Winner Determination and Strategic Control in Conditional Approval Voting
Authors: Evangelos Markakis, Georgios Papasotiropoulos
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work focuses on a generalization of the classic Minisum approval voting rule... Additionally we investigate the complexity of problems related to the strategic control of such elections... We exhibit that in most variants of these problems, CMS is resistant against control. Our main goal is to investigate algorithmic and complexity aspects of solving and controlling elections under CMS. More precisely, our results in Section 3, provide characterizations for the families of CMS instances that can be placed in P and FPT. Theorem 1. If the global dependency graph of a CMS instance... has constant treewidth, then the problem is optimally solvable in polynomial time... Theorem 2. Every binary CSP with primal graph G, can be reduced in polynomial time to a CMS instance... Corollary 1. ...there is a polynomial algorithm for CMS(G) if and only if every graph in G has constant treewidth. |
| Researcher Affiliation | Academia | Athens University of Economics and Business |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not involve the use of datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not involve the use of datasets, thus no dataset split information is provided. |
| Hardware Specification | No | This is a theoretical paper and does not mention any specific hardware details used for running experiments. |
| Software Dependencies | No | This is a theoretical paper and does not specify software dependencies with version numbers for replication. |
| Experiment Setup | No | This is a theoretical paper and does not include details about an experimental setup, such as hyperparameters or training configurations. |