Rules for Choosing Societal Tradeoffs
Authors: Vincent Conitzer, Rupert Freeman, Markus Brill, Yuqian Li
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments We generated three classes of voting profiles and compare the different algorithms performances in terms of running time, penalty (LP s objective), and the distance between the aggregated result and the ground truth (if there is one). ... Results are shown in Figures 5 and 6. |
| Researcher Affiliation | Academia | Vincent Conitzer, Rupert Freeman, Markus Brill, and Yuqian Li Department of Computer Science Duke University Durham, NC 27708, USA {conitzer,rupert,brill,yuqian}@cs.duke.edu |
| Pseudocode | No | The paper describes algorithms (e.g., 'a linear program formulation for computing its outcomes, as well as a simple hill-climbing algorithm'), but it does not include pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper states, 'We generated three classes of voting profiles...' and describes the generative process for these synthetic datasets. However, it does not provide any links, citations, or explicit statements about these generated datasets being publicly available. |
| Dataset Splits | No | The paper describes generating synthetic voting profiles for its experiments but does not specify any training, validation, or test dataset splits. The experimental setup involves generating different types of profiles for comparison, not splitting a pre-existing dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper mentions the use of 'GLPK is the optimal LP solver (using the GNU linear programming kit)', but it does not specify version numbers for this or any other software dependencies, which would be necessary for reproducibility. |
| Experiment Setup | No | The paper describes how voting profiles were generated for experiments and general algorithm comparisons, but it does not provide specific experimental setup details such as hyperparameter values, optimizer settings, or detailed training configurations. |