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..
Ties in Multiwinner Approval Voting
Authors: Łukasz Janeczko, Piotr Faliszewski
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 3. We generate a number of elections, both synthetic and based on real-life data, and evaluate the frequency of ties. It turns out to be surprisingly high. ... Our code is available at https://github.com/ Project-PRAGMA/Ties-IJCAI-2023. |
| Researcher Affiliation | Academia | Łukasz Janeczko , Piotr Faliszewski AGH University, Poland EMAIL |
| Pseudocode | No | The paper describes algorithms in prose and mathematical notation but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/ Project-PRAGMA/Ties-IJCAI-2023. |
| Open Datasets | Yes | Pabu Lib Data. Pabu Lib is a library of real-life participatory budgeting (PB) instances, mostly from Polish cities [Faliszewski et al., 2023]. |
| Dataset Splits | No | The paper describes data generation models (Resampling Model, Interval Model, Pabu Lib Data) and explains how elections are generated, but it does not specify train/validation/test splits. |
| Hardware Specification | No | The paper describes experimental setups related to data generation and evaluation of tie frequencies but does not provide any specific hardware specifications. |
| Software Dependencies | No | The paper makes no mention of specific software dependencies or their version numbers. |
| Experiment Setup | Yes | In a basic experiment we fix the number of candidates m, the committee size k, and a statistical culture. Then, for each number n of voters between 20 and 100 (with a step of 1) we generate 1000 elections with m candidates and n voters, and for each of them compute whether our rules have a unique winning committee (we omit Greedy CCAV). |