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

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...

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Bibliographic Details
Published in:PloS one 2008-04, Vol.3 (4), p.e1917-e1917
Main Authors: Wang, Shijun, Szalay, Máté S, Zhang, Changshui, Csermely, Peter
Format: Article
Language:English
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
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0001917