Evolutionary Dynamics with Self-Interaction Learning in Networked Systems

TL;DR

This paper investigates how self-interaction learning influences the evolution of cooperation in networked systems, showing that proper self-interaction can promote cooperation and protect cooperators in various network topologies.

Contribution

It introduces a self-interaction landscape model to analyze the impact of self-loop strength on strategy evolution in networked systems, revealing new mechanisms for cooperation promotion.

Findings

Proper self-interaction reduces the threshold for cooperation.

Self-interaction helps cooperators survive in spite-favoring systems.

It significantly lowers the critical condition for advantageous mutants in large networks.

Abstract

The evolution of cooperation in networked systems helps to understand the dynamics in social networks, multi-agent 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.