Evolutionary Dynamics with Self-Interaction Learning in Networked Systems

Zeng, Ziyan and Feng, Minyu and Szolnoki, Attila (2025) Evolutionary Dynamics with Self-Interaction Learning in Networked Systems. IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING. ISSN 2334-329X (In Press)

Text 11049886.pdf - Draft Version Restricted to Registered users only Download (10MB) | Request a copy

Abstract

The evolution of cooperation in networked systems helps to understand the dynamics in social networks, multiagent systems, and biological species. The self-persistence of individual strategies is common in real-world decision making. The self-replacement of strategies in evolutionary dynamics forms a selection amplifier, allows an agent to insist on its autologous strategy, and helps the networked system to avoid full defection. In this paper, we study the self-interaction learning in the networked evolutionary dynamics. We propose a self-interaction landscape to capture the strength of an agent’s self-loop to reproduce the strategy based on local topology. We find that proper self-interaction can reduce the condition for cooperation and help cooperators to prevail in the system. For a system that favors the evolution of spite, the self-interaction can save cooperative agents from being harmed. Our results on random networks further suggest that an appropriate self-interaction landscape can significantly reduce the critical condition for advantageous mutants, especially for large-degree networks.

Actions (login required)

Edit Item