Multiwinner Voting with Possibly Unavailable Candidates
Authors: Markus Brill, Hayrullah Dindar, Jonas Israel, Jérôme Lang, Jannik Peters, Ulrike Schmidt-Kraepelin
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we formalize the mentioned model and adapt the concept of a query policy (Boutilier et al. 2014) to multiwinner elections. Such a policy queries candidates for their availability, with the constraint that a queried candidate has to be included in the selection once they signal their availability. We then study common approval-based committee (ABC) rules and investigate whether these rules admit a safe query policy, i.e., a policy that only queries candidates that are guaranteed to be included in an optimal selection whenever they are available, whatever the other available candidates are. We show that under a mild technical assumption a necessary and sufficient condition for a rule to admit a safe query policy is sequentiality (to be formally defined later). |
| Researcher Affiliation | Academia | 1 Research Group Efficient Algorithms, Technische Universit at Berlin, Germany 2 Department of Computer Science, University of Warwick, Coventry, UK 3 CNRS, Universit e Paris-Dauphine, PSL, LAMSADE, France markus.brill@warwick.ac.uk, hayrullah.dindar@tu-berlin.de, j.israel@tu-berlin.de, lang@lamsade.dauphine.fr, jannik.peters@tu-berlin.de, u.schmidt-kraepelin@tu-berlin.de |
| Pseudocode | No | The paper describes algorithms and procedures but does not present them in a formalized pseudocode block or a clearly labeled algorithm section. |
| Open Source Code | No | Omitted proofs can be found in the full version of this paper (available at https://www.markus-brill.de/). This link is for omitted proofs, not for the source code of the methodology described in the paper. |
| Open Datasets | No | This is a theoretical paper and does not involve training models on datasets. Therefore, no information about public datasets for training is provided. |
| Dataset Splits | No | This is a theoretical paper and does not involve data splits for validation or training. Therefore, no information about training/validation/test splits is provided. |
| Hardware Specification | No | This is a theoretical paper that does not involve computational experiments requiring specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper that does not involve computational experiments requiring specific software dependencies with version numbers. Therefore, no software dependency details are provided. |
| Experiment Setup | No | This is a theoretical paper and does not involve empirical experiments with specific setups or hyperparameters. Therefore, no experimental setup details are provided. |