Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Randomized Social Choice Functions Under Metric Preferences
Authors: Elliot Anshelevich, John Postl
JAIR 2017 | Venue PDF | 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. |