An Exercise in Tournament Design: When Some Matches Must Be Scheduled

Authors: Sushmita Gupta, Ramanujan Sridharan, Peter Strulo

AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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.