What is it about?
Cooperation is universal in real societies, and complex networks, such as scale-free networks, describe well-known individuals' interaction structures. In this paper, we realized individuals' cooperation by using reinforcement learning. Exploiting reinforcement learning can change agents' network structures from a regular lattice to a complex network of scale-free structures. Ultimately, our findings can provide evidence to explain the construction of social structures.
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Why is it important?
Classic approaches for evolutionary game models have been approached by exploiting square lattices. However, actual interaction structures in societies can form differently from a regular structure, and the structure can change steadily. Experimental research shows that scale-free networks describe populations' structures. Rather than such experimental approaches, in our study, we demonstrate such constructions of individuals' relationships by using evolutionary game theory incorporating reinforcement learning.
Perspectives
Our collaborational study can propose a new perspective to generate scale-free networks, which is generating scale-free networks by the evolution of homogeneous networks rather than typical ways of network growth and preferential connection. Our results provide new aspects to understanding the network structure, the emergence of cooperation, and the behavior of actors in nature and society. I hope this article shows people how the evolutionary game can work and what it can mean in the future.
Junpyo Park
Kyung Hee University
Read the Original
This page is a summary of: Evolution of cooperation on reinforcement-learning driven-adaptive networks, Chaos An Interdisciplinary Journal of Nonlinear Science, April 2024, American Institute of Physics,
DOI: 10.1063/5.0201968.
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