The Smoothed Possibility of Social Choice
Authors: Lirong Xia
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop a framework that leverages the elegant smoothed complexity analysis to answer the key question above. ... Using our smoothed analysis framework, we obtain the following two dichotomy theorems on the asymptotic smoothed likelihood of Condorcet s paradox and the ANR impossibility under mild assumptions... To prove our theorems, we first model various events of interest as systems of linear constraints. Then, we develop a technical tool (Lemma 1)... |
| Researcher Affiliation | Academia | Lirong Xia, RPI, xialirong@gmail.com |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to source code or statements about its availability. |
| Open Datasets | No | This is a theoretical paper and does not involve the use of datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not involve the use of datasets for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe software dependencies with version numbers required for experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |