  Data analytics allows an analyst to gain insight into underlying populations
through the use of various computational approaches, including Monte Carlo
methods. This paper discusses an approach to apply Monte Carlo methods to
hedonic games. Hedonic games have gain popularity over the last two decades
leading to several research articles that are concerned with the necessary,
sufficient, or both conditions of the existence of a core partition.
Researchers have used analytical methods for this work. We propose that using a
numerical approach will give insights that might not be available through
current analytical methods. In this paper, we describe an approach to
representing hedonic games, with strict preferences, in a matrix form that can
easily be generated; that is, a hedonic game with randomly generated
preferences for each player. Using this generative approach, we were able to
create and solve, i.e., find any core partitions, of millions of hedonic games.
Our Monte Carlo experiment generated games with up to thirteen players. The
results discuss the distribution form of the core size of the games of a given
number of players. We also discuss computational considerations. Our numerical
study of hedonic games gives insight into the underlying properties of hedonic
games.

Theoretical Economics