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].
Statistical Foundations of Virtual Democracy
Authors: Anson Kahng, Min Kyung Lee, Ritesh Noothigattu, Ariel Procaccia, Christos-Alexandros Psomas
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results further support, and more precisely measure, the robustness of Borda count. and Finally, we provide empirical results that further strengthen our case for the robustness of Borda count. |
| Researcher Affiliation | Academia | 1School of Computer Science, Carnegie Mellon University, Pittsburgh, USA. |
| Pseudocode | No | The paper describes methods through text and mathematical formulations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | All of our code is open-source and can be found at https://github.com/akahng/Virtual Democracy-ICML2019. |
| Open Datasets | No | The paper generates synthetic data for its experiments (from a mixture of Mallows models) rather than using a pre-existing publicly available dataset. |
| Dataset Splits | No | The paper describes generating synthetic data for its experiments and does not specify training, validation, or test splits for model training within its own experimental setup. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU, CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for key software components used in the experiments. |
| Experiment Setup | Yes | Throughout our experiments, we let n = 100, m = 40, φ {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}, and p {1, 0.7, 0.5}. |