Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying long-term learning + innovative strategy adoption rules on a variety of random, regular, small-word, scale-free and modular networks in repeated, multi-agent  games. Furthermore, we found that while long-term learning promotes cooperation, innovation makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and innovation , when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. Learning and innovation help to preserve cooperation during network URLanization , and may be key mechanisms promoting the evolution of URLanizing, complex systems.
Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoners Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules , when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the URLanization of real-world networks , and may play a prominent role in the evolution of URLanizing, complex systems.
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long-term learning + innovative strategy adoption rules
the reinforcement learning strategy adoption rule, Q-learning
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networks
network models
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Prisoners Dilemma and Hawk-Dove
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while
using the above model systems other
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promotes cooperation, innovation
strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules
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innovation
random elements in the strategy adoption rules
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Learning and innovation
These results suggest that a balanced duo of learning and innovation may
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network URLanization
the URLanization of real-world networks
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be key mechanisms promoting
play a prominent role in