Cooperation on Interdependent Networks by Means of Migration and Stochastic Imitation

Area of Science:

• Network Science
• Evolutionary Game Theory
• Computational Social Science

Background:

• Interdependent networks enhance cooperation by leveraging network reciprocity across layers.
• The impact of agent mobility on cooperation dynamics within interdependent networks remains underexplored.

Purpose of the Study:

• To investigate the role of agent mobility and inter-layer imitation in promoting cooperation within interdependent networks.
• To explore novel mechanisms for fostering cooperation in social dilemmas using agent-based modeling.

Main Methods:

• Developed an interdependent network model where agents play distinct evolutionary games on separate layers.
• Incorporated agent mobility within layers to enhance individual fitness.
• Introduced probabilistic imitation of strategies between neighboring agents on different layers.

Main Results:

• Mobility and stochastic imitation significantly promote cooperation across both layers of the interdependent network.
• Cooperation can emerge and be sustained even in layers where it would be improbable under isolated conditions.
• The interplay between mobility and inter-layer imitation creates novel pathways for cooperation.

Conclusions:

• Agent mobility and inter-layer imitation are crucial factors for enhancing cooperation in interdependent network systems.
• Findings offer a theoretical basis for designing social systems that promote cooperation through network structure and agent behavior.
• This research provides insights into optimizing human decision-making in complex, interconnected environments.