Multiwinner Voting with Restricted Admissible Sets: Complexity and Strategyproofness
Authors: Yongjie Yang, Jianxin Wang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We first investigate the question of how efficiently an optimal committee can be calculated in this setting, i.e., the complexity of the winner determination problem. Whether winners with respect to a voting rule can be calculated efficiently is an important factor to evaluate the quality of the rule. We particularly focus on approval voting (AV), net-approval voting (NAV), proportional approval voting (PAV), Chamberlin-Courant approval voting (CCAV), satisfaction approval voting (SAV), and net SAV (NSAV) in our setting 1, aiming to reveal how different combinatorial restrictions on admissible sets shape the complexity of winner determination for these rules. [...] Strategyproofness is another important factor to evaluate the quality of voting rules. We explore the strategyproofness of multiwinner rules with different classes of admissible sets, and obtain some interesting results. |
| Researcher Affiliation | Academia | Yongjie Yang1,2, Jianxin Wang1 1 School of Information Science and Engineering, Central South University, Changsha, China 2 Chair of Economic Theory, Saarland University, Saarbr ucken, Germany yyongjiecs@gmail.com, jxwang@csu.edu.cn, |
| Pseudocode | No | The paper describes algorithms verbally within proofs (e.g., 'Our algorithm is based on standard dynamic programming technique') but does not present any formal pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not contain any statement or link indicating that open-source code for the methodology is provided. |
| Open Datasets | No | This is a theoretical paper that does not conduct experiments involving datasets, and therefore does not mention dataset availability for training. |
| Dataset Splits | No | This is a theoretical paper that does not conduct experiments involving datasets, and therefore does not mention training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or system-level training settings. |