Can We Predict the Election Outcome from Sampled Votes?
Authors: Evi Micha, Nisarg Shah2176-2183
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct experiments to measure the predictability of popular voting rules in the average case. We consider profiles σn with n = 1, 000 voters and m = 5 alternatives. We use two distributions to draw i.i.d. rankings in σn: the Mallows model with ϕ = 1/3 (in short, Mallows distribution ) and the uniform distribution. |
| Researcher Affiliation | Academia | Evi Micha, Nisarg Shah University of Toronto {emicha, nisarg}@cs.toronto.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | We use two distributions to draw i.i.d. rankings in σn: the Mallows model with ϕ = 1/3 (in short, Mallows distribution ) and the uniform distribution. |
| Dataset Splits | No | The paper describes drawing rankings i.i.d. from distributions and sampling votes, but it does not specify explicit train/validation/test dataset splits of a fixed dataset. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models or types of computing resources used for experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or specific tool versions). |
| Experiment Setup | Yes | We consider profiles σn with n = 1, 000 voters and m = 5 alternatives. ... with k = 50 (tables on the left) and with k = 500 (tables on the right). ... We average our results across 10^6 draws of profile σn. |