{"ID":2899465,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.00422","arxiv_id":"2507.00422","title":"Evolutionary Dynamics with Self-Interaction Learning in Networked Systems","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.","short_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 amplifi...","url_abs":"https://arxiv.org/abs/2507.00422","url_pdf":"https://arxiv.org/pdf/2507.00422v1","authors":"[\"Ziyan Zeng\",\"Minyu Feng\",\"Attila Szolnoki\"]","published":"2025-07-01T04:42:17Z","proceeding":"cs.SI","tasks":"[\"cs.SI\",\"physics.soc-ph\"]","methods":"[]","has_code":false}