Feedback-Driven Differentiation and Stabilization in Multi-Layer Adaptive Agent Systems

Adaptive systems operating in stochastic environments often exhibit unpredictable divergence and instability due to interacting feedback processes. While prior computational models frequently isolate individual mechanisms such as bias, learning, or coupling, fewer studies examine how layered feedback architectures jointly shape long-term dynamics, robustness, and stability. In this work, we propose a minimal computational framework integrating perceptual bias, outcome-driven adaptive updates, memory decay, emotional modulation, social interaction, and a meta-feedback stabilization layer within a unified architecture. A progressive sequence of controlled simulations is conducted to assess how each architectural component contributes to behavioral differentiation and system-level stability under identical initial conditions. Results indicate that static bias and perception alone produce limited divergence, whereas adaptive learning introduces strong path dependence across stochastic trials. Introducing a meta-feedback layer consistently improves average performance and reduces variance, particularly in socially coupled and emotionally modulated regimes where unregulated dynamics frequently degrade or collapse. These findings suggest that stabilization mechanisms play a critical role in maintaining robustness in complex adaptive systems. The proposed framework provides a reproducible platform for studying feedback-driven differentiation and regulation in layered adaptive architectures, with potential relevance to the design of resilient multi-agent and adaptive control systems.