Misrepresentation in District Voting
Authors: Yoram Bachrach, Omer Lev, Yoad Lewenberg, Yair Zick
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide simulation results for several such probabilistic election models, showing the effects of the number of voters and candidates on the misrepresentation ratio. We now use our Algorithm Expected-MR to analyze the MR in several voting domains. |
| Researcher Affiliation | Collaboration | Yoram Bachrach Microsoft Research UK yobach@microsoft.com Omer Lev Univ. of Toronto Canada omerl@cs.toronto.edu Yoad Lewenberg Hebrew Univ. of Jerusalem Israel yoadlew@cs.huji.ac.il Yair Zick Carnegie Mellon Univ. USA yairzick@cs.cmu.edu |
| Pseudocode | Yes | Algorithm 1 Monte-Carlo MR Approximation |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code for the work described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | No | The paper describes generating synthetic data using probabilistic models like the Mallows model and specific voter distribution rules for simulations, rather than using or providing access to a pre-existing publicly available dataset. |
| Dataset Splits | No | The paper describes simulation experiments but does not provide specific dataset split information (percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | We fix C = {w, p}, and the number of districts to 11: 6 districts of type A and 5 of type B, modeling heterogeneous and homogenous districts respectively. In type A districts, every voter v votes randomly with Pr[v votes for w] = 1/2 + ", and Pr[v votes for p] = 1/2 - "; in type B districts, Pr[v votes for w] = ", Pr[v votes for p] = 1 - ", for " ", ". In our second experiment, we fix the number of districts to 15, and range the number of voters in every district from 100 to 5000. We examined MR of elections with m {3, 4, ..., 7} candidates. ... Under the Mallows model every voter compares every pair of candidates independently and ranks them correctly (according to ) with probability . For every district, was drawn uniformly at random and [0.01, 2, 1). |