Thwarting Vote Buying Through Decoy Ballots
Authors: David C. Parkes, Paul Tylkin, Lirong Xia
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In simulation, we show that the EA can make effective use of decoy ballots to maintain election integrity (e.g., reducing the probability that the buyer changes the outcome to less than 1%). For the optimal defense, we are able to achieve this by adding a small number of decoys that are proportional in quantity to the number of ballots the buyer can afford to buy. Interestingly, a civic duty defense that allocates decoys to a random subset of those who request one is almost as effective as the optimal defense in which the EA optimizes the distribution of voter types that receive decoys. We describe the results of an extensive simulation study to compare power of various defenses in preventing a buyer succeeding in changing the outcome of an election. |
| Researcher Affiliation | Academia | David C. Parkes Harvard University parkes@eecs.harvard.edu Paul Tylkin Harvard University ptylkin@g.harvard.edu Lirong Xia Rensselaer Polytechnic Institute xial@cs.rpi.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper uses simulated data based on a "voter type distribution f = Beta(2, 4)" but does not refer to a publicly available or open dataset with access information. |
| Dataset Splits | No | The paper describes a simulation study with a voter type distribution, but it does not specify training, validation, or test dataset splits in the context of typical machine learning experiments. It uses parameters for the simulation (e.g., f = Beta(2,4), number of ballots, buyer budgets). |
| Hardware Specification | No | The paper describes a simulation study and does not provide any details about the specific hardware (GPU, CPU models, etc.) used to run these simulations. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers used for the simulation or analysis. |
| Experiment Setup | Yes | We choose to present results for voter type distribution f = Beta(2, 4), but the analysis is qualitatively unchanged for other distributions, including those with mean voting types in [0.01,0.49]. Figure 4 fixes the number of real ballots, and shows that vote buying can be successfully thwarted by issuing sufficiently many decoy ballots. Figure 2(a) shows the effect of varying the fraction of real ballots when using an optimal defense. Figures 2(b) and 2(c) show the effect of the civic duty defense and auction-based defensse for different values of model parameter x C (the max type requesting a decoy ) and x A (the max type winning a decoy ), with the EA optimizing the number of decoys for each value of x C and x A, respectively. In Figure 3 (with 1000 total ballots) we see that an optimal defense can use decoys to protect against buyers with around twice the budget of a no defense approach that just uses real ballots. |