Emergence of cooperation in reactive strategy space through similarity-based interactions

Considering agents tend to interact preferentially with others adopting similar behaviors, this paper investigates the impact of similarity-based interaction mechanism on learning dynamics and evolution of strategies. We propose a novel evolution model for reactive strategies in which agents interact with similar partners within some threshold, forming a dynamically structural population. By combining exploitation and exploration, we analyze the evolution dynamics of cooperation under the influence of various payoff parameters and similarity thresholds. The results show that similarity-based interactions can efficiently protect cooperative strategies and promote their persistence. Robustness analysis further demonstrates that similarity-driven interactions sustain high cooperation across different population sizes and that moderate random exploration enhances cooperative evolution.