Suchergebnisse

Cluster behavior is prevalent in nature. Many individuals change their behavior to adapt to a dynamically changing environment by following simple rules of behavior and interacting with information from neighboring individuals. In this study, the traditional public goods game model is improved by combining the advantages of game theory and interactive learning. A strategy adaptive evolution method based on a public goods game is proposed. The emergence of cooperative behavior in weighted networks under the co-evolution of game strategies and node weights is explored in conjunction with multi-agent interactive learning. The results show that in a public goods game with strategic adaptation, a person's influence becomes greater if their level of adaptation exceeds the desired level, and less otherwise. This weight adjustment is defined by the intensity parameter δ. A moderate δ value can effectively facilitate the occurrence of cooperative evolution. The level of cooperation depends mainly on the weight distribution of participants, which leads to the formation of cooperative clusters controlled by high-weighted cooperators. Even with the great temptation to defect, these cooperators can prevail over defectors. The adjustment of node weights increases the heterogeneity of individuals. This research provides a viable pathway to solve social dilemmas and will further promote the application of multi-agent intelligent decision making.