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 [1].
Voting with Rank Dependent Scoring Rules
Authors: Judy Goldsmith, Jérôme Lang, Nicholas Mattei, Patrice Perny
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study some properties of these rules, and show, empirically, that certain RDSRs are less manipulable than Borda voting, across a variety of statistical cultures. Then we give experimental results that show that under several distributions over profiles, some typical members of the Borda family are less frequently manipulable by a single voter than the Borda rule. |
| Researcher Affiliation | Academia | Judy Goldsmith University of Kentucky Lexington, KY, USA EMAIL Jerˆome Lang LAMSADE-CNRS, Universit e Paris-Dauphine Paris, France EMAIL Nicholas Mattei NICTA and UNSW Sydney, Australia EMAIL Patrice Perny LIP6-CNRS, UPMC Paris, France EMAIL |
| Pseudocode | No | The paper provides mathematical definitions and formulas, but it does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | No | The paper describes using 'five generative statistical cultures to create profiles for our testing,' such as Impartial Culture and Mallows Mixture Models. These are methods for generating data, not specific, publicly accessible, pre-existing datasets with concrete access information (links, DOIs, formal citations). |
| Dataset Splits | No | The paper mentions generating '1000 random instances' for testing but does not specify any training, validation, or test dataset splits in terms of percentages, absolute counts, or citations to predefined splits. The data is generated on-the-fly for testing purposes. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, cloud instances) used to run the experiments. |
| Software Dependencies | No | The paper does not list any specific software components or libraries with their version numbers that would be necessary to replicate the experiments. |
| Experiment Setup | No | The paper describes the general approach to its empirical evaluation, such as generating 1000 instances and using brute force search for manipulation. However, it does not provide specific experimental setup details like hyperparameters, model initialization, or optimizer settings, which are typically found in machine learning contexts but are not relevant to this type of social choice research. |