Structure in Dichotomous Preferences
Authors: Edith Elkind, Martin Lackner
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The goal of this paper is to extend this line of research to the setting where voters preferences are dichotomous, i.e., each voter approves a subset of candidates and disapproves the remaining candidates. We propose several analogues of the notions of single-peaked and single-crossing preferences for dichotomous profiles and investigate the relationships among them. We then demonstrate that for some of these notions the respective restricted domains admit efficient algorithms for computationally hard approval-based multi-winner rules. |
| Researcher Affiliation | Academia | Edith Elkind University of Oxford, UK elkind@cs.ox.ac.uk Martin Lackner Vienna University of Technology, Austria lackner@dbai.tuwien.ac.at |
| Pseudocode | No | The paper describes algorithms verbally (e.g., in Theorem 9, 'This dynamic program has n 2s (k + 1) states, and the value of each state is computed using O(2s) arithmetic operations.'), but it does not include explicitly labeled pseudocode blocks or figures. |
| Open Source Code | No | The paper states: 'The full version of this paper is available on ar Xiv [Elkind and Lackner, 2015].' This refers to a preprint, not a code repository, and no other statements explicitly providing open-source code for the described methodology were found. |
| Open Datasets | No | The paper is theoretical and does not describe or use a specific dataset for experiments. It focuses on abstract preference profiles and algorithmic tractability. |
| Dataset Splits | No | The paper focuses on theoretical analysis and algorithm design; it does not report empirical experiments that would require training, validation, or test dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical and algorithmic results and does not describe any empirical experiments that would necessitate the mention of specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and complexity. It does not mention specific software dependencies with version numbers for reproducing empirical experiments. |
| Experiment Setup | No | The paper focuses on theoretical algorithms and their complexity. It does not describe an empirical experimental setup with hyperparameters or system-level training settings. |