Spatial Voting with Incomplete Voter Information
Authors: Aviram Imber, Jonas Israel, Markus Brill, Hadas Shachnai, Benny Kimelfeld
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the computational complexity of finding the possible and necessary winners for positional scoring rules. Our results show that we retain tractable cases of the classic model where voters have partial-order preferences. Moreover, we show that there are positional scoring rules under which the possible-winner problem is intractable for partial orders, but tractable in the one-dimensional spatial setting. |
| Researcher Affiliation | Academia | 1Technion Israel Institute of Technology, Haifa, Israel 2Research Group Efficient Algorithms, TU Berlin, Germany 3Department of Computer Science, University of Warwick, UK |
| Pseudocode | Yes | A pseudocode of the algorithm is given in the full version of the paper. |
| Open Source Code | No | The paper does not mention releasing any open-source code for the methodologies or algorithms described within it. It focuses on theoretical complexity results. |
| Open Datasets | No | This paper is theoretical and does not describe any experiments involving datasets, training, or evaluation on data splits. |
| Dataset Splits | No | This paper is theoretical and does not involve experimental validation on datasets, thus no training, validation, or test splits are discussed. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware for execution or training. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or their version numbers that would be required to reproduce experimental results. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup, hyperparameters, or system-level training settings. |