Optimality and Nash Stability in Additive Separable Generalized Group Activity Selection Problems
Authors: Vittorio Bilò, Angelo Fanelli, Michele Flammini, Gianpiero Monaco, Luca Moscardelli
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We completely characterize the complexity of computing a social optimum and provide approximation algorithms for the NP-hard cases. We also focus on Nash stable outcomes, for which we give some complexity results and a full picture of the related performance by providing tights bounds on both the price of anarchy and the price of stability. |
| Researcher Affiliation | Academia | Vittorio Bil o1 , Angelo Fanelli2 , Michele Flammini3,4 , Gianpiero Monaco4 and Luca Moscardelli5 1University of Salento, Italy 2CNRS, (UMR-6211), France 3Gran Sasso Science Institute, Italy 4University of L Aquila, Italy 5University of Chieti-Pescara, Italy |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies with datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies with dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental work requiring specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |