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.

中文翻译：

---

使用基于代理的建模寻找合作博弈的核心成员

基于代理的建模 (ABM) 是深入了解社会现象的强大范式。ABM 很少应用的一个领域是联盟的形成。传统上，联盟的形成是使用合作博弈论建模的。在本文中，开发了一种启发式算法，该算法可以嵌入到 ABM 中，以允许代理找到联盟。由此产生的联盟结构与合作博弈论解决方法发现的联盟结构相当，特别是核心。由于寻找合作博弈论解决方案的计算复杂性，需要采用启发式方法，这将其应用限制在大约只有分数的代理。ABM 范式提供了一个平台，其中简单的规则和代理之间的交互可以在没有大量计算要求的情况下产生宏观层面的效果。像这样，它可以是为大量代理逼近合作博弈解决方案的有效手段。我们的启发式算法结合了基于代理的建模和合作博弈论，以帮助找到属于游戏核心解决方案成员的代理分区。我们的启发式算法的准确性可以通过将其结果与实际核心解决方案进行比较来确定。这种比较是通过开发一个实验来实现的，该实验使用称为手套游戏的合作游戏的特定示例。手套游戏是一种交换经济游戏。对于大量玩家来说，寻找传统的合作博弈论解决方案是计算密集型的，因为必须将每个可能的分区与每个可能的联盟进行比较以确定核心集；因此我们的实验只考虑最多九名玩家的游戏。