Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fixing Tournaments for Kings, Chokers, and More
Authors: Michael P. Kim, Virginia Vassilevska Williams
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the tournament fixing problem (TFP), which asks whether a tournament organizer can rig a single-elimination (SE) tournament such that their favorite player wins, simply by adjusting the initial seeding. Prior results give two perspectives of TFP: on the one hand, deciding whether an arbitrary player can win any SE tournament is known to be NP-complete; on the other hand, there are a number of known conditions, under which a player is guaranteed to win some SE tournament. We extend and connect both these lines of work. We show that for a number of structured variants of the problem, where our player is seemingly strong, deciding whether the player can win any tournament is still NP-complete. Dual to this hardness result, we characterize a new set of sufficient conditions for a player to win a tournament. Further, we give an improved exact algorithm for deciding whether a player can win a tournament. |
| Researcher Affiliation | Academia | Michael P. Kim and Virginia V. Williams Computer Science Department Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1 COVERB(A, B, E) |
| Open Source Code | No | The paper does not contain any statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets that require public availability for training purposes. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not conduct empirical experiments, thus no hardware specifications for running experiments are provided. |
| Software Dependencies | No | The paper is theoretical and does not conduct empirical experiments that would require specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not conduct empirical experiments; thus, there are no experimental setup details like hyperparameters or training settings. |