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..
Balanced and Fair Partitioning of Friends
Authors: Argyrios Deligkas, Eduard Eiben, Stavros D. Ioannidis, Dušan Knop, Šimon Schierreich
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our initial contribution is the generalization of the model of (Li et al. 2023), which goes beyond binary, symmetric, and additive utilities and thus, it can capture more real-life scenarios. Having this as our foundation, our contribution is threefold: (a) we adapt several fairness notions that have been developed in the fair division literature to our setting; (b) we give several existence guarantees supported by polynomial-time algorithms; (c) we initiate the study of the computational (and parameterized) complexity of the model and provide an almost complete landscape of the (in)tractability frontier for our fairness concepts. |
| Researcher Affiliation | Academia | 1Royal Holloway, University of London, 2Czech Technical University in Prague EMAIL, EMAIL |
| Pseudocode | No | No specific pseudocode or algorithm blocks are provided in the paper. Algorithms are described in prose, such as the one in Theorem 9's proof sketch: 'The main idea of the algorithm is to root each tree in the forest in some arbitrary vertex and then process the agents in a BFS order.' |
| Open Source Code | No | The paper does not contain any statement about making source code available, nor does it provide links to any code repositories. |
| Open Datasets | No | The paper is theoretical, focusing on computational complexity and algorithm design. It does not utilize any specific datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, therefore, no dataset splits are discussed. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or hardware used for computations. |
| Software Dependencies | No | The paper is theoretical and focuses on computational complexity and algorithm design, thus no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical, focusing on mathematical proofs, computational complexity, and algorithm design. It does not describe any experimental setup, hyperparameters, or training configurations. |