The Shapley Value as a Function of the Quota in Weighted Voting Games
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 490-495.
Abstract
In weighted voting games, each agent has a weight, and a coalition of players is deemed to be winning if its weight meets or exceeds the given quota. An agent’s power in such games is usually measured by her Shapley value, which depends both on the agent’s weight and the quota. [Zuckerman et al., 2008] show that one can alter a player’s power sig- nificantly by modifying the quota, and investigate some of the related algorithmic issues. In this pa- per, we answer a number of questions that were left open by [Zuckerman et al., 2008]: we show that, even though deciding whether a quota maximizes or minimizes an agent’s Shapley value is coNP- hard, finding a Shapley value-maximizing quota is easy. Minimizing a player’s power appears to be more difficult. However, we propose and evaluate a heuristic for this problem, which takes into account the voter’s rank and the overall weight distribution. We also explore a number of other algorithmic is- sues related to quota manipulation.
@inproceedings{ijcai2011p89,
title = {The Shapley Value as a Function of the Quota in Weighted Voting Games},
author = {Zick, Yair and Skopalik, Alexander and Elkind, Edith},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {490--495},
year = {2011},
month = {7},
url = {https://ijcai.org/proceedings/2011/89},
}
TY - CPAPER TI - The Shapley Value as a Function of the Quota in Weighted Voting Games AU - Zick, Yair AU - Skopalik, Alexander AU - Elkind, Edith T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 490 EP - 495 UR - https://ijcai.org/proceedings/2011/89 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: