Optimally Protecting Elections
Authors: Yue Yin, Yevgeniy Vorobeychik, Bo An, Noam Hazon
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed algorithms on both synthetic and real data with respect to solution quality and scalability. Solution quality of our approach is compared to two baselines. The first, termed Random, is a uniformly random defense. The second, termed Greedy, deterministically protects m groups in which ! has the greatest advantage over the next best candidate in that group. Linear and mixed integer programs were solved using CPLEX 12.6.1.We randomly generated a tally for each candidate within each group uniformly in [0, 100]. Each data point is an average over 30 such samples. |
| Researcher Affiliation | Academia | Yue Yin1,2, Yevgeniy Vorobeychik3, Bo An4, Noam Hazon5 1Key Lab of Intelligent Information Processing, ICT, CAS, 2University of CAS, Beijing, China 3Dept. of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 4School of Computer Science and Engineering, Nanyang Technological University, Singapore 5Dept. of Computer Science, Ariel University, Israel |
| Pseudocode | Yes | Algorithm 1: Optimal Election Control by Group Deletion, Algorithm 2: Double Oracle Approach, Algorithm 3: Attacker s Better Response (AO-Better)., Algorithm 4: Defender Oracle with Better Response |
| Open Source Code | No | The paper does not explicitly state that source code for the described methodology is made publicly available or provide a link to a code repository. |
| Open Datasets | Yes | Finally, we evaluate our algorithms on the 2002 French president election dataset [Laslier and Van der Straeten, 2008], consisting of 2597 votes for 16 candidates by voters in 6 districts (voter groups). |
| Dataset Splits | No | The paper describes generating synthetic data and using a real-world dataset but does not explicitly provide details on training, validation, or test dataset splits needed for reproducibility. It only mentions 'Each data point is an average over 30 such samples' for synthetic data. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory, or cloud instance types used for running its experiments. It only mentions that 'Linear and mixed integer programs were solved using CPLEX 12.6.1.' |
| Software Dependencies | Yes | Linear and mixed integer programs were solved using CPLEX 12.6.1. |
| Experiment Setup | Yes | We randomly generated a tally for each candidate within each group uniformly in [0, 100]. Each data point is an average over 30 such samples. Figures 1(a) and 1(b) show the solution quality of the proposed algorithms and the baselines when there are 30 voter groups and 5 candidates. We model uncertainty by taking baseline voting tallies (generated as described above), and adding zero-mean Gaussian noise. We study two cases: low uncertainty, where the variance of Gaussian noise is 10, and high uncertainty, where tallies of candidates are drawn uniformly in [1, 400] and variance is 20. In both cases, we take 60 attacker types (drawn from this distribution) to be the ground truth. |