Voting by sequential elimination with few voters
Authors: Sylvain Bouveret, Yann Chevaleyre, François Durand, Jérôme Lang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we apply our rules to randomly generated data.In Section 5 we study the performance of elimination-based rules on randomly generated data. Now we show to which extent sequential elimination rules provide a good Borda score approximation in practice. To this end we have carried out two sets of experiments. |
| Researcher Affiliation | Academia | Sylvain Bouveret LIG Grenoble INP, France sylvain.bouveret@imag.fr Yann Chevaleyre LIPN Univ. Paris-Nord, France yann.chevaleyre@lipn.univ-paris13.fr Franc ois Durand U. Paris-Dauphine, CNRS, PSL, France fradurand@gmail.com J erˆome Lang CNRS, U. Paris-Dauphine, PSL, France lang@lamsade.dauphine.fr |
| Pseudocode | No | The paper defines the deterministic sequential elimination rule (SER) Fπ recursively with numbered steps but does not present it in a formally labeled pseudocode or algorithm block. |
| Open Source Code | No | No explicit mention or link to open-source code for the described methodology. |
| Open Datasets | Yes | Finally, we tested our approach on the real Sushi dataset from Pref Lib [Mattei and Walsh, 2013]. |
| Dataset Splits | No | The paper mentions generating 10000 profiles and sampling from the Sushi dataset but does not provide specific training, validation, or test splits or their proportions. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud resources) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies or their version numbers are mentioned in the paper. |
| Experiment Setup | Yes | In the first experiment, the number of voters n varies from 2 to 10 and the number of candidates is 2n+1 1. ... For each (n, m) and each culture, we focused on three different sequences: (i) geometric..., (ii) round-robin..., and (iii) random (single) dictator.... We have computed for each one the mean value of the differential ratio... over a sample of 10000 profiles generated for each (n, m) and culture. |