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..
Ballot Length in Instant Runoff Voting
Authors: Kiran Tomlinson, Johan Ugander, Jon Kleinberg
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we analyze a collection of 168 real-world elections, where we truncate rankings to simulate shorter ballots. We find that shorter ballots could have changed the outcome in one quarter of these elections. |
| Researcher Affiliation | Academia | 1Cornell University 2Stanford University EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and data are available at https://github.com/tomlinsonk/irv-ballot-length. |
| Open Datasets | Yes | Finally, we use data from 168 real-world elections from Pref Lib (Mattei and Walsh 2013) |
| Dataset Splits | No | The paper analyzes real-world election data and simulated profiles, but does not describe traditional training/validation/test dataset splits for model development. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For the general profiles, we fix 1000 voters. For 1-Euclidean profiles, we simulate an infinite voter population uniformly distributed over [0, 1], where the number of first-place votes a candidate i has is the size of the interval of [0, 1] containing points closer to i than any other candidate. |