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A Multi-agent Based Migration Model for Evolving Cooperation in the Spatial N-Player Snowdrift Game

机译:基于多智能体的空间N层雪堆漂移合作演化迁移模型

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In recent years, there has been an increased interest in using agent-based simulation models to investigate the evolution of cooperative behaviour in spatial evolutionary games. However, the relationship between individual player mobility (or migration) and population dynamics is not clear. In this paper, we investigate the impacts of alternative migration mechanisms in the spatial N-player Snowdrift game. Here, agents occupy sites in a two-dimensional toroidal lattice. Specific game instances are created by nominating N sites from each of the local neighbourhoods. We use a genetic algorithm to evolve agent game-playing strategies. In addition, agents have an opportunity to migrate to different sites in the lattice at regular intervals. Key parameters in our model include the migration rate, the actual dispersal distance, the "take-over" scheme, the group size N, and the relative cost-to-benefit ratio of the game. Detailed simulation experiments show that the proposed model is able to promote cooperation in a population of mobile agents. However, the magnitude of the dispersal distance plays a significant role in determining population dynamics. Our findings help to further understand how migratory (mobility) patterns affect evolutionary processes.
机译:近年来,人们越来越关注使用基于代理的仿真模型来研究空间演化游戏中合作行为的演化。但是,个人玩家流动(或迁移)与人口动态之间的关系尚不清楚。在本文中,我们研究了空间N玩家Snowdrift游戏中替代迁移机制的影响。在此,代理占据二维环形格子中的位置。通过提名每个本地社区的N个站点来创建特定的游戏实例。我们使用一种遗传算法来发展代理游戏策略。此外,代理商有机会定期迁移到晶格中的不同位置。我们模型中的关键参数包括迁移率,实际分散距离,“接管”方案,小组人数N和游戏的相对成本收益比。详细的仿真实验表明,所提出的模型能够促进移动代理群体中的合作。但是,分散距离的大小在决定种群动态方面起着重要作用。我们的发现有助于进一步了解迁移(流动)模式如何影响进化过程。

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