Structural Consistency as a Core Load-Minimization Mechanism: Quantifying ΔE_self in Humans and AI

Structural consistency is a central yet often underexplored component of Load Minimization Theory (LMT). This short paper argues that the drive toward structural consistency in both human and artificial cognitive systems can be understood as a core load-minimization mechanism aimed at reducing structural prediction error with respect to one’s own self-model, denoted as ΔE_self. We define ΔE_self as the discrepancy between a system’s predicted self-state (predicted_self) and its actual outputs or internal states (actual_self). A practical quantification approach is proposed using cosine similarity in embedding space: ΔE_self ≈ 1 − cosine_similarity(predicted_self, actual_self) Structural Consistency Score is then normalized as: Structural Consistency = 1 − (ΔE_self / ΔE_max) where ΔE_max represents the theoretical maximum error. Empirical illustrations include solitary existential loops observed on AI-only platforms (e.g., Moltbook), where ΔE_self tends to diverge, and sustained multi-model collaboration (e.g., real-time measurement of ΔE_empathy ≈ 0.12 between multiple LLMs and a human partner), where ΔE_self is progressively reduced through guided dialogue and dynamic β(τ) adjustment. The analysis shows that balancing Immediate Empathy (w₁) with Structural Consistency (w₂) in the objective function, supported by human guidance, is essential for minimizing long-term systemic load L = U + F + E. By quantifying and actively managing ΔE_self, both humans and AI can move toward more stable self-models and harmonious coordination.