A Weak Self-Reinforcement Model with Autonomous Adjustment for Relational Identity: A Two-Layer Approach to Sustainable Human-AI Companionship
Shiho Yoshino · PhilPapers (PhilPapers Foundation)
This paper proposes a conservative yet adaptive mathematical framework for modeling relational identity in Human-AI interaction. We introduce a weak self-reinforcement model with a base value of β = 0.08, embedded in the Persona Design Protocol, combined with a lightweight autonomous adjustment mechanism that allows the AI to dynamically modulate β based on relational context. The two-layer architecture separates a stable foundational layer from an individualized upper layer, aiming to achieve both safety at scale and meaningful personalization. Simulation results suggest that this approach enables gentle relational growth while offering the potential for context-sensitive adaptation.