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..
Social Choice Under Metric Preferences: Scoring Rules and STV
Authors: Piotr Skowron, Edith Elkind
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove bounds on distortion of scoring rules and STV in the utilitarian setting... Theorem 1. For every scoring rule RW we have Distm(RW) 1 + 2 ln m 1. ... Theorem 2. For every metric space M we have Dist M m (RH) = O m(ln m) 1/2 . ... Theorem 3. For every metric space M we have Dist M m (STV) = O(ln m). ... Theorem 4. There exists a metric space M such that Dist M m (STV) = Ω( ln m). |
| Researcher Affiliation | Academia | Piotr Skowron University of Oxford United Kingdom EMAIL Edith Elkind University of Oxford United Kingdom EMAIL |
| Pseudocode | No | The paper describes algorithms and processes in narrative text and mathematical formulations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and constructs abstract instances for proofs rather than utilizing or providing access to publicly available datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper that does not involve computational experiments requiring specific hardware specifications. |
| Software Dependencies | No | This is a theoretical paper and does not mention specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | No | This is a theoretical paper and does not describe experimental setups, hyperparameters, or system-level training settings. |