Fixing Knockout Tournaments With Seeds
Authors: Pasin Manurangsi, Warut Suksompong
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that certain structural conditions that guarantee that a player can win a knockout tournament without seeds are no longer sufficient in light of seed constraints. On the other hand, we prove that when the pairwise match outcomes are generated randomly, all players are still likely to be knockout winners under the same probability threshold with seeds as without seeds. In addition, we investigate the complexity of deciding whether a manipulation is possible when seeds are present. |
| Researcher Affiliation | Collaboration | 1Google Research, USA 2School of Computing, National University of Singapore, Singapore |
| Pseudocode | No | The paper describes algorithms in prose (e.g., "Consider the following algorithm. In the first round...") but does not include structured pseudocode blocks or explicitly labeled algorithm sections. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the methodology or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical models (e.g., "generalized random model", "tournament graph T") rather than empirical studies on specific datasets, so there is no mention of publicly available datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments with dataset splits, thus no validation split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments that would require specific hardware specifications for computation. No hardware details are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments that would require specific software dependencies with version numbers. No software dependencies are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments with hyperparameters, training configurations, or system-level settings. No such details are provided. |