2Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland EMAIL |
| Pseudocode | Yes | Algorithm 1: Algorithm α-PSF-ALG for positional scoring functions. Algorithm 2: Randomized Algorithm for Minimax. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper generates data using models like 'Impartial Culture', 'One-dimensional Euclidean Model', and 'Mixture of Mallows Models', but does not provide access information for a fixed, publicly available dataset. |
| Dataset Splits | No | The paper describes generating preferences for simulations but does not specify traditional train/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | We set the number m of candidates to 50 and tested for ℓ {2, 5, 8} and n ranging from 10 to 1000 in steps of 25. For each combination of values of (ℓ, n) we ran 500 independent experiments, each time computing the ratio r(A, D) between the score of the candidate returned by algorithm A to the score of the optimal candidate. |