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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
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. |