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Coalitions from interactions and adaptations of cognitive map agents are evolved using an algorithm. A population of agents are seeded with cognitive map variants characterizing different cultures or different affiliations. The algorithm evolves this population by modifying the cognitive maps using a modified Particle Swarm Optimization algorithm. The modifications include modification to weights of the cognitive map, and the structure of the cognitive map of the global best (gbest) in the neighborhood is imitated according to a weighted random selection, based on the commonality of the node characteristic in the neighborhood. The end results indicate whether a coalition is possible and what cognitive maps emerge. These results are visualized on a 2D grid and measured with a clustering metric.