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 [1].

Settling the Complexity of Popularity in Additively Separable and Fractional Hedonic Games

Authors: Martin Bullinger, Matan Gilboa

IJCAI 2025 | Venue PDF | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We go further and settle the complexity of the existence problem concerning popularity in additively separable and fractional hedonic games by showing that it is Δp 2-complete in both cases. We are thus the first work that proves a completeness result of popularity for the second level of the polynomial hierarchy.
Researcher Affiliation Academia Martin Bullinger , Matan Gilboa University of Oxford EMAIL, EMAIL
Pseudocode No The paper describes reductions and proofs of complexity, but does not include any structured pseudocode or algorithm blocks.
Open Source Code No The paper refers to a technical report for detailed proofs but does not include any statement about releasing source code or provide a link to a code repository for the methodology described.
Open Datasets No This paper is theoretical in nature, focusing on complexity analysis of hedonic games, and therefore does not use or make available any datasets.
Dataset Splits No This paper is theoretical and does not conduct experiments involving datasets, thus there is no information regarding dataset splits.
Hardware Specification No This paper focuses on theoretical complexity results and does not describe any experiments that would require specific hardware specifications.
Software Dependencies No This paper is theoretical and does not detail any experimental implementation, thus no specific software dependencies with version numbers are provided.
Experiment Setup No This paper is theoretical and does not conduct experiments, therefore no experimental setup details such as hyperparameters or training configurations are provided.