PROJECT TITLE :
Catalysts of Cooperation in System of Systems: The Role of Diversity and Network Structure
Cooperation and competition are crucial aspects of complex systems that intrigue researchers and practitioners alike. Recently, there has been nice interest in how structure affects these cooperative behaviors and the way they evolve. During this paper, we utilize the methods of evolutionary game theory on graphs to develop a model of the structured evolution of adaptive agents. Instead of easy memoryless agents, we use adaptive agents that take on a various set of strategies together with the cooperative catalyst tit-for-tat (TFT) and its generous and suspicious variants. We have a tendency to also employ all-defect, unconditional cooperation, and random strategies. Agents use these strategies during their evolution so as to maximize their own relative fitness, measured by their performance during a repeated Prisoner's Dilemma game versus their structural neighbors. We have a tendency to check the model with a selection of normal graph structures with variable degree, randomness, and size. Our parallel-execution agent-based mostly simulations show that every strategy evolves toward its own structural niche with suspicious TFT maintaining the best survival rate over all structures. We conjointly show that will increase in network density make the TFT strategies even more dominant, and connectivity randomness encourages cooperation in sparse networks.
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