Hedonic Expertise Games

11/03/2020 ∙ by Bugra Caskurlu, et al. ∙ 0

We consider a team formation setting where agents have varying levels of expertise in a global set of required skills, and teams are ranked with respect to how well the expertise of teammates complement each other. We model this setting as a hedonic game, and we show that this class of games possesses many desirable properties, some of which are as follows: A partition that is Nash stable, core stable and Pareto optimal is always guaranteed to exist. A contractually individually stable partition (and a Nash stable partition in a restricted setting) can be found in polynomial-time. A core stable partition can be approximated within a factor of 1 - 1/e, and this bound is tight unless P = NP. We also introduce a larger and relatively general class of games, which we refer to as monotone submodular hedonic games with common ranking property. We show that the above multi-concept existence guarantee also holds for this larger class of games.



There are no comments yet.


page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.