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..
An Exercise in Tournament Design: When Some Matches Must Be Scheduled
Authors: Sushmita Gupta, Ramanujan Sridharan, Peter Strulo
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we initiate the algorithmic study of a novel variant of SE tournament manipulation that aims to model the fact that certain matchups are highly desired in a sporting context, incentivizing an organizer to manipulate the bracket to make such matchups take place. We obtain both hardness and tractability results. We show that while the problem of computing a bracket enforcing a given set of matches in an SE tournament is NP-hard, there are natural restrictions that lead to polynomial-time solvability. |
| Researcher Affiliation | Academia | Sushmita Gupta1, Ramanujan Sridharan2, Peter Strulo2 1The Institute of Mathematical Sciences, HBNI, India 2University of Warwick, UK |
| Pseudocode | Yes | Algorithm 1: 1 Q0,0 (V (T), S); 2 for 0 i < n do 3 for 0 j < ht g(vn i) do 4 if there exists y Child Qi,0(vn i) with sz Qi,0(y) = 2j then 5 Qi,j+1 Qi,j; ... |
| Open Source Code | No | The paper does not contain any explicit statements about making source code available, nor does it provide any links to a code repository. |
| Open Datasets | No | This is a theoretical paper focusing on algorithmic complexity, and therefore does not use or reference any datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper that does not involve experimental validation; therefore, there is no mention of training, validation, or test dataset splits. |
| Hardware Specification | No | This is a theoretical computer science paper focused on algorithm design and complexity analysis; therefore, it does not describe any experimental setup or specific hardware used. |
| Software Dependencies | No | This is a theoretical computer science paper focused on algorithm design and complexity analysis; therefore, it does not specify any software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | This is a theoretical computer science paper focused on algorithm design and complexity analysis; therefore, it does not include details on experimental setup, hyperparameters, or training settings. |