  Agent-based modeling (ABM) is a powerful paradigm to gain insight into social
phenomena. One area that ABM has rarely been applied is coalition formation.
Traditionally, coalition formation is modeled using cooperative game theory. In
this paper, a heuristic algorithm is developed that can be embedded into an ABM
to allow the agents to find coalition. The resultant coalition structures are
comparable to those found by cooperative game theory solution approaches,
specifically, the core. A heuristic approach is required due to the
computational complexity of finding a cooperative game theory solution which
limits its application to about only a score of agents. The ABM paradigm
provides a platform in which simple rules and interactions between agents can
produce a macro-level effect without the large computational requirements. As
such, it can be an effective means for approximating cooperative game solutions
for large numbers of agents. Our heuristic algorithm combines agent-based
modeling and cooperative game theory to help find agent partitions that are
members of a games' core solution. The accuracy of our heuristic algorithm can
be determined by comparing its outcomes to the actual core solutions. This
comparison achieved by developing an experiment that uses a specific example of
a cooperative game called the glove game. The glove game is a type of exchange
economy game. Finding the traditional cooperative game theory solutions is
computationally intensive for large numbers of players because each possible
partition must be compared to each possible coalition to determine the core
set; hence our experiment only considers games of up to nine players. The
results indicate that our heuristic approach achieves a core solution over 90%
of the time for the games considered in our experiment.
