Randomized Social Choice Functions Under Metric Preferences
Authors: Elliot Anshelevich, John Postl
JAIR 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We determine the quality of randomized social choice algorithms in a setting in which the agents have metric preferences... We provide new distortion bounds for a variety of randomized algorithms, for both general metrics and for important special cases. Our results show a sizable improvement in distortion over deterministic algorithms. |
| Researcher Affiliation | Academia | Elliot Anshelevich EMAIL John Postl EMAIL Rensselaer Polytechnic Institute 110 8th Street Troy, NY 12180 |
| Pseudocode | Yes | Algorithm 1 Optimal randomized algorithm for the α-decisive, 1-Euclidean space... Algorithm 2 Uncovered Set Min-Cover |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the described methodology. It mentions open-source code in the context of related work but not for its own contributions. |
| Open Datasets | No | The paper presents theoretical research on social choice functions under metric preferences. It defines models and proves theorems but does not use or reference any empirical datasets for evaluation. |
| Dataset Splits | No | The paper presents theoretical research and does not use any empirical datasets, therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not involve experimental runs on specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and proofs. It does not mention any specific software dependencies or versions required for implementation or experimentation. |
| Experiment Setup | No | The paper presents theoretical work on randomized social choice functions and distortion bounds. It does not describe any experimental setup, hyperparameters, or training configurations. |