Social Choice Under Metric Preferences: Scoring Rules and STV
Authors: Piotr Skowron, Edith Elkind
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 p.k.skowron@gmail.com Edith Elkind University of Oxford United Kingdom elkind@cs.ox.ac.uk |
| 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. |