Explainable Voting
Authors: Dominik Peters, Ariel D. Procaccia, Alexandros Psomas, Zixin Zhou
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove, however, that outcomes of the important Borda rule can be explained using O(m2) steps, where m is the number of alternatives. Our main technical result is a general lower bound that, in particular, implies that the foregoing bound is asymptotically tight. |
| Researcher Affiliation | Academia | Dominik Peters Harvard University dpeters@seas.harvard.edu Ariel D. Procaccia Harvard University arielpro@seas.harvard.edu Alexandros Psomas Purdue University apsomas@cs.purdue.edu Zixin Zhou CFCS, Peking University zhouzixin1998@gmail.com |
| Pseudocode | No | The paper mentions that "Rules are typically explained to users through pseudocode" but does not provide any pseudocode or algorithm blocks for its own methods. |
| Open Source Code | No | No explicit statement or link indicating the release of source code for the methodology described in this paper. |
| Open Datasets | No | The paper mentions "election data from the 2009 mayoral election in Burlington, Vermont" as an example, but does not provide access information. It also references the "impartial culture assumption", which is a theoretical model for generating preferences, not a concrete publicly available dataset. |
| Dataset Splits | No | No experiments with data splits (training, validation, test) are described in the paper. |
| Hardware Specification | No | No specific hardware (e.g., CPU, GPU models, memory) used for computation or experiments is mentioned in the paper. |
| Software Dependencies | No | No specific software names with version numbers are mentioned that would be required to replicate the work. |
| Experiment Setup | No | No experimental setup details, such as hyperparameters or training configurations, are provided, as the paper is primarily theoretical. |