Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Can We Predict the Election Outcome from Sampled Votes?
Authors: Evi Micha, Nisarg Shah2176-2183
AAAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 18,949 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,161 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL |
| 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. |