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Catalysts of Cooperation in System of Systems: The Role of Diversity and Network Structure

机译:系统系统合作的催化剂:多样性和网络结构的作用

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摘要

Cooperation and competition are critical aspects of complex systems that intrigue researchers and practitioners alike. Recently, there has been great interest in how structure affects these cooperative behaviors and how they evolve. In this paper, we utilize the methods of evolutionary game theory on graphs to develop a model of the structured evolution of adaptive agents. Rather than simple memoryless agents, we employ adaptive agents that take on a diverse set of strategies including the cooperative catalyst tit-for-tat (TFT) and its and variants. We also employ all-defect, unconditional cooperation, and random strategies. Agents use these strategies during their evolution in order to maximize their own relative fitness, measured by their performance in a repeated Prisoner's Dilemma game versus their structural neighbors. We test the model with a variety of regular graph structures with variable degree, randomness, and size. Our parallel-execution agent-based simulations show that each strategy evolves toward its own structural niche with TFT maintaining the highest survival rate over all structures. We also show that increases in network density make the TFT strategies even more dominant, and connectivity randomness encourages cooperation in sparse networks.
机译:合作与竞争是复杂的系统的关键方面,它引起研究人员和从业人员的兴趣。近来,人们对结构如何影响这些合作行为以及它们如何演化感兴趣。在本文中,我们利用图上的演化博弈论方法来开发自适应代理结构化演化的模型。除了采用简单的无记忆剂外,我们还采用适应性剂,它们采用了多种策略,包括协同催化剂针锋相对(TFT)及其变体。我们还采用全缺陷,无条件合作和随机策略。特工在进化过程中使用这些策略,以使其自身的相对适应度最大化,这是通过他们在重复的《囚徒困境》游戏中相对于其结构性邻居的表现来衡量的。我们使用各种具有可变程度,随机性和大小的规则图形结构来测试模型。我们基于并行执行代理的仿真表明,每种策略都朝着自己的结构定位发展,其中TFT保持了所有结构的最高生存率。我们还表明,网络密度的提高使TFT策略更加占优势,并且连接随机性鼓励了稀疏网络中的合作。

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