An Evolutionary Game with Memory-Based Reputation in Networked Populations

The networked evolutionary game theory investigates the emergence of cooperative behaviors in the real world possible. Many reputation-based studies have made significant progress. However, reputation is easily forgotten in the real world, and only the reputation of loyal individuals remains. Therefore, in this paper, we focus on a reputation updating mechanism based on forgetting and explore how it influences the emergence and maintenance of cooperative behavior in complex networks. We creatively introduce a behavior threshold that influences the memory of reputation. In the simulation, we show the evolution of cooperation density over time, discuss the influence of behavior threshold on cooperation density, and further show the evolution of cooperators and defectors over time under different thresholds, which helps us understand the formation of cooperation. Results show that the mechanism we propose helps to overcome social dilemmas.