Multiwinner Approval Rules as Apportionment Methods
Authors: Markus Brill, Jean-Francois Laslier, Piotr Skowron
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We establish a link between multiwinner elections and apportionment problems by showing how approval-based multiwinner election rules can be interpreted as methods of apportionment. We consider several multi-winner rules and observe that some, but not all, of them induce apportionment methods that are well established in the literature and in the actual practice of proportional representation. For instance, we show that Proportional Approval Voting induces the D Hondt method and that Monroe s rule induces the largest remainder method. We also consider properties of apportionment methods and exhibit multiwinner rules that induce apportionment methods satisfying these properties. |
| Researcher Affiliation | Academia | Markus Brill University of Oxford mbrill@cs.ox.ac.uk Jean-Franc ois Laslier CNRS and Paris School of Economics jean-francois.laslier@ens.fr Piotr Skowron University of Oxford piotr.skowron@cs.ox.ac.uk |
| Pseudocode | No | The paper describes procedures in natural language, for example: “Start with the empty seat allocation (0, . . . , 0) and iteratively assign a seat to a party Pi maximizing vi d(si) where si is the number of seats that have already been allocated to party Pi.” However, these are not formatted as distinct pseudocode blocks or algorithms. |
| Open Source Code | No | All proofs can be found in the full version of this paper (Brill, Laslier, and Skowron 2016). Explanation: The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes formal instances of apportionment and multiwinner election problems (e.g., (v, h) and (A, k)), which are theoretical constructs, not concrete datasets used for training or evaluation. Explanation: This is a theoretical paper that defines abstract problem instances rather than using concrete, publicly available datasets for empirical analysis. |
| Dataset Splits | No | This is a theoretical paper that does not involve empirical experiments with dataset splits. Explanation: The paper is theoretical and does not involve empirical experiments with training, validation, or test data splits. |
| Hardware Specification | No | This is a theoretical paper that does not involve empirical experiments, and therefore, no hardware specifications are provided. Explanation: The paper is theoretical and does not report on computational experiments that would require specific hardware specifications. |
| Software Dependencies | No | This is a theoretical paper. No software dependencies with specific version numbers are mentioned that would be necessary to replicate any computational work. Explanation: The paper is theoretical and does not mention any software dependencies with specific version numbers required for replication. |
| Experiment Setup | No | This is a theoretical paper that does not involve empirical experiments, and therefore, no experimental setup details like hyperparameters or system-level settings are provided. Explanation: The paper is theoretical and does not describe any empirical experimental setup, hyperparameters, or training configurations. |