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].
Effective Heuristics for Committee Scoring Rules
Authors: Piotr Faliszewski, Martin Lackner, Dominik Peters, Nimrod Talmon
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then provide an experimental evaluation of our heuristics (and two others, known from the literature): we compare the scores of the committees output by our algorithms to the scores of the optimal committees, and also use the two-dimensional Euclidean domain to compare the visual representations of the outputs of our algorithms. We evaluate all our heuristics experimentally on synthetic data, generated using the impartial culture model and the two-dimensional Euclidean model. |
| Researcher Affiliation | Academia | Piotr Faliszewski AGH University Krakow, Poland EMAIL Martin Lackner TU Wien Vienna, Austria EMAIL Dominik Peters University of Oxford Oxford, UK EMAIL Nimrod Talmon Weizmann Institute of Science Rehovot, Israel EMAIL |
| Pseudocode | No | The paper describes the algorithms (Simulated Annealing, Greedy, Removal, Banzhaf) in textual paragraphs explaining their steps, but does not include formal pseudocode blocks or labeled algorithm sections. |
| Open Source Code | No | The paper does not provide any specific links to source code repositories, nor does it explicitly state that the code for its methodology is made publicly available. |
| Open Datasets | No | We evaluate all our heuristics experimentally on synthetic data, generated using the impartial culture model and the two-dimensional Euclidean model. We generate voters preferences as follows: 1. In the impartial culture (IC) model, for each voter we draw her preference order uniformly at random from the set of all possible linear orders. 2. In the 2D uniform square (2D) model, each candidate and each voter is associated with a point drawn uniformly at random from the square [โ3, 3] ร [โ3, 3]... The paper describes how the data was generated but does not provide access information for the specific generated datasets. |
| Dataset Splits | No | The paper generates synthetic elections for experimental evaluation rather than using a fixed dataset with predefined training, validation, and test splits. For example, 'we generated 5000 elections' for the t-Borda experiments, and 'we generated 1000 elections' for the Chamberlin Courant experiments. |
| Hardware Specification | Yes | to compute the results for 100 candidates, 100 voters, and committee size k = 10 (on an of๏ฌce machine with Intel i5 760 CPU and 8GB of RAM) |
| Software Dependencies | No | The paper mentions using 'the CPLEX ILP solver' but does not provide a specific version number for it. |
| Experiment Setup | Yes | The algorithm uses parameters p, q and T, 0 < p, q < 1, T โ N. ... We use the same parameters as Faliszewski et al. (2017a); we run T = 2000 iterations with p = 0.02 and q = 0.999. We consider synthetically generated elections with m = 100 candidates and n = 100 voters. We ๏ฌxed the committee size to be k = 10. |