Learning and innovative elements of strategy adoption rules expand cooperative network topologies

• Published in: PloS one Vol. 3; no. 4; p. e1917
• Main Authors: Wang, Shijun, Szalay, Máté S, Zhang, Changshui, Csermely, Peter
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.04.2008; Public Library of Science (PLoS)
• Subjects: Adoption of innovations; Algorithms; Analysis; Biochemistry/Bioinformatics; Biochemistry/Theory and Simulation; Competitive Behavior; Complex systems; Computer Simulation; Cooperation; Cooperative Behavior; Evolution; Game Theory; Games; Humans; Innovations; Interpersonal Relations; Learning; Low level; Machine learning; Models, Biological; Models, Statistical; Multiagent systems; Network topologies; Neural Networks, Computer; Physics/Condensed Matter; Physics/General Physics; Prisoner's dilemma; Protein folding; Reinforcement; Short term; Social networks; Social structure; Strategies; Strategy; Systems Analysis; Thinking; Topology; Beijing China; United States > US; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0001917

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 Prisoner's 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 re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.