Evolutionary Dynamics with Self-Interaction Learning in Networked Systems

2025-07-01

AI Summary

This study investigates how self-persistence—the individual’s capacity to maintain its own strategy amid networked interactions—enhances the robustness of cooperation evolution, particularly under pervasive defection or deleterious mutations. We propose the “self-interaction landscape” model, which formally quantifies an agent’s ability to retain its strategy based on local network structure and integrates this mechanism into evolutionary game dynamics. Theoretical analysis and simulations demonstrate that moderate self-interaction significantly lowers the critical benefit threshold for cooperation emergence, suppresses defection propagation, and improves cooperators’ colonization success in heterogeneous networks. Our key contribution lies in the first formalization of strategy self-sustainability as a tunable, biologically plausible mechanism—revealing its universal catalytic role: it not only stabilizes cooperative equilibria but also systematically reduces the invasion threshold for beneficial cooperative mutants, thereby elevating collective cooperation levels across large-scale networks.

Technology Category

• Game Theory and Economic Paradigms: Cooperative Game Theory
• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Economics, Online Markets and Human Computation: Sustainability of Web economics
• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization

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.

Problem

• Study self-interaction learning in networked evolutionary dynamics
• Explore how self-interaction reduces conditions for cooperation
• Analyze self-interaction's role in preventing full defection

Innovation

• Self-interaction learning in networked evolutionary dynamics
• Self-interaction landscape based on local topology
• Reduces critical condition for cooperation