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

Authors

Jiancheng Zhao, Jing Zhang, Zhihai Rong, Xiuli Du, Wen Yang

Published in

Chaos (Woodbury, N.Y.). Volume 36. Issue 10. Oct 01, 2026.

Abstract

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.

PMID:
42886046
Bibliographic data and abstract were imported from PubMed on 16 Oct 2026.

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