On the Tree Representations of Dichotomous Preferences
Authors: Yongjie Yang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study numerous restricted domains of dichotomous preferences with respect to some tree structures. Particularly, we study the relationships among these domains and the ones proposed by Elkind and Lackner [2015]. We also show that recognizing all the restricted domains proposed in this paper is polynomial-time solvable. Finally, we explore the complexity of winner determination for several important approval-based multiwinner voting rules when restricted to these domains. |
| Researcher Affiliation | Academia | Yongjie Yang Chair of Economic Theory, Saarland University, Saarbr ucken, Germany yyongjiecs@gmail.com |
| Pseudocode | No | The paper describes algorithms in prose, for example, 'Based on this observation, we develop a dynamic-programming algorithm as follows.' It does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper focusing on computational complexity and mathematical properties of preference domains. It does not use empirical datasets in the context of training or evaluation, therefore no information about public dataset availability is provided. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical experiments or dataset splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper that does not report on empirical experiments requiring specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper discusses theoretical concepts, algorithms, and proofs. It references a textbook (Cormen et al., 2009) for a definition but does not mention any specific software dependencies with version numbers used for computation or analysis. |
| Experiment Setup | No | This is a theoretical paper that does not describe empirical experiments. Therefore, there are no experimental setup details like hyperparameters or training settings provided. |