Authors:
Ramoni O. Lasisi
and
Vicki H. Allan
Affiliation:
Utah State University, United States
Keyword(s):
Agents, Weighted Voting Games, Power Indices, Manipulation, Merging.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Cooperation and Coordination
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Software Engineering
;
Symbolic Systems
Abstract:
This paper considers weighted voting games and a method of manipulating those games, called merging. This manipulation involves a coordinated action among some agents who come together to form a bloc by merging their weights in order to have more power over the outcomes of the games. We conduct careful experimental investigations to evaluate the opportunities for beneficial merging available for strategic agents using two prominents power indices: Shapley-Shubik and Banzhaf indices. Previous work has shown that finding a beneficial merge is NP-hard for both the Shapley-Shubik and Banzhaf indices, and leaves us with the impression that this is indeed so in practice. However, results from our experiments suggest that finding beneficial merge is relatively easy in practice. Furthermore, while it appears that we may be powerless to stop manipulation by merging for a given game, we suggest a measure, termed quota ratio, that the game designer may be able to control. Thus, we deduce that i
f we know that the quota ratio of a game is high, we would feel more comfortable about the honesty of the game as the percentage of beneficial merges reduces. Finally, we conclude that the Banzhaf index may be more desirable to avoid manipulation by merging, especially for high values of quota ratios.
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