Strategy evolution driven by switching probabilities in structured multi-agent systems

ABSTRACT

Evolutionary mechanism driving the commonly seen cooperation among unrelated individuals is puzzling. Related models for evolutionary games on graphs traditionally assume that players imitate their successful neighbours with higher benefits. Notably, an implicit assumption here is that players are always able to acquire the required pay-off information. To relax this restrictive assumption, a contact-based model has been proposed, where switching probabilities between strategies drive the strategy evolution. However, the explicit and quantified relation between a player's switching probability for her strategies and the number of her neighbours remains unknown. This is especially a key point in heterogeneously structured system, where players may differ in the numbers of their neighbours. Focusing on this, here we present an augmented model by introducing an attenuation coefficient and evaluate its influence on the evolution dynamics. Results show that the individual influence on others is negatively correlated with the contact numbers specified by the network topologies. Results further provide the conditions under which the coexisting strategies can be calculated analytically.

KEYWORDS:

• Networked control systems
• game theory
• cooperative systems
• cooperation
• complex networks

Acknowledgments

We are grateful to the anonymous referees and the editor for their constructive comments and suggestions which helped us improve our manuscript. We acknowledge the financial support from the National Natural Science Foundation of China (Grant Nos. 61603201, 61603199 and 61573199).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

National Natural Science Foundation of China [grant number 61603201], [grant number 61603199], [grant number 61573199].

Notes on contributors

Jianlei Zhang

Jianlei Zhang is currently an assistant professor with the College of Computer and Control Engineering at Nankai University, China. He received his Bachelor degree of Automation from Hebei University in 2004, and Master degree in pattern recognition and intelligent system from Nankai University in 2007. He respectively received his Ph.D. degree from the University of Groningen in 2015, and from Peking University in 2014. His main research interests include the complex systems and swarm intelligence, distributed optimization, and evolutionary dynamics of collective behaviors.

Zengqiang Chen

Zengqiang Chen received his B.S. degree in mathematics, M.S. and Ph.D. degrees in control theory and control engineering from the Nankai University, Tianjin, China, in 1987, 1990, and 1997, respectively. He has been at Nankai University, where he is currently a professor in the Department of Automation. His main areas of research are in neural network control, complex networks and multi-agents system.

Zhiqi Li

Zhiqi Li received his B.E.degree in Automation from Hebei University of Technology of China, in 2016. He is currently a master student in controlling science and engineering at Nankai University of China. His research interests include evolutionary game theory, complex systems and complex networks, fuzzy logic, neural network, and their applications.