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

TL;DR

This study demonstrates that reinforcement learning and innovation in strategy adoption promote cooperation across various network topologies in multi-agent games, enhancing stability and robustness of cooperative behavior.

Contribution

It reveals that combining long-term learning with stochastic elements in strategy adoption rules broadens the network structures supporting cooperation.

Findings

Reinforcement learning (Q-learning) stabilizes cooperation across network types.

Adding noise to strategy adoption reduces dependence on network topology.

Long-term learning and innovation together expand cooperation conditions.

Abstract

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