Coevolution of relationship-driven cooperation under recommendation protocol on multiplex networks
Hongyu Yue, Xiaojin Xiong, Minyu Feng, Attila Szolnoki

TL;DR
This paper presents a coevolutionary multiplex network model where relationship strength and strategy choices influence each other, demonstrating that relationship quality and recommendation protocols can promote cooperation in social dilemmas.
Contribution
It introduces a novel multiplex network model incorporating relationship thresholds and recommendation mechanisms to study cooperation dynamics.
Findings
Higher average degree supports cooperation.
Increased randomness inhibits cooperation.
Recommendation protocols improve global cooperation.
Abstract
While traditional game models often simplify interactions among agents as static, real-world social relationships are inherently dynamic, influenced by both immediate payoffs and alternative information. Motivated by this fact, we introduce a coevolutionary multiplex network model that incorporates the concepts of a relationship threshold and a recommendation mechanism to explore how the strength of relationships among agents interacts with their strategy choices within the framework of weak prisoner's dilemma games. In the relationship layer, the relationship strength between agents varies based on interaction outcomes. In return, the strategy choice of agents in the game layer is influenced by both payoffs and relationship indices, and agents can interact with distant agents through a recommendation mechanism. Simulation of various network topologies reveals that a higher average…
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Taxonomy
TopicsOpinion Dynamics and Social Influence · Complex Network Analysis Techniques · Opportunistic and Delay-Tolerant Networks
