ARTIFICIAL SELF-DOMESTICATION: HOW SOCIAL SE- LECTION SHAPES PROSOCIAL AGENT POPULATIONS

We study Artificial Self-Domestication (ASD): whether autonomous agent populations can reduce socially costly behavior through endogenous social dynamics, without externally imposed norms. We formalize ASD and introduce a Self-Domestication Index (SDI), evaluating it in multi-agent learning populations and LLM-agent societies. We find that ASD does not emerge from random initialization: populations converge to universal predation or, when encounters can be declined, to universal refusal. However, prosocial behavior becomes stable once sufficiently established, revealing ASD as a bistable basin-of-attraction problem. Seeding prosocial and discriminating agents produces a critical-mass transition. Partner choice is necessary, while punishment and social learning progressively lower the critical mass, with social learning additionally converting non-prosocial agents. ASD also requires agents to represent their own social standing; outcome-only learning fails to produce domestication. In LLM societies, prosocial behavior is sustained under reputation but collapses under anonymity, and agents consistently fail to exercise partner refusal even against partners with highly negative histories. These results show that social structure can sustain and propagate prosocial behavior but does not create it from scratch, identifying partner exclusion—not information alone—as a key mechanism making prosociality viable.