Explaining Preferences by Multiple Patterns in Voters’ Behavior
Authors: Sonja Kraiczy, Edith Elkind
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we study the complexity of deciding whether voters preferences can be explained in this manner. For k = 2, we use the technique developed by Yang [2020] in the context of single-peaked preferences to obtain a polynomial-time algorithm for several domains: value-restricted preferences, group-separable preferences, and a natural subdomain of group-separable preferences, namely, caterpillar group-separable preferences. For k 3, the problem is known to be hard for single-peaked preferences; we show that this is also the case for value-restricted and group-separable preferences. |
| Researcher Affiliation | Academia | Sonja Kraiczy and Edith Elkind Department of Computer Science, University of Oxford Sonja.Kraiczy@merton.ox.ac.uk, elkind@cs.ox.ac.uk |
| Pseudocode | No | The paper describes algorithmic steps in paragraph form, such as in Section 3 (Partitioning Voters into Two Groups) and Section 5 (Group-separability on a Caterpillar), but does not present them as structured pseudocode blocks or clearly labeled algorithm figures. |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical, focusing on computational complexity and mathematical characterizations of preference profiles. It does not use or refer to any empirical datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data; therefore, it does not mention training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setups or report on empirical results that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and complexity analysis. It does not describe any computational experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments. Therefore, it does not include details about an experimental setup, such as hyperparameters or system-level training settings. |