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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Voting by sequential elimination with few voters
Authors: Sylvain Bouveret, Yann Chevaleyre, François Durand, Jérôme Lang
IJCAI 2017 | Venue PDF | 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 EMAIL Yann Chevaleyre LIPN Univ. Paris-Nord, France EMAIL Franc ois Durand U. Paris-Dauphine, CNRS, PSL, France EMAIL J erˆome Lang CNRS, U. Paris-Dauphine, PSL, France EMAIL |
| 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. |