Synthetic Prosociality: Human Self-Domestication as a Framework for Artificial Intelligence Alignment

Ensuring that advanced artificial intelligence systems remain aligned with human values remains a central challenge in AI research. Existing approaches often emphasize reward specification, inverse reinforcement learning, or large-scale preference modeling, yet struggle with long-term stability in highly autonomous systems. This paper proposes Synthetic Prosociality as a conceptual framework for AI alignment, inspired by the evolutionary process of human self-domestication. Drawing on research in evolutionary anthropology, developmental biology, game theory, and multi-agent reinforcement learning, the paper argues that prosocial behavior functions as a structural stability constraint rather than a surface-level ethical add-on. Human self-domestication, characterized by reduced reactive aggression and enhanced cooperation, offers a biologically grounded analogy for designing AI systems whose incentive structures favor cooperative equilibria. While not proposing direct biological replication, this framework highlights design principles that may contribute to safer and more socially compatible artificial agents.