Loyalty in Cardinal Hedonic Games
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 66-72.
https://doi.org/10.24963/ijcai.2021/10
Keywords
- Agent-based and Multi-agent Systems: Cooperative Games
- Agent-based and Multi-agent Systems: Computational Social Choice
Abstract
A common theme of decision making in multi-agent systems is to assign utilities to alternatives, which individuals seek to maximize. This rationale is questionable in coalition formation where agents are affected by other members of their coalition. Based on the assumption that agents are benevolent towards other agents they like to form coalitions with, we propose loyalty in hedonic games, a binary relation dependent on agents' utilities. Given a hedonic game, we define a loyal variant where agents' utilities are defined by taking the minimum of their utility and the utilities of agents towards which they are loyal. This process can be iterated to obtain various degrees of loyalty, terminating in a locally egalitarian variant of the original game.
We investigate axioms of group stability and efficiency for different degrees of loyalty. Specifically, we consider the problem of finding coalition structures in the core and of computing best coalitions, obtaining both positive and intractability results. In particular, the limit game possesses Pareto optimal coalition structures in the core.
@inproceedings{ijcai2021p10,
title = {Loyalty in Cardinal Hedonic Games},
author = {Bullinger, Martin and Kober, Stefan},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Prof. Zhi-Hua Zhou},
pages = {66--72},
year = {2021},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2021/10},
url = {https://doi.org/10.24963/ijcai.2021/10},
}
TY - CPAPER TI - Loyalty in Cardinal Hedonic Games AU - Bullinger, Martin AU - Kober, Stefan T2 - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2021 SP - 66 EP - 72 DO - 10.24963/ijcai.2021/10 UR - https://doi.org/10.24963/ijcai.2021/10 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: