# Evolutionary Dynamics with Self-Interaction Learning in Networked Systems

arXiv: https://arxiv.org/abs/2507.00422 · HTML: https://mlsanity.com/paper/2507.00422/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2025.

## Built on by (most cited first, 1 of 1)

- [An Evolutionary Computation Framework for Multi-Agent Q-Learning with Mean-Field Environmental Feedback](https://mlsanity.com/paper/2609.13253/) (2026)

## Abstract (arXiv)

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.