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].
Refined Characterizations of Approval-Based Committee Scoring Rules
Authors: Chris Dong, Patrick Lederer
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We address this issue by characterizing two important subclasses of ABC scoring rules in the standard ABC election model, thereby both extending the result for ABC ranking rules to the standard setting and refining it to subclasses. In more detail, by relying on a consistency axiom for variable electorates, we characterize (i) the prominent class of Thiele rules and (ii) a new class of ABC voting rules called ballot size weighted approval voting. Based on these theorems, we also infer characterizations of three well-known ABC voting rules, namely multi-winner approval voting, proportional approval voting, and satisfaction approval voting. |
| Researcher Affiliation | Academia | School of Computation, Information, and Technology, Technical University of Munich |
| Pseudocode | No | The paper presents theorems and proof sketches but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code is made available. |
| Open Datasets | No | This paper is theoretical and does not involve training models on datasets. Therefore, no information about public datasets is provided. |
| Dataset Splits | No | This paper is theoretical and does not involve validation sets. Therefore, no information about dataset splits for validation is provided. |
| Hardware Specification | No | This paper is theoretical and does not involve computational experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and does not describe software implementations or dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not describe experimental setups, hyperparameters, or training configurations. |