How to Make Knockout Tournaments More Popular?
Authors: Juhi Chaudhary, Hendrik Molter, Meirav Zehavi
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce TOURNAMENT VALUE MAXIMIZATION and analyze its computational complexity. Due to space constraints, the proofs of some results (marked with ) are deferred to the full version of this paper (Chaudhary, Molter, and Zehavi 2023). In Section 3, we prove that it is NP-hard (as well as APX-hard) in two highly restricted scenarios... Nevertheless, we provide a wide spectrum of positive results in Section 4. First, we provide a simple (1/ log n)-factor approximation algorithm... Second, we identify a large family of game-value functions that give rise to efficient algorithms... Our main results are summarized in Table 1. We hope that our work will open up the door for further studies of the maximization of the profit or popularity of tournaments, and present some directions for further research in Section 5. |
| Researcher Affiliation | Academia | Juhi Chaudhary, Hendrik Molter, Meirav Zehavi Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel juhic@post.bgu.ac.il, molterh@post.bgu.ac.il, meiravze@bgu.ac.il |
| Pseudocode | No | The paper describes algorithms (e.g., Algorithm A, dynamic program T) in textual steps and mathematical formulations, but it does not include explicitly labeled pseudocode blocks or algorithms formatted as code. |
| Open Source Code | No | The paper does not provide a link to open-source code for the methodology described. It references an arXiv paper (Chaudhary, Molter, and Zehavi 2023) as a full version, but this is not a code repository. |
| Open Datasets | No | This paper is theoretical and focuses on computational complexity analysis rather than empirical experiments. It defines problem inputs (e.g., 'A set N N of players'), but these are abstract problem instances, not real-world datasets used for training. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments with datasets, so there is no mention of training, validation, or test splits. |
| Hardware Specification | No | This paper is theoretical and focuses on algorithms and complexity analysis. It does not mention any specific hardware used for experiments. |
| Software Dependencies | No | This paper is theoretical and focuses on algorithms and complexity analysis. It does not mention specific software dependencies or their version numbers. |
| Experiment Setup | No | This paper is theoretical and does not conduct empirical experiments, so there are no details regarding experimental setup, hyperparameters, or system-level training settings. |