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..
Price of Pareto Optimality in Hedonic Games
Authors: Edith Elkind, Angelo Fanelli, Michele Flammini
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we argue that Pareto optimality can be seen as a notion of stability, and introduce the concept of Price of Pareto Optimality: this is an analogue of the Price of Anarchy, where the maximum is computed over the class of Pareto optimal outcomes, i.e., outcomes that do not permit a deviation by the grand coalition that makes all players weakly better off and some players strictly better off. As a case study, we focus on hedonic games, and provide lower and upper bounds of the Price of Pareto Optimality in three classes of hedonic games: additively separable hedonic games, fractional hedonic games, and modified fractional hedonic games; for fractional hedonic games on trees our bounds are tight. |
| Researcher Affiliation | Academia | Edith Elkind Oxford University, UK. EMAIL Angelo Fanelli CNRS (UMR-6211), France. EMAIL Michele Flammini DISIM University of L Aquila & Gran Sasso Science Institute, Italy. michele.flammini@univaq.it |
| Pseudocode | No | The paper is theoretical and focuses on mathematical proofs and definitions; it does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper and does not involve empirical studies with datasets, therefore no training dataset information is provided. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical studies with datasets, therefore no validation dataset split information is provided. |
| Hardware Specification | No | This is a theoretical paper and does not describe any experimental setup that would require hardware specifications. |
| Software Dependencies | No | This is a theoretical paper and does not involve empirical work that would list software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not involve empirical experiments requiring details such as hyperparameters or training settings. |