Representation with Incomplete Votes
Authors: Daniel Halpern, Gregory Kehne, Ariel D. Procaccia, Jamie Tucker-Foltz, Manuel Wüthrich
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, an empirical evaluation using real data shows that the proposed algorithm provides representative outcomes in practice.In Section 5 we show empirically (on real datasets from Polis and Reddit) that this extension allows us to find committees satisfying (approximate) JR (and stronger properties) despite access to little information (i.e., few voters, each voting on only a small fraction of the comments). |
| Researcher Affiliation | Academia | Daniel Halpern, Gregory Kehne, Ariel D. Procaccia, Jamie Tucker-Foltz and Manuel W uthrich Harvard University |
| Pseudocode | Yes | Algorithm 1: (k, t)-α-PAVAlgorithm 2: (k, t)-noisy-α-PAV |
| Open Source Code | No | The paper does not provide a statement or link indicating that the source code for the developed algorithms (α-PAV, noisy-α-PAV, ucb-α-PAV) is publicly available. |
| Open Datasets | Yes | Polis provides open-use data from real deliberations hosted on their platform.7 These include, for instance, a discussion organized by the government of Taiwan, which led to the successful regulation of Uber.The second dataset we consider consists of Reddit discussions.9 To obtain an interesting dataset, we combined voting data from two subreddits, r/politics and r/Conservative, which are arguably situated at opposite ends of the American political spectrum. |
| Dataset Splits | Yes | We split the data into training, validation, and testing as follows: 80% for training, 10% for validation, and 10% for testing. |
| Hardware Specification | Yes | All experiments were run on a machine with an AMD Ryzen Threadripper 3970X CPU and a single NVIDIA GeForce RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions using 'Lens Kit' as a matrix factorization library but does not provide specific version numbers for it or any other software dependencies crucial for reproducibility. |
| Experiment Setup | Yes | For all datasets, we assume that each voter votes on t = 20 comments. Since the total number of comments m ranges from 31 to 1719 across datasets, the percentage of comments each voter votes on, t/m, ranges from 1% to 65%. For each dataset, we run the algorithms with target committee sizes k = 5, 7, 10.For both Algorithm 2 and Algorithm 4 we treat ℓ, the number of times we ask voters about each candidate, as a parameter. In addition, for Algorithm 4, we replace the numerator in the confidence intervals errs with a parameter θ. Both ℓand θ were chosen based on validation on a separate dataset, see Appendix H for details. |